Fade performance prediction of automotive friction materials by means of artificial neural networks
Само за регистроване кориснике
2007
Чланак у часопису (Објављена верзија)
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Temperature sensitivity of friction materials has always been a critical aspect while ensuring their smooth and reliable functioning, and that sensitivity need to be constantly optimized. The performance of friction materials at elevated temperatures is defined by their fading performance. In this paper, possibilities for predicting the fading performance of the friction materials, regarding their formulation and manufacturing conditions, have been investigated by means of artificial neural networks. The neural modelling of the friction materials behaviour at elevated temperatures has been based on the two different training data sets regarding the number, type, and distribution of the stored data. The first training data set is consisted by 360 data related to cold, fading, and recovery performance. These data have been used for developing of the neural model for predicting not only the fading performance but also cold and recovery performance. The second training data set, consisted ...by 120 data, has been used for developing the neural model that is going to be only used for predicting the fading performance of the friction materials. In this paper, 18 neural networks have been trained by the 5 training algorithms. These networks have been tested by the testing data set formed using the parameters of formulating, manufacturing, and testing of the two friction materials which input parameters were completely unknown for the networks.
Кључне речи:
prediction / neural models / fade performance / automotive friction material / artificial neural networksИзвор:
Wear, 2007, 262, 7-8, 778-790Издавач:
- Elsevier Science Sa, Lausanne
DOI: 10.1016/j.wear.2006.08.013
ISSN: 0043-1648
WoS: 000245063500003
Scopus: 2-s2.0-33847106956
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Aleksendrić, Dragan AU - Duboka, Čedomir PY - 2007 UR - https://machinery.mas.bg.ac.rs/handle/123456789/732 AB - Temperature sensitivity of friction materials has always been a critical aspect while ensuring their smooth and reliable functioning, and that sensitivity need to be constantly optimized. The performance of friction materials at elevated temperatures is defined by their fading performance. In this paper, possibilities for predicting the fading performance of the friction materials, regarding their formulation and manufacturing conditions, have been investigated by means of artificial neural networks. The neural modelling of the friction materials behaviour at elevated temperatures has been based on the two different training data sets regarding the number, type, and distribution of the stored data. The first training data set is consisted by 360 data related to cold, fading, and recovery performance. These data have been used for developing of the neural model for predicting not only the fading performance but also cold and recovery performance. The second training data set, consisted by 120 data, has been used for developing the neural model that is going to be only used for predicting the fading performance of the friction materials. In this paper, 18 neural networks have been trained by the 5 training algorithms. These networks have been tested by the testing data set formed using the parameters of formulating, manufacturing, and testing of the two friction materials which input parameters were completely unknown for the networks. PB - Elsevier Science Sa, Lausanne T2 - Wear T1 - Fade performance prediction of automotive friction materials by means of artificial neural networks EP - 790 IS - 7-8 SP - 778 VL - 262 DO - 10.1016/j.wear.2006.08.013 ER -
@article{ author = "Aleksendrić, Dragan and Duboka, Čedomir", year = "2007", abstract = "Temperature sensitivity of friction materials has always been a critical aspect while ensuring their smooth and reliable functioning, and that sensitivity need to be constantly optimized. The performance of friction materials at elevated temperatures is defined by their fading performance. In this paper, possibilities for predicting the fading performance of the friction materials, regarding their formulation and manufacturing conditions, have been investigated by means of artificial neural networks. The neural modelling of the friction materials behaviour at elevated temperatures has been based on the two different training data sets regarding the number, type, and distribution of the stored data. The first training data set is consisted by 360 data related to cold, fading, and recovery performance. These data have been used for developing of the neural model for predicting not only the fading performance but also cold and recovery performance. The second training data set, consisted by 120 data, has been used for developing the neural model that is going to be only used for predicting the fading performance of the friction materials. In this paper, 18 neural networks have been trained by the 5 training algorithms. These networks have been tested by the testing data set formed using the parameters of formulating, manufacturing, and testing of the two friction materials which input parameters were completely unknown for the networks.", publisher = "Elsevier Science Sa, Lausanne", journal = "Wear", title = "Fade performance prediction of automotive friction materials by means of artificial neural networks", pages = "790-778", number = "7-8", volume = "262", doi = "10.1016/j.wear.2006.08.013" }
Aleksendrić, D.,& Duboka, Č.. (2007). Fade performance prediction of automotive friction materials by means of artificial neural networks. in Wear Elsevier Science Sa, Lausanne., 262(7-8), 778-790. https://doi.org/10.1016/j.wear.2006.08.013
Aleksendrić D, Duboka Č. Fade performance prediction of automotive friction materials by means of artificial neural networks. in Wear. 2007;262(7-8):778-790. doi:10.1016/j.wear.2006.08.013 .
Aleksendrić, Dragan, Duboka, Čedomir, "Fade performance prediction of automotive friction materials by means of artificial neural networks" in Wear, 262, no. 7-8 (2007):778-790, https://doi.org/10.1016/j.wear.2006.08.013 . .