Cutting tool wear detection method based on fusion of ELM and wavelet packet decomposition

Huijing Li; Renming Deng

doi:10.1117/12.2637399

15 June 2022 Cutting tool wear detection method based on fusion of ELM and wavelet packet decomposition

Huijing Li, Renming Deng

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122851F (2022) https://doi.org/10.1117/12.2637399
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

Aimed at the puzzle that is difficult to detect the cutting tool wear during machining process, the paper discussed a method of tool wear detection based on fusion between Wavelet packet decomposition and extreme learning machine (ELM). In this paper, it firstly analysed the time-frequency correlated characteristics in sound signal of cutting tool, an then based on wavelet packet decomposition it explored the extraction method of statistical feature for cutting tool status-sensitive spectrum energy, and finally based on sound feature recognition, it constructed a sort of fast ELM detection model. Taken the sound signal identification of cutting wear as example in some operation site, the data of actual measuring in work site verified that the explored method above could get faster response speed and higher detection accuracy than other traditionally used methods. The experimental simulation results show that the discussed method is effective and reasonable.

Citation Download Citation

Huijing Li and Renming Deng "Cutting tool wear detection method based on fusion of ELM and wavelet packet decomposition", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122851F (15 June 2022); https://doi.org/10.1117/12.2637399

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