Artificial neural networks in advanced thermoset matrix composite manufacturing
Само за регистроване кориснике
2018
Поглавље у монографији (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
Curing process / Composite material / Artificial neural networksИзвор:
Lecture Notes in Mechanical Engineering, 2018, 0, 9783319895628, 78-88Издавач:
- Pleiades journals
Колекције
Институција/група
Mašinski fakultetTY - 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 . .