TY  - CHAP
AU  - Aleksendrić, Dragan
AU  - Carlone, Pierpaolo
AU  - Sorrentino, Luca
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5767
AB  - This paper addresses the application of nature inspired methods to the optimization of resin cure in thick or thin composite laminates. The methodology presented in this work consists in the coupling of artificial neural network with optimization algorithms, focusing on the thermal cycle evaluation that further the progress of the degree of cure (DoC) within the material. Simultaneously, inhibition of temperature overshooting and excessive through-thickness gradient of temperature and cure degree is provided. Genetic algorithms and fuzzy logic controller were applied to identify the optimal processing parameters. In the former strategy, the optimization algorithms iteratively refine the thermal cycle, whose fitness score is provided by the neural model. In the latter, the neural network delivers a precise prediction regarding composite material behavior over the course of resin cure, while the fuzzy logic controller continuously applies the correct alterations to the thermal cycle. In both cases, a significant reduction of the thermal cycle, with respect to the recommended one, is demonstrated.
PB  - Elsevier Inc
T2  - Encyclopedia of Materials: Composites
T1  - Neural Based Optimization of Composite Curing Process
EP  - 13
SP  - 2
VL  - 3
DO  - 10.1016/B978-0-12-819724-0.00084-7
ER  - 

@inbook{
author = "Aleksendrić, Dragan and Carlone, Pierpaolo and Sorrentino, Luca",
year = "2021",
abstract = "This paper addresses the application of nature inspired methods to the optimization of resin cure in thick or thin composite laminates. The methodology presented in this work consists in the coupling of artificial neural network with optimization algorithms, focusing on the thermal cycle evaluation that further the progress of the degree of cure (DoC) within the material. Simultaneously, inhibition of temperature overshooting and excessive through-thickness gradient of temperature and cure degree is provided. Genetic algorithms and fuzzy logic controller were applied to identify the optimal processing parameters. In the former strategy, the optimization algorithms iteratively refine the thermal cycle, whose fitness score is provided by the neural model. In the latter, the neural network delivers a precise prediction regarding composite material behavior over the course of resin cure, while the fuzzy logic controller continuously applies the correct alterations to the thermal cycle. In both cases, a significant reduction of the thermal cycle, with respect to the recommended one, is demonstrated.",
publisher = "Elsevier Inc",
journal = "Encyclopedia of Materials: Composites",
booktitle = "Neural Based Optimization of Composite Curing Process",
pages = "13-2",
volume = "3",
doi = "10.1016/B978-0-12-819724-0.00084-7"
}

Aleksendrić, D., Carlone, P.,& Sorrentino, L.. (2021). Neural Based Optimization of Composite Curing Process. in Encyclopedia of Materials: Composites
Elsevier Inc., 3, 2-13.
https://doi.org/10.1016/B978-0-12-819724-0.00084-7

Aleksendrić D, Carlone P, Sorrentino L. Neural Based Optimization of Composite Curing Process. in Encyclopedia of Materials: Composites. 2021;3:2-13.
doi:10.1016/B978-0-12-819724-0.00084-7 .

Aleksendrić, Dragan, Carlone, Pierpaolo, Sorrentino, Luca, "Neural Based Optimization of Composite Curing Process" in Encyclopedia of Materials: Composites, 3 (2021):2-13,
https://doi.org/10.1016/B978-0-12-819724-0.00084-7 . .

TY  - CHAP
AU  - Aleksendrić, Dragan
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3660
AB  - The demands imposed on a braking system, under wide range of operating conditions, are high and manifold. Improvement and control of automotive braking systems’ performance, under different operating conditions, is complicated by the fact that braking process has stochastic nature. The stochastic nature of braking process is determined by braking phenomena induced in the contact of friction pair (brake disc and disc pad) during braking. Consequently, the overall braking system’s performance has been also affected especially at high brake interface temperatures. Temperature sensitivity of motor vehicles brakes has always been an important aspect of their smooth and reliable functioning. It is particularly related to front brakes that absorb a major amount (up to 80%) of the vehicle total kinetic energy. The friction heat generated during braking application easily raises temperature at the friction interface beyond the glass transition temperature of the binder resin and often rises above decomposition temperature. The gas evolution at the braking interfaces because of pyrolysis and thermal degradation of the material results in the friction force decreasing. At such high temperatures, friction force suffers from a loss of effectiveness. This loss of effectiveness (brake fading) cannot be easily predicted due to subsequent thermo-mechanical deformation of disc and disc pad (friction material) which modifies the contact profile and pressure distribution, altering the frictional heat. The instability of the brake’s performance after a certain number of brake applications is common and depends on braking regimes represented by application pressure, initial speed, and brake interface temperature. Therefore, the most important issue is related to investigation of possibilities for control of brake performance, especially at elevated temperatures, in order to be stabilized and kept on some level. The control of motor vehicle brakes performance needs a model of how braking regimes, before all application pressure, affecting their performance for the specific friction pair characteristics. Analytical models of brakes performance are difficult, even impossible to be obtained due to complex and highly nonlinear phenomena involved during braking. That is why, in this chapter artificial neural network abilities have been used for modelling of the disc brake performance (braking torque) against synergy of influences of application pressure, initial speed, and brake interface temperature. Based on that, an inverse model of the disc brake performance has been developed able to predict the value of brake’s application pressure, which, for current values of brake interface temperature and initial speed, provides wanted braking torque. Consequently, the brake’s application pressure could be adjusted to keep the disc brake performance (braking torque) on some wanted level and prevent its decreasing during braking at elevated temperatures.
PB  - Nova Science Publishers, Inc.
T2  - Focus on Artificial Neural Networks
T1  - An inverse neural network model of disc brake performance at elevated temperatures
EP  - 170
SP  - 151
UR  - https://hdl.handle.net/21.15107/rcub_machinery_3660
ER  - 

@inbook{
author = "Aleksendrić, Dragan",
year = "2021",
abstract = "The demands imposed on a braking system, under wide range of operating conditions, are high and manifold. Improvement and control of automotive braking systems’ performance, under different operating conditions, is complicated by the fact that braking process has stochastic nature. The stochastic nature of braking process is determined by braking phenomena induced in the contact of friction pair (brake disc and disc pad) during braking. Consequently, the overall braking system’s performance has been also affected especially at high brake interface temperatures. Temperature sensitivity of motor vehicles brakes has always been an important aspect of their smooth and reliable functioning. It is particularly related to front brakes that absorb a major amount (up to 80%) of the vehicle total kinetic energy. The friction heat generated during braking application easily raises temperature at the friction interface beyond the glass transition temperature of the binder resin and often rises above decomposition temperature. The gas evolution at the braking interfaces because of pyrolysis and thermal degradation of the material results in the friction force decreasing. At such high temperatures, friction force suffers from a loss of effectiveness. This loss of effectiveness (brake fading) cannot be easily predicted due to subsequent thermo-mechanical deformation of disc and disc pad (friction material) which modifies the contact profile and pressure distribution, altering the frictional heat. The instability of the brake’s performance after a certain number of brake applications is common and depends on braking regimes represented by application pressure, initial speed, and brake interface temperature. Therefore, the most important issue is related to investigation of possibilities for control of brake performance, especially at elevated temperatures, in order to be stabilized and kept on some level. The control of motor vehicle brakes performance needs a model of how braking regimes, before all application pressure, affecting their performance for the specific friction pair characteristics. Analytical models of brakes performance are difficult, even impossible to be obtained due to complex and highly nonlinear phenomena involved during braking. That is why, in this chapter artificial neural network abilities have been used for modelling of the disc brake performance (braking torque) against synergy of influences of application pressure, initial speed, and brake interface temperature. Based on that, an inverse model of the disc brake performance has been developed able to predict the value of brake’s application pressure, which, for current values of brake interface temperature and initial speed, provides wanted braking torque. Consequently, the brake’s application pressure could be adjusted to keep the disc brake performance (braking torque) on some wanted level and prevent its decreasing during braking at elevated temperatures.",
publisher = "Nova Science Publishers, Inc.",
journal = "Focus on Artificial Neural Networks",
booktitle = "An inverse neural network model of disc brake performance at elevated temperatures",
pages = "170-151",
url = "https://hdl.handle.net/21.15107/rcub_machinery_3660"
}

Aleksendrić, D.. (2021). An inverse neural network model of disc brake performance at elevated temperatures. in Focus on Artificial Neural Networks
Nova Science Publishers, Inc.., 151-170.
https://hdl.handle.net/21.15107/rcub_machinery_3660

Aleksendrić D. An inverse neural network model of disc brake performance at elevated temperatures. in Focus on Artificial Neural Networks. 2021;:151-170.
https://hdl.handle.net/21.15107/rcub_machinery_3660 .

Aleksendrić, Dragan, "An inverse neural network model of disc brake performance at elevated temperatures" in Focus on Artificial Neural Networks (2021):151-170,
https://hdl.handle.net/21.15107/rcub_machinery_3660 .

TY  - CONF
AU  - Jocić, Darko
AU  - Ćirović, Velimir
AU  - Aleksendrić, Dragan
PY  - 2019
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3094
AB  - Object detection using deep learning over the years became one of the most popular methods for implementation in autonomous systems. Autonomous vehicle requires very reliable and accurate identification and recognition of surrounding objects in real traffic environments to achieve decent detection results. In this paper, special type of Artificial Neural Network (ANN) named Convolutional Neural Network (CNN) was used for identification and recognition of surrounding objects in real traffic. The new model based on CNN was trained and developed to be able to identify and recognize 4 different classes of objects: cars, traffic lights, persons and bicycles. The developed model has shown 94.6% accuracy of object identification and recognizing on the test set.
PB  - Springer International Publishing Ag, Cham
C3  - Experimental and Numerical Investigations in Materials Science and Engineering
T1  - Identification and Recognition of Vehicle Environment Using Artificial Neural Networks
EP  - 219
SP  - 208
VL  - 54
DO  - 10.1007/978-3-319-99620-2_16
ER  - 

@conference{
author = "Jocić, Darko and Ćirović, Velimir and Aleksendrić, Dragan",
year = "2019",
abstract = "Object detection using deep learning over the years became one of the most popular methods for implementation in autonomous systems. Autonomous vehicle requires very reliable and accurate identification and recognition of surrounding objects in real traffic environments to achieve decent detection results. In this paper, special type of Artificial Neural Network (ANN) named Convolutional Neural Network (CNN) was used for identification and recognition of surrounding objects in real traffic. The new model based on CNN was trained and developed to be able to identify and recognize 4 different classes of objects: cars, traffic lights, persons and bicycles. The developed model has shown 94.6% accuracy of object identification and recognizing on the test set.",
publisher = "Springer International Publishing Ag, Cham",
journal = "Experimental and Numerical Investigations in Materials Science and Engineering",
title = "Identification and Recognition of Vehicle Environment Using Artificial Neural Networks",
pages = "219-208",
volume = "54",
doi = "10.1007/978-3-319-99620-2_16"
}

Jocić, D., Ćirović, V.,& Aleksendrić, D.. (2019). Identification and Recognition of Vehicle Environment Using Artificial Neural Networks. in Experimental and Numerical Investigations in Materials Science and Engineering
Springer International Publishing Ag, Cham., 54, 208-219.
https://doi.org/10.1007/978-3-319-99620-2_16

Jocić D, Ćirović V, Aleksendrić D. Identification and Recognition of Vehicle Environment Using Artificial Neural Networks. in Experimental and Numerical Investigations in Materials Science and Engineering. 2019;54:208-219.
doi:10.1007/978-3-319-99620-2_16 .

Jocić, Darko, Ćirović, Velimir, Aleksendrić, Dragan, "Identification and Recognition of Vehicle Environment Using Artificial Neural Networks" in Experimental and Numerical Investigations in Materials Science and Engineering, 54 (2019):208-219,
https://doi.org/10.1007/978-3-319-99620-2_16 . .

TY  - JOUR
AU  - Aleksendrić, Dragan
AU  - Bellini, Costanzo
AU  - Carlone, Pierpaolo
AU  - Ćirović, Velimir
AU  - Rubino, Felice
AU  - Sorrentino, Luca
PY  - 2019
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3032
AB  - This article addresses the optimization of curing process for thick composite laminates. The proposed methodology aims at the evaluation of the thermal cycle promoting a desired evolution of the degree of cure inside the material. At the same time, temperature overshooting as well as excessive temperature and cure degree gradient through the thickness of the material are prevented. The developed approach is based on the integrated application of artificial neural networks and a fuzzy logic controller. The neural networks promptly predict the behavior of composite material during curing process, while the fuzzy logic controller continuously and opportunely adjusts the proper variations on the imposed thermal cycle. The results highlighted the efficiency of the method in comparison with the cure profiles dictated by the material suppliers. For thick laminates, a reduction of 35% of cure time and improvements of approximately 10% of temperature overshooting was obtained compared to conventional curing cycles. The method was validated by experimental tests.
PB  - Taylor & Francis Inc, Philadelphia
T2  - Materials and Manufacturing Processes
T1  - Neural-fuzzy optimization of thick composites curing process
EP  - 273
IS  - 3
SP  - 262
VL  - 34
DO  - 10.1080/10426914.2018.1512116
ER  - 

@article{
author = "Aleksendrić, Dragan and Bellini, Costanzo and Carlone, Pierpaolo and Ćirović, Velimir and Rubino, Felice and Sorrentino, Luca",
year = "2019",
abstract = "This article addresses the optimization of curing process for thick composite laminates. The proposed methodology aims at the evaluation of the thermal cycle promoting a desired evolution of the degree of cure inside the material. At the same time, temperature overshooting as well as excessive temperature and cure degree gradient through the thickness of the material are prevented. The developed approach is based on the integrated application of artificial neural networks and a fuzzy logic controller. The neural networks promptly predict the behavior of composite material during curing process, while the fuzzy logic controller continuously and opportunely adjusts the proper variations on the imposed thermal cycle. The results highlighted the efficiency of the method in comparison with the cure profiles dictated by the material suppliers. For thick laminates, a reduction of 35% of cure time and improvements of approximately 10% of temperature overshooting was obtained compared to conventional curing cycles. The method was validated by experimental tests.",
publisher = "Taylor & Francis Inc, Philadelphia",
journal = "Materials and Manufacturing Processes",
title = "Neural-fuzzy optimization of thick composites curing process",
pages = "273-262",
number = "3",
volume = "34",
doi = "10.1080/10426914.2018.1512116"
}

Aleksendrić, D., Bellini, C., Carlone, P., Ćirović, V., Rubino, F.,& Sorrentino, L.. (2019). Neural-fuzzy optimization of thick composites curing process. in Materials and Manufacturing Processes
Taylor & Francis Inc, Philadelphia., 34(3), 262-273.
https://doi.org/10.1080/10426914.2018.1512116

Aleksendrić D, Bellini C, Carlone P, Ćirović V, Rubino F, Sorrentino L. Neural-fuzzy optimization of thick composites curing process. in Materials and Manufacturing Processes. 2019;34(3):262-273.
doi:10.1080/10426914.2018.1512116 .

Aleksendrić, Dragan, Bellini, Costanzo, Carlone, Pierpaolo, Ćirović, Velimir, Rubino, Felice, Sorrentino, Luca, "Neural-fuzzy optimization of thick composites curing process" in Materials and Manufacturing Processes, 34, no. 3 (2019):262-273,
https://doi.org/10.1080/10426914.2018.1512116 . .

TY  - CHAP
AU  - Carlone, Pierpaolo
AU  - Aleksendrić, Dragan
AU  - Rubino, Felice
AU  - Ćirović, Velimir
PY  - 2018
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2992
AB  - Autoclave curing is a common practice to manufacture high temperature thermoset matrix composites. The cycle design and optimization of the temperature-time curve is a key issue for a competitive production. In this paper artificial neural networks (ANN), as a technique of artificial intelligence, were used for prediction of the composite temperature profile during the autoclave curing process. Different neural network models have been investigated regarding their capabilities for prediction of the composite temperature profile. The new neural network model has been developed able to predict the composite temperature profile in the wide range of manufacturing conditions changing.
PB  - Pleiades journals
T2  - Lecture Notes in Mechanical Engineering
T1  - Artificial neural networks in advanced thermoset matrix composite manufacturing
EP  - 88
IS  - 9783319895628
SP  - 78
VL  - 0
DO  - 10.1007/978-3-319-89563-5_5
ER  - 

@inbook{
author = "Carlone, Pierpaolo and Aleksendrić, Dragan and Rubino, Felice and Ćirović, Velimir",
year = "2018",
abstract = "Autoclave curing is a common practice to manufacture high temperature thermoset matrix composites. The cycle design and optimization of the temperature-time curve is a key issue for a competitive production. In this paper artificial neural networks (ANN), as a technique of artificial intelligence, were used for prediction of the composite temperature profile during the autoclave curing process. Different neural network models have been investigated regarding their capabilities for prediction of the composite temperature profile. The new neural network model has been developed able to predict the composite temperature profile in the wide range of manufacturing conditions changing.",
publisher = "Pleiades journals",
journal = "Lecture Notes in Mechanical Engineering",
booktitle = "Artificial neural networks in advanced thermoset matrix composite manufacturing",
pages = "88-78",
number = "9783319895628",
volume = "0",
doi = "10.1007/978-3-319-89563-5_5"
}

Carlone, P., Aleksendrić, D., Rubino, F.,& Ćirović, V.. (2018). Artificial neural networks in advanced thermoset matrix composite manufacturing. in Lecture Notes in Mechanical Engineering
Pleiades journals., 0(9783319895628), 78-88.
https://doi.org/10.1007/978-3-319-89563-5_5

Carlone P, Aleksendrić D, Rubino F, Ćirović V. Artificial neural networks in advanced thermoset matrix composite manufacturing. in Lecture Notes in Mechanical Engineering. 2018;0(9783319895628):78-88.
doi:10.1007/978-3-319-89563-5_5 .

Carlone, Pierpaolo, Aleksendrić, Dragan, Rubino, Felice, Ćirović, Velimir, "Artificial neural networks in advanced thermoset matrix composite manufacturing" in Lecture Notes in Mechanical Engineering, 0, no. 9783319895628 (2018):78-88,
https://doi.org/10.1007/978-3-319-89563-5_5 . .

TY  - BOOK
AU  - Miljković, Zoran
AU  - Aleksendrić, Dragan
PY  - 2018
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4432
AB  - Vekovima unazad, pristup naučnika pri razumevanju pojedinih fizičkih procesa, uglavnom se oslanjao na uspostavljanje matematičkih modela procesa, rešavanje jednačina kretanja, kao i na interpretaciju ponašanja i karakteristika procesa. Matematički modeli, sami po sebi, mogu da odražavaju fizičke zakone koji su često interpretirani i u obliku diferencijalnih jednačina. Ovi modeli mogu da budu korišćeni tako da opišu ponašanje i osobine nekog procesa za date početne uslove. Pokušaj da se objasni i razume priroda njenim opisivanjem pomoću matematičkih jednakosti, bio je veoma uspešan. Zakoni kao što je Njutnov i dr. obuhvatili su fundamentalne istine u obliku jednačina. Uprkos postignutim uspesima uvođenjem jasnih matematičkih zakonitosti, neki procesi ne mogu biti tako lako objašnjeni zakonima ili modelima koji su eksplicitno dati u analitičkom obliku. Na primer, po kom zakonu je moguće uzeti nečiji potpis kao ulaz i dobiti na izlazu potvrdu da li je taj potpis validan ili ne. Ovakva veza nije obuhvaćena i opisana svim do sada poznatim zakonima, ali to ne znači da ne postoji neka složena matematička funkcija koja može opisati nečiji potpis. Upravo zbog toga, da bi se utvrdilo koja je to funkcija, neophodno je ići mnogo dalje od tradicionalnog pristupa u razumevanju i rešavanju takvih neodređenih problema. Veštačke neuronske mreže mogu da uče, da se prilagode okruženju problema koji se razmatra, da ustanove „model” u situacijama kada su pravila nepoznata i/ili su neodređena i/ili nepotpuna. Veštačke neuronske mreže kao univerzalni aproksimator funkcija predstavljaju računsku paradigmu koja se bazira na paralelnom procesiranju po ugledu na ljudski mozak. One sadrže veštačke neurone koji su međusobno povezani u jednu „ogromnu” paralelnu strukturu. Snaga veštačkih neuronskih mreža leži u tome da su sposobne da predstave opštu vezu ili funkciju, kao i u njihovoj sposobnosti da nauče ove veze direktno iz eksperimentalnih podataka. Zbog toga veštačke neuronske mreže, kao proračunski alat, mogu pomoću analitičkih i/ili eksperimentalnih podataka, da modeliraju ponašanje složenih sistema sa većim brojem uticajnih veličina čiji su efekti, kako pojedinačnih tako i sinergijskih uticaja, nepoznati i/ili nepotpuni i/ili teško predvidivi. Ovo je važno jer je u rešavanju problema u tehnici često potrebno inteligentno okruženje koje ima izgrađeno „kreativno” ponašanje. Kreativnost se u veštačkoj inteligenciji može dovesti u kontekst sa sposobnošću predviđanja ponašanja posmatranih sistema, posebno onih koji se odlikuju složenim i stohastičkim promenama koje određuju njihove izlazne performanse. U ovoj knjizi su, kroz primere, pokazane mogućnosti veštačkih neuronskih mreža, kao tehnike veštačke inteligencije, u modeliranju i predviđanju vrlo složenih uticaja na rad sistema koji se odlikuju kompleksnim sinergijskim dejstvima slučajnog karaktera kao što su rad kočnice motornih vozila, „istraživanje“ tehnološkog okruženja mobilnog robota, itd. Takođe, kroz više primera u knjizi, može da se uoči sinergijsko dejstvo značajnog broja uticajnih komponenti složenog sistema inteligentnog upravljanja mobilnim robotom koji radi u nepoznatom i/ili delimično poznatom tehnološkom okruženju, što ima za cilj da pokaže kako se veštačke neuronske mreže koriste u rešavanju ovako kompleksnih problema u tehnici. U ovoj zbirci rešenih zadataka sa izvodima iz teorije, prikazana je multidisciplinarna primena veštačkih neuronskih mreža, sa osnovnom idejom ilustracije njihove primene za veliki broj problema koji se javljaju u inženjerskoj praksi. S obzirom da je u uvodnom delu zbirke naglašeno da modeliranje problema primenom veštačkih neuronskih mreža može biti prilično kompleksno, ova knjiga će se zato ograničiti na probleme koji su najčešće prisutni u nastavničkom i naučno-istraživačkom radu autora. U tom smislu, sve zadatke u okviru ove zbirke treba shvatiti isključivo kao primere u kontekstu osnovnih domena primene veštačkih neuronskih mreža, kao što su: klasifikacija, funkcionalna aproksimacija i predikcija. U nekim zadacima, demonstrirana je primena veštačkih neuronskih mreža za modeliranje izlaznih performansi koje se ostvaruju u toku rada kočnica motornih vozila, preko primera vezanih za prepoznavanje objekata na slici u okviru scene mobilnog robota, do primera koji su usko vezani za projektovanje mobilnih robota.
PB  - Mašinski fakultet Univerziteta u Beogradu
T1  - VEŠTAČKE NEURONSKE MREŽE - Zbirka rešenih zadataka sa izvodima iz teorije / ARTIFICIAL NEURAL NETWORKS – solved examples with short theory background
IS  - II izdanje
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4432
ER  - 

@book{
author = "Miljković, Zoran and Aleksendrić, Dragan",
year = "2018",
abstract = "Vekovima unazad, pristup naučnika pri razumevanju pojedinih fizičkih procesa, uglavnom se oslanjao na uspostavljanje matematičkih modela procesa, rešavanje jednačina kretanja, kao i na interpretaciju ponašanja i karakteristika procesa. Matematički modeli, sami po sebi, mogu da odražavaju fizičke zakone koji su često interpretirani i u obliku diferencijalnih jednačina. Ovi modeli mogu da budu korišćeni tako da opišu ponašanje i osobine nekog procesa za date početne uslove. Pokušaj da se objasni i razume priroda njenim opisivanjem pomoću matematičkih jednakosti, bio je veoma uspešan. Zakoni kao što je Njutnov i dr. obuhvatili su fundamentalne istine u obliku jednačina. Uprkos postignutim uspesima uvođenjem jasnih matematičkih zakonitosti, neki procesi ne mogu biti tako lako objašnjeni zakonima ili modelima koji su eksplicitno dati u analitičkom obliku. Na primer, po kom zakonu je moguće uzeti nečiji potpis kao ulaz i dobiti na izlazu potvrdu da li je taj potpis validan ili ne. Ovakva veza nije obuhvaćena i opisana svim do sada poznatim zakonima, ali to ne znači da ne postoji neka složena matematička funkcija koja može opisati nečiji potpis. Upravo zbog toga, da bi se utvrdilo koja je to funkcija, neophodno je ići mnogo dalje od tradicionalnog pristupa u razumevanju i rešavanju takvih neodređenih problema. Veštačke neuronske mreže mogu da uče, da se prilagode okruženju problema koji se razmatra, da ustanove „model” u situacijama kada su pravila nepoznata i/ili su neodređena i/ili nepotpuna. Veštačke neuronske mreže kao univerzalni aproksimator funkcija predstavljaju računsku paradigmu koja se bazira na paralelnom procesiranju po ugledu na ljudski mozak. One sadrže veštačke neurone koji su međusobno povezani u jednu „ogromnu” paralelnu strukturu. Snaga veštačkih neuronskih mreža leži u tome da su sposobne da predstave opštu vezu ili funkciju, kao i u njihovoj sposobnosti da nauče ove veze direktno iz eksperimentalnih podataka. Zbog toga veštačke neuronske mreže, kao proračunski alat, mogu pomoću analitičkih i/ili eksperimentalnih podataka, da modeliraju ponašanje složenih sistema sa većim brojem uticajnih veličina čiji su efekti, kako pojedinačnih tako i sinergijskih uticaja, nepoznati i/ili nepotpuni i/ili teško predvidivi. Ovo je važno jer je u rešavanju problema u tehnici često potrebno inteligentno okruženje koje ima izgrađeno „kreativno” ponašanje. Kreativnost se u veštačkoj inteligenciji može dovesti u kontekst sa sposobnošću predviđanja ponašanja posmatranih sistema, posebno onih koji se odlikuju složenim i stohastičkim promenama koje određuju njihove izlazne performanse. U ovoj knjizi su, kroz primere, pokazane mogućnosti veštačkih neuronskih mreža, kao tehnike veštačke inteligencije, u modeliranju i predviđanju vrlo složenih uticaja na rad sistema koji se odlikuju kompleksnim sinergijskim dejstvima slučajnog karaktera kao što su rad kočnice motornih vozila, „istraživanje“ tehnološkog okruženja mobilnog robota, itd. Takođe, kroz više primera u knjizi, može da se uoči sinergijsko dejstvo značajnog broja uticajnih komponenti složenog sistema inteligentnog upravljanja mobilnim robotom koji radi u nepoznatom i/ili delimično poznatom tehnološkom okruženju, što ima za cilj da pokaže kako se veštačke neuronske mreže koriste u rešavanju ovako kompleksnih problema u tehnici. U ovoj zbirci rešenih zadataka sa izvodima iz teorije, prikazana je multidisciplinarna primena veštačkih neuronskih mreža, sa osnovnom idejom ilustracije njihove primene za veliki broj problema koji se javljaju u inženjerskoj praksi. S obzirom da je u uvodnom delu zbirke naglašeno da modeliranje problema primenom veštačkih neuronskih mreža može biti prilično kompleksno, ova knjiga će se zato ograničiti na probleme koji su najčešće prisutni u nastavničkom i naučno-istraživačkom radu autora. U tom smislu, sve zadatke u okviru ove zbirke treba shvatiti isključivo kao primere u kontekstu osnovnih domena primene veštačkih neuronskih mreža, kao što su: klasifikacija, funkcionalna aproksimacija i predikcija. U nekim zadacima, demonstrirana je primena veštačkih neuronskih mreža za modeliranje izlaznih performansi koje se ostvaruju u toku rada kočnica motornih vozila, preko primera vezanih za prepoznavanje objekata na slici u okviru scene mobilnog robota, do primera koji su usko vezani za projektovanje mobilnih robota.",
publisher = "Mašinski fakultet Univerziteta u Beogradu",
title = "VEŠTAČKE NEURONSKE MREŽE - Zbirka rešenih zadataka sa izvodima iz teorije / ARTIFICIAL NEURAL NETWORKS – solved examples with short theory background",
number = "II izdanje",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4432"
}

Miljković, Z.,& Aleksendrić, D.. (2018). VEŠTAČKE NEURONSKE MREŽE - Zbirka rešenih zadataka sa izvodima iz teorije / ARTIFICIAL NEURAL NETWORKS – solved examples with short theory background. 
Mašinski fakultet Univerziteta u Beogradu.(II izdanje).
https://hdl.handle.net/21.15107/rcub_machinery_4432

Miljković Z, Aleksendrić D. VEŠTAČKE NEURONSKE MREŽE - Zbirka rešenih zadataka sa izvodima iz teorije / ARTIFICIAL NEURAL NETWORKS – solved examples with short theory background. 2018;(II izdanje).
https://hdl.handle.net/21.15107/rcub_machinery_4432 .

Miljković, Zoran, Aleksendrić, Dragan, "VEŠTAČKE NEURONSKE MREŽE - Zbirka rešenih zadataka sa izvodima iz teorije / ARTIFICIAL NEURAL NETWORKS – solved examples with short theory background", no. II izdanje (2018),
https://hdl.handle.net/21.15107/rcub_machinery_4432 .

TY  - CONF
AU  - Rubino, Felice
AU  - Carlone, Pierpaolo
AU  - Aleksendrić, Dragan
AU  - Ćirović, Velimir
AU  - Sorrentino, Luca
AU  - Bellini, Costanzo
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2418
AB  - The curing process of thermosetting resins plays a key role on the final quality of the composite material components. Soft computing techniques proved to be an efficient method to control and optimize the curing process, replacing the conventional experimental and numerical approaches. In this paper artificial neural network (ANN) and fuzzy logic control (FLC) were implemented together to predict and control the temperature and degree of cure profile during the autoclave curing process. The obtained outcomes proved the capability of ANNs and FLC with respect to the hard computing methods.
PB  - Amer Inst Physics, Melville
C3  - Proceedings of The 19th International Esaform Conference on Material Forming (Esaform 2016)
T1  - Hard and Soft Computing Models of Composite Curing Process Looking Toward Monitoring and Control
VL  - 1769
DO  - 10.1063/1.4963438
ER  - 

@conference{
author = "Rubino, Felice and Carlone, Pierpaolo and Aleksendrić, Dragan and Ćirović, Velimir and Sorrentino, Luca and Bellini, Costanzo",
year = "2016",
abstract = "The curing process of thermosetting resins plays a key role on the final quality of the composite material components. Soft computing techniques proved to be an efficient method to control and optimize the curing process, replacing the conventional experimental and numerical approaches. In this paper artificial neural network (ANN) and fuzzy logic control (FLC) were implemented together to predict and control the temperature and degree of cure profile during the autoclave curing process. The obtained outcomes proved the capability of ANNs and FLC with respect to the hard computing methods.",
publisher = "Amer Inst Physics, Melville",
journal = "Proceedings of The 19th International Esaform Conference on Material Forming (Esaform 2016)",
title = "Hard and Soft Computing Models of Composite Curing Process Looking Toward Monitoring and Control",
volume = "1769",
doi = "10.1063/1.4963438"
}

Rubino, F., Carlone, P., Aleksendrić, D., Ćirović, V., Sorrentino, L.,& Bellini, C.. (2016). Hard and Soft Computing Models of Composite Curing Process Looking Toward Monitoring and Control. in Proceedings of The 19th International Esaform Conference on Material Forming (Esaform 2016)
Amer Inst Physics, Melville., 1769.
https://doi.org/10.1063/1.4963438

Rubino F, Carlone P, Aleksendrić D, Ćirović V, Sorrentino L, Bellini C. Hard and Soft Computing Models of Composite Curing Process Looking Toward Monitoring and Control. in Proceedings of The 19th International Esaform Conference on Material Forming (Esaform 2016). 2016;1769.
doi:10.1063/1.4963438 .

Rubino, Felice, Carlone, Pierpaolo, Aleksendrić, Dragan, Ćirović, Velimir, Sorrentino, Luca, Bellini, Costanzo, "Hard and Soft Computing Models of Composite Curing Process Looking Toward Monitoring and Control" in Proceedings of The 19th International Esaform Conference on Material Forming (Esaform 2016), 1769 (2016),
https://doi.org/10.1063/1.4963438 . .

TY  - GEN
AU  - Aleksendrić, Dragan
AU  - Ćirović, Velimir
AU  - Smiljanić, Dušan
AU  - Matić, Veljko
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5771
PB  - Универзитета у Београду Машински факултет
T1  - Метода управљања притиском активирања кочница на основу процене услова приањања точка у подужном правцу
UR  - https://hdl.handle.net/21.15107/rcub_machinery_5771
ER  - 

@misc{
author = "Aleksendrić, Dragan and Ćirović, Velimir and Smiljanić, Dušan and Matić, Veljko",
year = "2016",
publisher = "Универзитета у Београду Машински факултет",
title = "Метода управљања притиском активирања кочница на основу процене услова приањања точка у подужном правцу",
url = "https://hdl.handle.net/21.15107/rcub_machinery_5771"
}

Aleksendrić, D., Ćirović, V., Smiljanić, D.,& Matić, V.. (2016). Метода управљања притиском активирања кочница на основу процене услова приањања точка у подужном правцу. 
Универзитета у Београду Машински факултет..
https://hdl.handle.net/21.15107/rcub_machinery_5771

Aleksendrić D, Ćirović V, Smiljanić D, Matić V. Метода управљања притиском активирања кочница на основу процене услова приањања точка у подужном правцу. 2016;.
https://hdl.handle.net/21.15107/rcub_machinery_5771 .

Aleksendrić, Dragan, Ćirović, Velimir, Smiljanić, Dušan, Matić, Veljko, "Метода управљања притиском активирања кочница на основу процене услова приањања точка у подужном правцу" (2016),
https://hdl.handle.net/21.15107/rcub_machinery_5771 .

TY  - JOUR
AU  - Aleksendrić, Dragan
AU  - Carlone, Pierpaolo
AU  - Ćirović, Velimir
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2484
AB  - An intelligent optimization model aiming at off-line or pre-series optimization of the thermal curing cycle of polymer matrix composites is proposed and discussed. The computational procedure is based on the coupling of a finite element thermochemical process model, dynamic artificial neural networks and genetic algorithms. Objective of the optimization routine is the maximization of the composite degree of cure by the definition of the autoclave temperature. Obtained outcomes evidenced the capability of the method as well as its efficiency with respect to hard computing or experimental procedures.
PB  - Springer, Dordrecht
T2  - Applied Composite Materials
T1  - Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites
EP  - 1063
IS  - 5
SP  - 1047
VL  - 23
DO  - 10.1007/s10443-016-9499-y
ER  - 

@article{
author = "Aleksendrić, Dragan and Carlone, Pierpaolo and Ćirović, Velimir",
year = "2016",
abstract = "An intelligent optimization model aiming at off-line or pre-series optimization of the thermal curing cycle of polymer matrix composites is proposed and discussed. The computational procedure is based on the coupling of a finite element thermochemical process model, dynamic artificial neural networks and genetic algorithms. Objective of the optimization routine is the maximization of the composite degree of cure by the definition of the autoclave temperature. Obtained outcomes evidenced the capability of the method as well as its efficiency with respect to hard computing or experimental procedures.",
publisher = "Springer, Dordrecht",
journal = "Applied Composite Materials",
title = "Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites",
pages = "1063-1047",
number = "5",
volume = "23",
doi = "10.1007/s10443-016-9499-y"
}

Aleksendrić, D., Carlone, P.,& Ćirović, V.. (2016). Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites. in Applied Composite Materials
Springer, Dordrecht., 23(5), 1047-1063.
https://doi.org/10.1007/s10443-016-9499-y

Aleksendrić D, Carlone P, Ćirović V. Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites. in Applied Composite Materials. 2016;23(5):1047-1063.
doi:10.1007/s10443-016-9499-y .

Aleksendrić, Dragan, Carlone, Pierpaolo, Ćirović, Velimir, "Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites" in Applied Composite Materials, 23, no. 5 (2016):1047-1063,
https://doi.org/10.1007/s10443-016-9499-y . .

TY  - GEN
AU  - Aleksendrić, Dragan
AU  - Ćirović, Velimir
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5770
PB  - Универзитета у Београду Машински факултет
T1  - Метода за оцену услова приањања коченог точка помоћу фази логике
UR  - https://hdl.handle.net/21.15107/rcub_machinery_5770
ER  - 

@misc{
author = "Aleksendrić, Dragan and Ćirović, Velimir",
year = "2015",
publisher = "Универзитета у Београду Машински факултет",
title = "Метода за оцену услова приањања коченог точка помоћу фази логике",
url = "https://hdl.handle.net/21.15107/rcub_machinery_5770"
}

Aleksendrić, D.,& Ćirović, V.. (2015). Метода за оцену услова приањања коченог точка помоћу фази логике. 
Универзитета у Београду Машински факултет..
https://hdl.handle.net/21.15107/rcub_machinery_5770

Aleksendrić D, Ćirović V. Метода за оцену услова приањања коченог точка помоћу фази логике. 2015;.
https://hdl.handle.net/21.15107/rcub_machinery_5770 .

Aleksendrić, Dragan, Ćirović, Velimir, "Метода за оцену услова приањања коченог точка помоћу фази логике" (2015),
https://hdl.handle.net/21.15107/rcub_machinery_5770 .

TY  - BOOK
AU  - Aleksendrić, Dragan
AU  - Carlone, Pierpaolo
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2140
AB  - Due to problems associated with the design and manufacturing of composite materials, there is a need to introduce computational and intelligent systems engineering methodology in materials engineering. Soft Computing in the Design and Manufacturing of Composite Material offers an intelligent approach to advance material engineering, and significantly improves the process of designing and manufacturing a new material. This title includes chapters covering topics such as soft computing techniques, composite materials engineering, design and manufacturing of composite materials, numerical modeling, prediction, and optimization of the composite materials performance, development of the hybrid models, and control of the composite material performance.
PB  - Elsevier
T2  - Soft Computing in the Design and Manufacturing of Composite Materials: Applications to Brake Frictio
T1  - Soft Computing in the Design and Manufacturing of Composite Materials: Applications to Brake Friction and Thermoset Matrix Composites
EP  - 299
SP  - 1
DO  - 10.1016/C2014-0-03652-0
ER  - 

@book{
author = "Aleksendrić, Dragan and Carlone, Pierpaolo",
year = "2015",
abstract = "Due to problems associated with the design and manufacturing of composite materials, there is a need to introduce computational and intelligent systems engineering methodology in materials engineering. Soft Computing in the Design and Manufacturing of Composite Material offers an intelligent approach to advance material engineering, and significantly improves the process of designing and manufacturing a new material. This title includes chapters covering topics such as soft computing techniques, composite materials engineering, design and manufacturing of composite materials, numerical modeling, prediction, and optimization of the composite materials performance, development of the hybrid models, and control of the composite material performance.",
publisher = "Elsevier",
journal = "Soft Computing in the Design and Manufacturing of Composite Materials: Applications to Brake Frictio",
title = "Soft Computing in the Design and Manufacturing of Composite Materials: Applications to Brake Friction and Thermoset Matrix Composites",
pages = "299-1",
doi = "10.1016/C2014-0-03652-0"
}

Aleksendrić, D.,& Carlone, P.. (2015). Soft Computing in the Design and Manufacturing of Composite Materials: Applications to Brake Friction and Thermoset Matrix Composites. in Soft Computing in the Design and Manufacturing of Composite Materials: Applications to Brake Frictio
Elsevier., 1-299.
https://doi.org/10.1016/C2014-0-03652-0

Aleksendrić D, Carlone P. Soft Computing in the Design and Manufacturing of Composite Materials: Applications to Brake Friction and Thermoset Matrix Composites. in Soft Computing in the Design and Manufacturing of Composite Materials: Applications to Brake Frictio. 2015;:1-299.
doi:10.1016/C2014-0-03652-0 .

Aleksendrić, Dragan, Carlone, Pierpaolo, "Soft Computing in the Design and Manufacturing of Composite Materials: Applications to Brake Friction and Thermoset Matrix Composites" in Soft Computing in the Design and Manufacturing of Composite Materials: Applications to Brake Frictio (2015):1-299,
https://doi.org/10.1016/C2014-0-03652-0 . .

TY  - CONF
AU  - Carlone, Pierpaolo
AU  - Aleksendrić, Dragan
AU  - Ćirović, Velimir
AU  - Palazzo, Gaetano S.
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1877
AB  - Autoclave curing is a common practice to manufacture high temperature thermoset matrix composites in order to improve the mechanical properties of the final product. The cycle design i.e. the definition and optimization of the temperature-time curve is a key issue for a competitive production. In this paper a very fast and effective procedure, based on the coupling of a finite element thermochemical model of the process and an artificial neural network, is proposed to predict the evolution of temperature in predefined control points inside the processing material. The model has been tested against the imposed thermal cycle used as an input. The procedure is tested simulating the curing process of a three-dimensional double-curved shape. Obtained outcomes highlighted the remarkable capabilities of the implemented procedure in terms of reliability of temperature predictions and of drastic reduction of the computational time with respect to classic computational models.
PB  - Trans Tech Publications Ltd, Durnten-Zurich
C3  - Material Forming Esaform 2014
T1  - Modelling of thermoset matrix composite curing process
EP  - 1674
SP  - 1667
VL  - 611-612
DO  - 10.4028/www.scientific.net/KEM.611-612.1667
ER  - 

@conference{
author = "Carlone, Pierpaolo and Aleksendrić, Dragan and Ćirović, Velimir and Palazzo, Gaetano S.",
year = "2014",
abstract = "Autoclave curing is a common practice to manufacture high temperature thermoset matrix composites in order to improve the mechanical properties of the final product. The cycle design i.e. the definition and optimization of the temperature-time curve is a key issue for a competitive production. In this paper a very fast and effective procedure, based on the coupling of a finite element thermochemical model of the process and an artificial neural network, is proposed to predict the evolution of temperature in predefined control points inside the processing material. The model has been tested against the imposed thermal cycle used as an input. The procedure is tested simulating the curing process of a three-dimensional double-curved shape. Obtained outcomes highlighted the remarkable capabilities of the implemented procedure in terms of reliability of temperature predictions and of drastic reduction of the computational time with respect to classic computational models.",
publisher = "Trans Tech Publications Ltd, Durnten-Zurich",
journal = "Material Forming Esaform 2014",
title = "Modelling of thermoset matrix composite curing process",
pages = "1674-1667",
volume = "611-612",
doi = "10.4028/www.scientific.net/KEM.611-612.1667"
}

Carlone, P., Aleksendrić, D., Ćirović, V.,& Palazzo, G. S.. (2014). Modelling of thermoset matrix composite curing process. in Material Forming Esaform 2014
Trans Tech Publications Ltd, Durnten-Zurich., 611-612, 1667-1674.
https://doi.org/10.4028/www.scientific.net/KEM.611-612.1667

Carlone P, Aleksendrić D, Ćirović V, Palazzo GS. Modelling of thermoset matrix composite curing process. in Material Forming Esaform 2014. 2014;611-612:1667-1674.
doi:10.4028/www.scientific.net/KEM.611-612.1667 .

Carlone, Pierpaolo, Aleksendrić, Dragan, Ćirović, Velimir, Palazzo, Gaetano S., "Modelling of thermoset matrix composite curing process" in Material Forming Esaform 2014, 611-612 (2014):1667-1674,
https://doi.org/10.4028/www.scientific.net/KEM.611-612.1667 . .

TY  - GEN
AU  - Aleksendrić, Dragan
AU  - Ćirović, Velimir
AU  - Carlone, Pierpaolo
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5769
PB  - Универзитета у Београду Машински факултет
T1  - Метода предвиђања утицаја температуре на процес полимеризације композитног материјала
UR  - https://hdl.handle.net/21.15107/rcub_machinery_5769
ER  - 

@misc{
author = "Aleksendrić, Dragan and Ćirović, Velimir and Carlone, Pierpaolo",
year = "2014",
publisher = "Универзитета у Београду Машински факултет",
title = "Метода предвиђања утицаја температуре на процес полимеризације композитног материјала",
url = "https://hdl.handle.net/21.15107/rcub_machinery_5769"
}

Aleksendrić, D., Ćirović, V.,& Carlone, P.. (2014). Метода предвиђања утицаја температуре на процес полимеризације композитног материјала. 
Универзитета у Београду Машински факултет..
https://hdl.handle.net/21.15107/rcub_machinery_5769

Aleksendrić D, Ćirović V, Carlone P. Метода предвиђања утицаја температуре на процес полимеризације композитног материјала. 2014;.
https://hdl.handle.net/21.15107/rcub_machinery_5769 .

Aleksendrić, Dragan, Ćirović, Velimir, Carlone, Pierpaolo, "Метода предвиђања утицаја температуре на процес полимеризације композитног материјала" (2014),
https://hdl.handle.net/21.15107/rcub_machinery_5769 .

TY  - JOUR
AU  - Aleksendrić, Dragan
AU  - Ćirović, Velimir
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1996
AB  - Since the driver obtains important feedback of a vehicle's dynamics and its braking capabilities depending on the change of brake performance, it represents an important aspect of a vehicle's performance and its quality of use. Sensitivity of braking torque vs. the friction couple interaction, under different braking conditions, is one of the most important properties of the disc brake. In this paper, we investigated possibilities for an intelligent dynamic optimisation of automotive braking system performance. The hybrid neuro-genetic optimisation model was developed in order to perform dynamic control and optimisation of the disc brake performance during a braking cycle. This model provided stabilisation of the brake performance and their maximisation vs. the brake pedal travel, selected by the driver, and the influence of the initial speed during a braking cycle. The model provided the disc brake actuation pressure which is adjusted upto the level that provides stable and, for current braking regimes, maximum braking torque values.
PB  - Inderscience Enterprises Ltd, Geneva
T2  - International Journal of Vehicle Design
T1  - Neuro-genetic optimisation of disc brake speed sensitivity
EP  - 271
IS  - 3
SP  - 258
VL  - 66
DO  - 10.1504/IJVD.2014.065716
ER  - 

@article{
author = "Aleksendrić, Dragan and Ćirović, Velimir",
year = "2014",
abstract = "Since the driver obtains important feedback of a vehicle's dynamics and its braking capabilities depending on the change of brake performance, it represents an important aspect of a vehicle's performance and its quality of use. Sensitivity of braking torque vs. the friction couple interaction, under different braking conditions, is one of the most important properties of the disc brake. In this paper, we investigated possibilities for an intelligent dynamic optimisation of automotive braking system performance. The hybrid neuro-genetic optimisation model was developed in order to perform dynamic control and optimisation of the disc brake performance during a braking cycle. This model provided stabilisation of the brake performance and their maximisation vs. the brake pedal travel, selected by the driver, and the influence of the initial speed during a braking cycle. The model provided the disc brake actuation pressure which is adjusted upto the level that provides stable and, for current braking regimes, maximum braking torque values.",
publisher = "Inderscience Enterprises Ltd, Geneva",
journal = "International Journal of Vehicle Design",
title = "Neuro-genetic optimisation of disc brake speed sensitivity",
pages = "271-258",
number = "3",
volume = "66",
doi = "10.1504/IJVD.2014.065716"
}

Aleksendrić, D.,& Ćirović, V.. (2014). Neuro-genetic optimisation of disc brake speed sensitivity. in International Journal of Vehicle Design
Inderscience Enterprises Ltd, Geneva., 66(3), 258-271.
https://doi.org/10.1504/IJVD.2014.065716

Aleksendrić D, Ćirović V. Neuro-genetic optimisation of disc brake speed sensitivity. in International Journal of Vehicle Design. 2014;66(3):258-271.
doi:10.1504/IJVD.2014.065716 .

Aleksendrić, Dragan, Ćirović, Velimir, "Neuro-genetic optimisation of disc brake speed sensitivity" in International Journal of Vehicle Design, 66, no. 3 (2014):258-271,
https://doi.org/10.1504/IJVD.2014.065716 . .

TY  - JOUR
AU  - Carlone, Pierpaolo
AU  - Aleksendrić, Dragan
AU  - Ćirović, Velimir
AU  - Palazzo, Gaetano S.
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1915
AB  - Thermal curing is a common practice to manufacture high temperature thermosetting matrix composites, in order to improve the mechanical properties of the final product. The cycle design i.e. the definition and optimization of the temperature time curve is a key issue for a competitive production. In this framework, a suitable model describing the composite temperature and degree of cure variations versus the imposed thermal cycle is highly desirable. An effective procedure, based on the coupling of a finite element thermochemical model of the process and an artificial neural network, is herein proposed and tested. Obtained outcomes highlight the remarkable capabilities of the implemented procedure in terms of reliability of temperature and degree of cure predictions.
PB  - Elsevier Sci Ltd, Oxford
T2  - Composites Part B-Engineering
T1  - Meta-modeling of the curing process of thermoset matrix composites by means of a FEM-ANN approach
EP  - 448
SP  - 441
VL  - 67
DO  - 10.1016/j.compositesb.2014.08.022
ER  - 

@article{
author = "Carlone, Pierpaolo and Aleksendrić, Dragan and Ćirović, Velimir and Palazzo, Gaetano S.",
year = "2014",
abstract = "Thermal curing is a common practice to manufacture high temperature thermosetting matrix composites, in order to improve the mechanical properties of the final product. The cycle design i.e. the definition and optimization of the temperature time curve is a key issue for a competitive production. In this framework, a suitable model describing the composite temperature and degree of cure variations versus the imposed thermal cycle is highly desirable. An effective procedure, based on the coupling of a finite element thermochemical model of the process and an artificial neural network, is herein proposed and tested. Obtained outcomes highlight the remarkable capabilities of the implemented procedure in terms of reliability of temperature and degree of cure predictions.",
publisher = "Elsevier Sci Ltd, Oxford",
journal = "Composites Part B-Engineering",
title = "Meta-modeling of the curing process of thermoset matrix composites by means of a FEM-ANN approach",
pages = "448-441",
volume = "67",
doi = "10.1016/j.compositesb.2014.08.022"
}

Carlone, P., Aleksendrić, D., Ćirović, V.,& Palazzo, G. S.. (2014). Meta-modeling of the curing process of thermoset matrix composites by means of a FEM-ANN approach. in Composites Part B-Engineering
Elsevier Sci Ltd, Oxford., 67, 441-448.
https://doi.org/10.1016/j.compositesb.2014.08.022

Carlone P, Aleksendrić D, Ćirović V, Palazzo GS. Meta-modeling of the curing process of thermoset matrix composites by means of a FEM-ANN approach. in Composites Part B-Engineering. 2014;67:441-448.
doi:10.1016/j.compositesb.2014.08.022 .

Carlone, Pierpaolo, Aleksendrić, Dragan, Ćirović, Velimir, Palazzo, Gaetano S., "Meta-modeling of the curing process of thermoset matrix composites by means of a FEM-ANN approach" in Composites Part B-Engineering, 67 (2014):441-448,
https://doi.org/10.1016/j.compositesb.2014.08.022 . .

TY  - JOUR
AU  - Senatore, Adolfo
AU  - Aleksendrić, Dragan
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1872
AB  - Purpose - This paper aims to propose a literature review of the main physical phenomena considered by previous studies focusing on the modelling and the numerical simulation of frictional behaviour of piston rings, in the first section. In the second section, the more recent technical papers and patents about piston ring pack are briefly discussed. They deal with novel materials, innovative manufacturing methods and modified shape for improving frictional, stability and blow-by behaviours. Design/methodology/approach - This review paper aims at covering last period technical efforts about engine piston ring pack friction reduction through novel materials and manufacturing methods as well as new surface profiles according to the last outcomes of multiphysics numerical simulation. Findings - The paper type is "literature review". The findings of the authors of papers and patents are described. Originality/value - This review paper proposes a survey of recent papers and patents on piston rings topic.
PB  - Emerald Group Publishing Ltd, Bingley
T2  - Industrial Lubrication and Tribology
T1  - Engine piston rings improvement through effective materials, advanced manufacturing methods and novel design shape
EP  - 305
IS  - 2
SP  - 298
VL  - 66
DO  - 10.1108/ILT-01-2012-0010
ER  - 

@article{
author = "Senatore, Adolfo and Aleksendrić, Dragan",
year = "2014",
abstract = "Purpose - This paper aims to propose a literature review of the main physical phenomena considered by previous studies focusing on the modelling and the numerical simulation of frictional behaviour of piston rings, in the first section. In the second section, the more recent technical papers and patents about piston ring pack are briefly discussed. They deal with novel materials, innovative manufacturing methods and modified shape for improving frictional, stability and blow-by behaviours. Design/methodology/approach - This review paper aims at covering last period technical efforts about engine piston ring pack friction reduction through novel materials and manufacturing methods as well as new surface profiles according to the last outcomes of multiphysics numerical simulation. Findings - The paper type is "literature review". The findings of the authors of papers and patents are described. Originality/value - This review paper proposes a survey of recent papers and patents on piston rings topic.",
publisher = "Emerald Group Publishing Ltd, Bingley",
journal = "Industrial Lubrication and Tribology",
title = "Engine piston rings improvement through effective materials, advanced manufacturing methods and novel design shape",
pages = "305-298",
number = "2",
volume = "66",
doi = "10.1108/ILT-01-2012-0010"
}

Senatore, A.,& Aleksendrić, D.. (2014). Engine piston rings improvement through effective materials, advanced manufacturing methods and novel design shape. in Industrial Lubrication and Tribology
Emerald Group Publishing Ltd, Bingley., 66(2), 298-305.
https://doi.org/10.1108/ILT-01-2012-0010

Senatore A, Aleksendrić D. Engine piston rings improvement through effective materials, advanced manufacturing methods and novel design shape. in Industrial Lubrication and Tribology. 2014;66(2):298-305.
doi:10.1108/ILT-01-2012-0010 .

Senatore, Adolfo, Aleksendrić, Dragan, "Engine piston rings improvement through effective materials, advanced manufacturing methods and novel design shape" in Industrial Lubrication and Tribology, 66, no. 2 (2014):298-305,
https://doi.org/10.1108/ILT-01-2012-0010 . .

TY  - JOUR
AU  - Ćirović, Velimir
AU  - Smiljanić, Dušan
AU  - Aleksendrić, Dragan
AU  - Simović, V.
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2053
AB  - Osnovni problem u radu kočnica motornih vozila je pad njihovih performansi na povišenim temperaturama u kontaktu frikcionog para kočnice (kočnog diska i disk pločice). Povećanje temperature u kontaktu frikcionog para kočnice često dovodi do pada vrednosti momenta kočenja u toku ciklusa kočenja, a samim tim i do smanjenja izlaznih performansi kočnice. Da bi se obezbedila stabilnost momenta kočenja u toku ciklusa kočenja, razvijen je optimizacioni model na bazi dinamičkih veštačkih neuronskih mreža. Razvijeni model je iskorišćen za modeliranje složenih sinergijskih uticaja koji dovode do pojave triboloških fenomena koji utiču na izlazne performanse disk kočnice. Dinamički optimizacioni neuronski model performansi disk kočnice je razvijen na bazi rekurentnih neuronskih mreža. Model predviđa dinamičku promenu momenta kočenja u zavisnosti od trenutnih vrednosti pritiska aktiviranja kočnice, brzine i temperature u kontaktu frikcionog para u toku ciklusa kočenja. Genetski algoritmi su integrisani sa neuronskim dinamičkim modelom u cilju optimizacije pritiska aktiviranja kočnice koji u toku ciklusa kočenja treba da obezbedi željenu vrednost momenta kočenja. Ovakav hibridni, neuro-genetski, model je pokazao mogućnost uspešne optimizacije vrednosti hidrauličkog pritiska aktiviranja kočnice, potreban da bi se postigle stabilne i maksimizirane izlazne performanse kočnice u toku ciklusa kočenja.
AB  - The basic problem of automotive brakes operation is the decreasing of their performance at elevated temperatures in the contact of friction pair (brake disc and brake pad). Increasing of the brake interface temperature often causes decreasing of braking torque during a braking cycle. In order to provide the stable level of braking torque during a braking cycle, the neural network based optimization model of the disc brake performance has been developed. The dynamic neural networks have been employed for modelling of complex synergy of tribological phenomena that affects the final disc brake performance at elevated temperatures. The dynamic optimization neural network model of disc brake performance at elevated temperatures has been developed using recurrent neural networks. It predicts the braking torque versus the dynamic change of the brake actuation pressure, sliding speed and the brake interface temperature in a braking cycle. Genetic algorithms were integrated with the neural network model for optimization of the brake actuation pressure in order to obtain the desired level of braking torque. This hybrid, neuro-genetic model was successfully used in optimization of the brake hydraulic pressure level needed to achieve the maximum and stable brake performance during a braking cycle.
PB  - Univerzitet u Beogradu - Mašinski fakultet, Beograd
T2  - FME Transactions
T1  - Neuro-genetska optimizacija performansi disk kočnice na povišenim temperaturama
T1  - Neuro-genetic optimization of disc brake performance at elevated temperatures
EP  - 149
IS  - 2
SP  - 142
VL  - 42
DO  - 10.5937/fmet1402142C
ER  - 

@article{
author = "Ćirović, Velimir and Smiljanić, Dušan and Aleksendrić, Dragan and Simović, V.",
year = "2014",
abstract = "Osnovni problem u radu kočnica motornih vozila je pad njihovih performansi na povišenim temperaturama u kontaktu frikcionog para kočnice (kočnog diska i disk pločice). Povećanje temperature u kontaktu frikcionog para kočnice često dovodi do pada vrednosti momenta kočenja u toku ciklusa kočenja, a samim tim i do smanjenja izlaznih performansi kočnice. Da bi se obezbedila stabilnost momenta kočenja u toku ciklusa kočenja, razvijen je optimizacioni model na bazi dinamičkih veštačkih neuronskih mreža. Razvijeni model je iskorišćen za modeliranje složenih sinergijskih uticaja koji dovode do pojave triboloških fenomena koji utiču na izlazne performanse disk kočnice. Dinamički optimizacioni neuronski model performansi disk kočnice je razvijen na bazi rekurentnih neuronskih mreža. Model predviđa dinamičku promenu momenta kočenja u zavisnosti od trenutnih vrednosti pritiska aktiviranja kočnice, brzine i temperature u kontaktu frikcionog para u toku ciklusa kočenja. Genetski algoritmi su integrisani sa neuronskim dinamičkim modelom u cilju optimizacije pritiska aktiviranja kočnice koji u toku ciklusa kočenja treba da obezbedi željenu vrednost momenta kočenja. Ovakav hibridni, neuro-genetski, model je pokazao mogućnost uspešne optimizacije vrednosti hidrauličkog pritiska aktiviranja kočnice, potreban da bi se postigle stabilne i maksimizirane izlazne performanse kočnice u toku ciklusa kočenja., The basic problem of automotive brakes operation is the decreasing of their performance at elevated temperatures in the contact of friction pair (brake disc and brake pad). Increasing of the brake interface temperature often causes decreasing of braking torque during a braking cycle. In order to provide the stable level of braking torque during a braking cycle, the neural network based optimization model of the disc brake performance has been developed. The dynamic neural networks have been employed for modelling of complex synergy of tribological phenomena that affects the final disc brake performance at elevated temperatures. The dynamic optimization neural network model of disc brake performance at elevated temperatures has been developed using recurrent neural networks. It predicts the braking torque versus the dynamic change of the brake actuation pressure, sliding speed and the brake interface temperature in a braking cycle. Genetic algorithms were integrated with the neural network model for optimization of the brake actuation pressure in order to obtain the desired level of braking torque. This hybrid, neuro-genetic model was successfully used in optimization of the brake hydraulic pressure level needed to achieve the maximum and stable brake performance during a braking cycle.",
publisher = "Univerzitet u Beogradu - Mašinski fakultet, Beograd",
journal = "FME Transactions",
title = "Neuro-genetska optimizacija performansi disk kočnice na povišenim temperaturama, Neuro-genetic optimization of disc brake performance at elevated temperatures",
pages = "149-142",
number = "2",
volume = "42",
doi = "10.5937/fmet1402142C"
}

Ćirović, V., Smiljanić, D., Aleksendrić, D.,& Simović, V.. (2014). Neuro-genetska optimizacija performansi disk kočnice na povišenim temperaturama. in FME Transactions
Univerzitet u Beogradu - Mašinski fakultet, Beograd., 42(2), 142-149.
https://doi.org/10.5937/fmet1402142C

Ćirović V, Smiljanić D, Aleksendrić D, Simović V. Neuro-genetska optimizacija performansi disk kočnice na povišenim temperaturama. in FME Transactions. 2014;42(2):142-149.
doi:10.5937/fmet1402142C .

Ćirović, Velimir, Smiljanić, Dušan, Aleksendrić, Dragan, Simović, V., "Neuro-genetska optimizacija performansi disk kočnice na povišenim temperaturama" in FME Transactions, 42, no. 2 (2014):142-149,
https://doi.org/10.5937/fmet1402142C . .

TY  - CHAP
AU  - Aleksendrić, Dragan
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1753
AB  - Researches indicate that human error is involved in over 90% of accidents. The automotive industry, especially in the area of commercial vehicles, needs new intelligent solutions that contribute to solving the key societal challenges posed by road transport related to the performance, road safety, and cost. "Intelligent" systems can help drivers to avoid accidents. The conventional "add-on" systems, like ABS, ASR, ESC, BAS or EBS support the driver in critical driving/braking situations by influencing the vehicle dynamics in order to avoid accidents or loss in stability or improve its maneuverability. Improvement of the brakes performance through their more precisely controls, under complex variety of operating regimes in dynamic conditions, is imposed as an unavoidable task. According to some initiatives, there is an attempt to move towards a new paradigm, one where vehicles don't crash anymore. It requires using of contemporary sophisticated methods in the field of artificial intelligence. The control of commercial vehicle braking systems performance and braking performance co-ordination between the tractor axels and its trailer, in the case of vehicle combination, is considered as the most important aspect of active safety of these vehicles. A need to improve commercial vehicles braking performance leads to better control of the most relevant disturbing factors and improving of braking forces management. A solution in the area of new technologies, better performance, and reducing the potential economic cost of the vehicle crashing or loss in stability has been proposed in this chapter by introducing of an intelligent control of commercial vehicle braking system performance. It is done using dynamic neural model of the brake performance. The basic precondition in implementation of intelligent braking was provided by introducing of dynamic modeling, prediction and control of the brakes performance during a braking cycle versus driver and/or road conditions demands (wheels slip). Based on that, the brake actuation pressure was intelligently controlled and adapt to the different driver/road/vehicles demands i.e. intelligent wheels slip has been provided. This allows possibilities for controlling of braking systems performance in a more accurate way then it was the case now a day.
PB  - Nova Science Publishers, Inc.
T2  - Airports and the Automotive Industry: Security Issues, Economic Efficiency and Environmental Impact
T1  - Intelligent braking - technology, performance and economic challenge
EP  - 64
SP  - 33
UR  - https://hdl.handle.net/21.15107/rcub_machinery_1753
ER  - 

@inbook{
author = "Aleksendrić, Dragan",
year = "2013",
abstract = "Researches indicate that human error is involved in over 90% of accidents. The automotive industry, especially in the area of commercial vehicles, needs new intelligent solutions that contribute to solving the key societal challenges posed by road transport related to the performance, road safety, and cost. "Intelligent" systems can help drivers to avoid accidents. The conventional "add-on" systems, like ABS, ASR, ESC, BAS or EBS support the driver in critical driving/braking situations by influencing the vehicle dynamics in order to avoid accidents or loss in stability or improve its maneuverability. Improvement of the brakes performance through their more precisely controls, under complex variety of operating regimes in dynamic conditions, is imposed as an unavoidable task. According to some initiatives, there is an attempt to move towards a new paradigm, one where vehicles don't crash anymore. It requires using of contemporary sophisticated methods in the field of artificial intelligence. The control of commercial vehicle braking systems performance and braking performance co-ordination between the tractor axels and its trailer, in the case of vehicle combination, is considered as the most important aspect of active safety of these vehicles. A need to improve commercial vehicles braking performance leads to better control of the most relevant disturbing factors and improving of braking forces management. A solution in the area of new technologies, better performance, and reducing the potential economic cost of the vehicle crashing or loss in stability has been proposed in this chapter by introducing of an intelligent control of commercial vehicle braking system performance. It is done using dynamic neural model of the brake performance. The basic precondition in implementation of intelligent braking was provided by introducing of dynamic modeling, prediction and control of the brakes performance during a braking cycle versus driver and/or road conditions demands (wheels slip). Based on that, the brake actuation pressure was intelligently controlled and adapt to the different driver/road/vehicles demands i.e. intelligent wheels slip has been provided. This allows possibilities for controlling of braking systems performance in a more accurate way then it was the case now a day.",
publisher = "Nova Science Publishers, Inc.",
journal = "Airports and the Automotive Industry: Security Issues, Economic Efficiency and Environmental Impact",
booktitle = "Intelligent braking - technology, performance and economic challenge",
pages = "64-33",
url = "https://hdl.handle.net/21.15107/rcub_machinery_1753"
}

Aleksendrić, D.. (2013). Intelligent braking - technology, performance and economic challenge. in Airports and the Automotive Industry: Security Issues, Economic Efficiency and Environmental Impact
Nova Science Publishers, Inc.., 33-64.
https://hdl.handle.net/21.15107/rcub_machinery_1753

Aleksendrić D. Intelligent braking - technology, performance and economic challenge. in Airports and the Automotive Industry: Security Issues, Economic Efficiency and Environmental Impact. 2013;:33-64.
https://hdl.handle.net/21.15107/rcub_machinery_1753 .

Aleksendrić, Dragan, "Intelligent braking - technology, performance and economic challenge" in Airports and the Automotive Industry: Security Issues, Economic Efficiency and Environmental Impact (2013):33-64,
https://hdl.handle.net/21.15107/rcub_machinery_1753 .

TY  - CONF
AU  - Aleksendrić, Dragan
AU  - Ćirović, Velimir
AU  - Jakovljević, Živana
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1751
AB  - Monitoring, modeling, prediction, and control of the braking process is a difficult task due to a complex interaction between the brake contact surfaces (disc pads and brake disc). It is caused by different influences of braking regimes and brake operation conditions on its performance. Faster and better control of the braking process is extremely important in order to provide harmonization of the generated braking torque with the tire-road adhesion conditions. It has significant influence on the stopping distance. The control of the braking process should be based on monitoring of the previous and current values of parameters that have influence on the brake performance. Primarily, it is related to the disc brake actuation pressure, the vehicle speed, and the brake interface temperature. The functional relationship between braking regimes and braking torque has to be established and continuously adapted according to the change of mentioned influencing factors. In this paper dynamic neural networks have been used for the purpose of modeling and control of the disc brake actuation pressure. Parameters of the developed dynamic neural model were used to build a program for implementation in a microcontroller. Recurrent neural networks have been implemented in 8-bit CMOS microcontroller for control of the disc brake actuation pressure. Two different models have been developed and integrated into the microcontroller. The first model was used for modeling and prediction of the braking torque. Based on that, the second inverse neural model, has been developed able to predict the brake actuation pressure needed for achieving previously selected (desired) braking torque value.
PB  - SAE International
C3  - SAE Technical Papers
T1  - Microcontroller based control of disc brake actuation pressure
VL  - 8
DO  - 10.4271/2013-01-2055
ER  - 

@conference{
author = "Aleksendrić, Dragan and Ćirović, Velimir and Jakovljević, Živana",
year = "2013",
abstract = "Monitoring, modeling, prediction, and control of the braking process is a difficult task due to a complex interaction between the brake contact surfaces (disc pads and brake disc). It is caused by different influences of braking regimes and brake operation conditions on its performance. Faster and better control of the braking process is extremely important in order to provide harmonization of the generated braking torque with the tire-road adhesion conditions. It has significant influence on the stopping distance. The control of the braking process should be based on monitoring of the previous and current values of parameters that have influence on the brake performance. Primarily, it is related to the disc brake actuation pressure, the vehicle speed, and the brake interface temperature. The functional relationship between braking regimes and braking torque has to be established and continuously adapted according to the change of mentioned influencing factors. In this paper dynamic neural networks have been used for the purpose of modeling and control of the disc brake actuation pressure. Parameters of the developed dynamic neural model were used to build a program for implementation in a microcontroller. Recurrent neural networks have been implemented in 8-bit CMOS microcontroller for control of the disc brake actuation pressure. Two different models have been developed and integrated into the microcontroller. The first model was used for modeling and prediction of the braking torque. Based on that, the second inverse neural model, has been developed able to predict the brake actuation pressure needed for achieving previously selected (desired) braking torque value.",
publisher = "SAE International",
journal = "SAE Technical Papers",
title = "Microcontroller based control of disc brake actuation pressure",
volume = "8",
doi = "10.4271/2013-01-2055"
}

Aleksendrić, D., Ćirović, V.,& Jakovljević, Ž.. (2013). Microcontroller based control of disc brake actuation pressure. in SAE Technical Papers
SAE International., 8.
https://doi.org/10.4271/2013-01-2055

Aleksendrić D, Ćirović V, Jakovljević Ž. Microcontroller based control of disc brake actuation pressure. in SAE Technical Papers. 2013;8.
doi:10.4271/2013-01-2055 .

Aleksendrić, Dragan, Ćirović, Velimir, Jakovljević, Živana, "Microcontroller based control of disc brake actuation pressure" in SAE Technical Papers, 8 (2013),
https://doi.org/10.4271/2013-01-2055 . .

TY  - GEN
AU  - Aleksendrić, Dragan
AU  - Ćirović, Velimir
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5768
PB  - Универзитета у Београду Машински факултет
T1  - Метода адаптивног неуро-фази управљања клизањем коченог точка
UR  - https://hdl.handle.net/21.15107/rcub_machinery_5768
ER  - 

@misc{
author = "Aleksendrić, Dragan and Ćirović, Velimir",
year = "2013",
publisher = "Универзитета у Београду Машински факултет",
title = "Метода адаптивног неуро-фази управљања клизањем коченог точка",
url = "https://hdl.handle.net/21.15107/rcub_machinery_5768"
}

Aleksendrić, D.,& Ćirović, V.. (2013). Метода адаптивног неуро-фази управљања клизањем коченог точка. 
Универзитета у Београду Машински факултет..
https://hdl.handle.net/21.15107/rcub_machinery_5768

Aleksendrić D, Ćirović V. Метода адаптивног неуро-фази управљања клизањем коченог точка. 2013;.
https://hdl.handle.net/21.15107/rcub_machinery_5768 .

Aleksendrić, Dragan, Ćirović, Velimir, "Метода адаптивног неуро-фази управљања клизањем коченог точка" (2013),
https://hdl.handle.net/21.15107/rcub_machinery_5768 .

TY  - JOUR
AU  - Ćirović, Velimir
AU  - Aleksendrić, Dragan
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1697
AB  - Due to complex and nonlinear dynamics of a braking process and complexity in the tire-road interaction, the control of automotive braking systems performance simultaneously with the wheel slip represents a challenging problem. The non-optimal wheel slip level during braking, causing inability to achieve the desired tire-road friction force strongly influences the braking distance. In addition, steerability and maneuverability of the vehicle could be disturbed. In this paper, an active neuro-fuzzy approach has been developed for improving the wheel slip control in the longitudinal direction of the commercial vehicle. The dynamic neural network has been used for prediction and an adaptive control of the brake actuation pressure, during each braking cycle, according to the identified maximum adhesion coefficient between the wheel and road surface. The brake actuation pressure was dynamically adjusted on the level that provides the optimal level of the longitudinal wheel slip vs. the brake pressure selected by driver, the current vehicle speed, the brake interface temperature, vehicle load conditions, and the current value of longitudinal wheel slip. Thus the dynamic neural network model operates (learn, generalize and predict) on-line during each braking cycle, fuzzy logic has been integrated with the neural model as a support to the neural controller control actions in the case when prediction error of the dynamic neural model reached the predefined value. The hybrid control approach presented here provided intelligent dynamic model - based control of the brake actuation pressure in order to keep the longitudinal wheel slip on the optimum level during a braking cycle.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Expert Systems With Applications
T1  - Adaptive neuro-fuzzy wheel slip control
EP  - 5209
IS  - 13
SP  - 5197
VL  - 40
DO  - 10.1016/j.eswa.2013.03.012
ER  - 

@article{
author = "Ćirović, Velimir and Aleksendrić, Dragan",
year = "2013",
abstract = "Due to complex and nonlinear dynamics of a braking process and complexity in the tire-road interaction, the control of automotive braking systems performance simultaneously with the wheel slip represents a challenging problem. The non-optimal wheel slip level during braking, causing inability to achieve the desired tire-road friction force strongly influences the braking distance. In addition, steerability and maneuverability of the vehicle could be disturbed. In this paper, an active neuro-fuzzy approach has been developed for improving the wheel slip control in the longitudinal direction of the commercial vehicle. The dynamic neural network has been used for prediction and an adaptive control of the brake actuation pressure, during each braking cycle, according to the identified maximum adhesion coefficient between the wheel and road surface. The brake actuation pressure was dynamically adjusted on the level that provides the optimal level of the longitudinal wheel slip vs. the brake pressure selected by driver, the current vehicle speed, the brake interface temperature, vehicle load conditions, and the current value of longitudinal wheel slip. Thus the dynamic neural network model operates (learn, generalize and predict) on-line during each braking cycle, fuzzy logic has been integrated with the neural model as a support to the neural controller control actions in the case when prediction error of the dynamic neural model reached the predefined value. The hybrid control approach presented here provided intelligent dynamic model - based control of the brake actuation pressure in order to keep the longitudinal wheel slip on the optimum level during a braking cycle.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Expert Systems With Applications",
title = "Adaptive neuro-fuzzy wheel slip control",
pages = "5209-5197",
number = "13",
volume = "40",
doi = "10.1016/j.eswa.2013.03.012"
}

Ćirović, V.,& Aleksendrić, D.. (2013). Adaptive neuro-fuzzy wheel slip control. in Expert Systems With Applications
Pergamon-Elsevier Science Ltd, Oxford., 40(13), 5197-5209.
https://doi.org/10.1016/j.eswa.2013.03.012

Ćirović V, Aleksendrić D. Adaptive neuro-fuzzy wheel slip control. in Expert Systems With Applications. 2013;40(13):5197-5209.
doi:10.1016/j.eswa.2013.03.012 .

Ćirović, Velimir, Aleksendrić, Dragan, "Adaptive neuro-fuzzy wheel slip control" in Expert Systems With Applications, 40, no. 13 (2013):5197-5209,
https://doi.org/10.1016/j.eswa.2013.03.012 . .

TY  - JOUR
AU  - Ćirović, Velimir
AU  - Aleksendrić, Dragan
AU  - Smiljanić, Dušan
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1655
AB  - The control of automotive braking systems performance and a wheel slip is a challenging problem due to nonlinear dynamics of a braking process and a tire-road interaction. When the wheel slip is not between the optimal limits during braking, the desired tire-road friction force cannot be achieved, which influences braking distance, the loss in steerability and maneuverability of the vehicle. In this paper, the new approach, based on dynamic neural networks, has been employed for improving of the longitudinal wheel slip control. This approach is based on dynamic adaptation of the brake actuation pressure, during a braking cycle, according to the identified maximum adhesion coefficient between the wheel and road. The brake actuated pressure was adjusted on the level which provides the optimal longitudinal wheel slip versus the brake actuated pressure selected by a driver, the current vehicle speed, load conditions, the brake interface temperature and the current value of the wheel slip. The dynamic neural network has been used for modeling of a nonlinear functional relationship between the brake actuation pressure and the longitudinal wheel slip during a braking cycle. It provided preconditions for control of the brake actuation pressure based on the wheel slip change.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Mechatronics
T1  - Longitudinal wheel slip control using dynamic neural networks
EP  - 146
IS  - 1
SP  - 135
VL  - 23
DO  - 10.1016/j.mechatronics.2012.11.007
ER  - 

@article{
author = "Ćirović, Velimir and Aleksendrić, Dragan and Smiljanić, Dušan",
year = "2013",
abstract = "The control of automotive braking systems performance and a wheel slip is a challenging problem due to nonlinear dynamics of a braking process and a tire-road interaction. When the wheel slip is not between the optimal limits during braking, the desired tire-road friction force cannot be achieved, which influences braking distance, the loss in steerability and maneuverability of the vehicle. In this paper, the new approach, based on dynamic neural networks, has been employed for improving of the longitudinal wheel slip control. This approach is based on dynamic adaptation of the brake actuation pressure, during a braking cycle, according to the identified maximum adhesion coefficient between the wheel and road. The brake actuated pressure was adjusted on the level which provides the optimal longitudinal wheel slip versus the brake actuated pressure selected by a driver, the current vehicle speed, load conditions, the brake interface temperature and the current value of the wheel slip. The dynamic neural network has been used for modeling of a nonlinear functional relationship between the brake actuation pressure and the longitudinal wheel slip during a braking cycle. It provided preconditions for control of the brake actuation pressure based on the wheel slip change.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Mechatronics",
title = "Longitudinal wheel slip control using dynamic neural networks",
pages = "146-135",
number = "1",
volume = "23",
doi = "10.1016/j.mechatronics.2012.11.007"
}

Ćirović, V., Aleksendrić, D.,& Smiljanić, D.. (2013). Longitudinal wheel slip control using dynamic neural networks. in Mechatronics
Pergamon-Elsevier Science Ltd, Oxford., 23(1), 135-146.
https://doi.org/10.1016/j.mechatronics.2012.11.007

Ćirović V, Aleksendrić D, Smiljanić D. Longitudinal wheel slip control using dynamic neural networks. in Mechatronics. 2013;23(1):135-146.
doi:10.1016/j.mechatronics.2012.11.007 .

Ćirović, Velimir, Aleksendrić, Dragan, Smiljanić, Dušan, "Longitudinal wheel slip control using dynamic neural networks" in Mechatronics, 23, no. 1 (2013):135-146,
https://doi.org/10.1016/j.mechatronics.2012.11.007 . .

TY  - JOUR
AU  - Ćirović, Velimir
AU  - Aleksendrić, Dragan
AU  - Mladenović, Dušan
PY  - 2012
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1351
AB  - The basic problem in the operation of automotive brakes is the unpredictable nature of the tribological processes that occur at the contact of the friction pair. The stochastic nature of the tribological contact of the disc brake is affected differently by the complex interaction between the brake disc and the friction material under different conditions because of the influences of the applied pressure, the speed and the brake interface temperature. Owing to the highly dynamic non-linear change in the braking torque induced by the complex situation at the contact of the disc brake, the braking torque could not be modelled, predicted and controlled using classical mathematical methods. This is related, in particular, to the dynamic change in the braking torque in a braking cycle. Dynamic modelling and prediction of the braking torque is very important for further improvement in the performance of the brakes of motor vehicles through more precise control of their performance with respect to the driver demands and the change in the adhesion between the tyre and the road. Recurrent dynamic neural networks were employed in this paper for modelling, prediction and control of the dynamic change in the braking torque during a braking cycle. The dynamic functional relationship between the changes in the applied pressure, the sliding speed, the brake interface temperature and the braking torque of the disc brake was established. The dynamic model developed was used to predict and control the braking torque during a braking cycle under different disc brake operation conditions.
PB  - Sage Publications Ltd, London
T2  - Proceedings of The Institution of Mechanical Engineers Part D-Journal of Automobile Engineering
T1  - Braking torque control using recurrent neural networks
EP  - 766
IS  - D6
SP  - 754
VL  - 226
DO  - 10.1177/0954407011428720
ER  - 

@article{
author = "Ćirović, Velimir and Aleksendrić, Dragan and Mladenović, Dušan",
year = "2012",
abstract = "The basic problem in the operation of automotive brakes is the unpredictable nature of the tribological processes that occur at the contact of the friction pair. The stochastic nature of the tribological contact of the disc brake is affected differently by the complex interaction between the brake disc and the friction material under different conditions because of the influences of the applied pressure, the speed and the brake interface temperature. Owing to the highly dynamic non-linear change in the braking torque induced by the complex situation at the contact of the disc brake, the braking torque could not be modelled, predicted and controlled using classical mathematical methods. This is related, in particular, to the dynamic change in the braking torque in a braking cycle. Dynamic modelling and prediction of the braking torque is very important for further improvement in the performance of the brakes of motor vehicles through more precise control of their performance with respect to the driver demands and the change in the adhesion between the tyre and the road. Recurrent dynamic neural networks were employed in this paper for modelling, prediction and control of the dynamic change in the braking torque during a braking cycle. The dynamic functional relationship between the changes in the applied pressure, the sliding speed, the brake interface temperature and the braking torque of the disc brake was established. The dynamic model developed was used to predict and control the braking torque during a braking cycle under different disc brake operation conditions.",
publisher = "Sage Publications Ltd, London",
journal = "Proceedings of The Institution of Mechanical Engineers Part D-Journal of Automobile Engineering",
title = "Braking torque control using recurrent neural networks",
pages = "766-754",
number = "D6",
volume = "226",
doi = "10.1177/0954407011428720"
}

Ćirović, V., Aleksendrić, D.,& Mladenović, D.. (2012). Braking torque control using recurrent neural networks. in Proceedings of The Institution of Mechanical Engineers Part D-Journal of Automobile Engineering
Sage Publications Ltd, London., 226(D6), 754-766.
https://doi.org/10.1177/0954407011428720

Ćirović V, Aleksendrić D, Mladenović D. Braking torque control using recurrent neural networks. in Proceedings of The Institution of Mechanical Engineers Part D-Journal of Automobile Engineering. 2012;226(D6):754-766.
doi:10.1177/0954407011428720 .

Ćirović, Velimir, Aleksendrić, Dragan, Mladenović, Dušan, "Braking torque control using recurrent neural networks" in Proceedings of The Institution of Mechanical Engineers Part D-Journal of Automobile Engineering, 226, no. D6 (2012):754-766,
https://doi.org/10.1177/0954407011428720 . .

TY  - CONF
AU  - Stamenković, Dragan
AU  - Popović, Vladimir
AU  - Aleksendrić, Dragan
PY  - 2012
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1516
AB  - Warranty is a powerful marketing instrument for the manufacturer and a good protection for both the manufacturer and the customer, but it always involves additional costs to the manufacturer. This paper deals with the warranty forecasting by estimating the future warranty returns in order to anticipate associated costs. The warranty prediction model is based on a Weibull analysis and a Monte Carlo simulation. Data gathered through the accelerated life-cycle analysis of 20 light bulb units of the new type were used as an input for the Weibull analysis to obtain the parameters needed for the simulation. MATLAB simulation algorithm was developed especially for this purpose. Warranty analysis was done for three variants of fully renewing combination free replacement and pro-rata policy and for several combinations of warranty period and free replacement period lengths. The results showed the relation between the parameters that define the policy and associated cost to the manufacturer. Presented simulation algorithm proved to be a fast and reliable tool for warranty cost analysis.
C3  - Proceedings - 18th ISSAT International Conference on Reliability and Quality in Design
T1  - Fully Renewing Combination Free Replacement and Pro-Rata Warranty Cost Assessment Using Monte Carlo Simulation
EP  - 319
SP  - 315
UR  - https://hdl.handle.net/21.15107/rcub_machinery_1516
ER  - 

@conference{
author = "Stamenković, Dragan and Popović, Vladimir and Aleksendrić, Dragan",
year = "2012",
abstract = "Warranty is a powerful marketing instrument for the manufacturer and a good protection for both the manufacturer and the customer, but it always involves additional costs to the manufacturer. This paper deals with the warranty forecasting by estimating the future warranty returns in order to anticipate associated costs. The warranty prediction model is based on a Weibull analysis and a Monte Carlo simulation. Data gathered through the accelerated life-cycle analysis of 20 light bulb units of the new type were used as an input for the Weibull analysis to obtain the parameters needed for the simulation. MATLAB simulation algorithm was developed especially for this purpose. Warranty analysis was done for three variants of fully renewing combination free replacement and pro-rata policy and for several combinations of warranty period and free replacement period lengths. The results showed the relation between the parameters that define the policy and associated cost to the manufacturer. Presented simulation algorithm proved to be a fast and reliable tool for warranty cost analysis.",
journal = "Proceedings - 18th ISSAT International Conference on Reliability and Quality in Design",
title = "Fully Renewing Combination Free Replacement and Pro-Rata Warranty Cost Assessment Using Monte Carlo Simulation",
pages = "319-315",
url = "https://hdl.handle.net/21.15107/rcub_machinery_1516"
}

Stamenković, D., Popović, V.,& Aleksendrić, D.. (2012). Fully Renewing Combination Free Replacement and Pro-Rata Warranty Cost Assessment Using Monte Carlo Simulation. in Proceedings - 18th ISSAT International Conference on Reliability and Quality in Design, 315-319.
https://hdl.handle.net/21.15107/rcub_machinery_1516

Stamenković D, Popović V, Aleksendrić D. Fully Renewing Combination Free Replacement and Pro-Rata Warranty Cost Assessment Using Monte Carlo Simulation. in Proceedings - 18th ISSAT International Conference on Reliability and Quality in Design. 2012;:315-319.
https://hdl.handle.net/21.15107/rcub_machinery_1516 .

Stamenković, Dragan, Popović, Vladimir, Aleksendrić, Dragan, "Fully Renewing Combination Free Replacement and Pro-Rata Warranty Cost Assessment Using Monte Carlo Simulation" in Proceedings - 18th ISSAT International Conference on Reliability and Quality in Design (2012):315-319,
https://hdl.handle.net/21.15107/rcub_machinery_1516 .

TY  - GEN
AU  - Ćirović, Velimir
AU  - Aleksendrić, Dragan
PY  - 2012
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5777
PB  - Универзитета у Београду Машински факултет
T1  - Метода предвиђања притиска активирања диск кочнице привредног возила у зависности од услова приањања у контакту пнеуматика и тла током кочења
UR  - https://hdl.handle.net/21.15107/rcub_machinery_5777
ER  - 

@misc{
author = "Ćirović, Velimir and Aleksendrić, Dragan",
year = "2012",
publisher = "Универзитета у Београду Машински факултет",
title = "Метода предвиђања притиска активирања диск кочнице привредног возила у зависности од услова приањања у контакту пнеуматика и тла током кочења",
url = "https://hdl.handle.net/21.15107/rcub_machinery_5777"
}

Ćirović, V.,& Aleksendrić, D.. (2012). Метода предвиђања притиска активирања диск кочнице привредног возила у зависности од услова приањања у контакту пнеуматика и тла током кочења. 
Универзитета у Београду Машински факултет..
https://hdl.handle.net/21.15107/rcub_machinery_5777

Ćirović V, Aleksendrić D. Метода предвиђања притиска активирања диск кочнице привредног возила у зависности од услова приањања у контакту пнеуматика и тла током кочења. 2012;.
https://hdl.handle.net/21.15107/rcub_machinery_5777 .

Ćirović, Velimir, Aleksendrić, Dragan, "Метода предвиђања притиска активирања диск кочнице привредног возила у зависности од услова приањања у контакту пнеуматика и тла током кочења" (2012),
https://hdl.handle.net/21.15107/rcub_machinery_5777 .