Research on the identification of polymer interface wear types based on machine learning algorithms

Xiaohao Wen; Shuwen Wang

doi:10.1117/12.3012299

21 December 2023 Research on the identification of polymer interface wear types based on machine learning algorithms

Xiaohao Wen, Shuwen Wang

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297025 (2023) https://doi.org/10.1117/12.3012299
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

To monitor the in-situ wear state of friction pairs, a friction noise signal processing method based on polymer interface wear performance prediction was introduced. Four types of friction noise signal datasets were collected from four different friction surfaces consisting of three constant load-speed points of six temperature points ranging from-120°Cto 25 °C and seven types of metal counterparts. Three wear states, including adhesive wear, wear, and a combination of both, were defined using field emission scanning electron microscopy for polymer wear surfaces. Six friction noise features were extracted from the time-domain signal of the sound using three machine learning algorithms, including random forest, deep forest, and XGBoost, to classify the three wear states. The results indicate that the deep forest algorithm exhibited the most effective classification performance. As a result, this method holds promising potential for on-site and real-time condition monitoring, as well as wear classification in the field of tribology applications.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaohao Wen and Shuwen Wang "Research on the identification of polymer interface wear types based on machine learning algorithms", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297025 (21 December 2023); https://doi.org/10.1117/12.3012299

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