EXAMINATION OF THE WEAR BEHAVIOR OF CU-BASED BRAKE PADS USED IN HIGH-SPEED TRAINS AND PREDICTION THROUGH STATISTICAL AND NEURAL NETWORK MODELS

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dc.authoridEkinci, Serafettin/0000-0003-0885-5903
dc.authoridAKKUS, Harun/0000-0002-9033-309X
dc.contributor.authorEkinci, Serafettin
dc.contributor.authorAsilturk, Ilhan
dc.contributor.authorAkkus, Harun
dc.contributor.authorMahammadzade, Akshin
dc.date.accessioned2024-11-07T13:34:21Z
dc.date.available2024-11-07T13:34:21Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe aim of this study is to provide insights into the performance of copper-based brake pads used in high-speed trains and contribute to a more predictable braking system by leveraging mathematical and artificial intelligence (AI) models. The wear behavior of Cu-based brake pads in high-speed trains was investigated using a pin-on-disc test setup under different speeds, temperatures, and loads with a constant sliding distance. Additionally, mathematical and AI models were developed to predict the friction coefficient and wear rate values obtained from the experiments. This innovative approach initiates a significant discussion in line with a current need, and the sharing and publication of the obtained results are currently essential to address the knowledge gap in this field. The results revealed that an increase in temperature led to an increase in both the friction coefficient and wear rate. Conversely, an increase in load resulted in a decrease in both the friction coefficient and wear rate. The transition from abrasive wear to adhesive wear occurred due to the softening of copper between friction surfaces, leading to material transfer. According to the results obtained from the models, both the artificial neural network (ANN) and multiple regression models demonstrated comparable accuracy, predicting the friction coefficient with approximately 94% accuracy in both cases, indicating reliable predictions. For the wear rate, the models achieved approximately 90% and 92% accuracy, respectively.
dc.description.sponsorshipScientific Research Projects Coordination Office of Selcuk University [22201032]
dc.description.sponsorshipThe authors acknowledge the support of the Scientific Research Projects Coordination Office of Selcuk University, Project Number: 22201032.
dc.identifier.doi10.1142/S0218625X24500628
dc.identifier.issn0218-625X
dc.identifier.issn1793-6667
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85181454582
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1142/S0218625X24500628
dc.identifier.urihttps://hdl.handle.net/11480/15939
dc.identifier.volume31
dc.identifier.wosWOS:001134399300003
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWorld Scientific Publ Co Pte Ltd
dc.relation.ispartofSurface Review and Letters
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectANN
dc.subjectCu-based brake pad
dc.subjectfriction coefficient
dc.subjectmathematical model
dc.subjectSEM
dc.subjectwear rate
dc.titleEXAMINATION OF THE WEAR BEHAVIOR OF CU-BASED BRAKE PADS USED IN HIGH-SPEED TRAINS AND PREDICTION THROUGH STATISTICAL AND NEURAL NETWORK MODELS
dc.typeArticle

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