Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization

Tool wear condition significantly influences equipment downtime and machining precision, necessitating the exploration of a more accurate tool wear state identification technique. In this paper, the wavelet packet thresholding denoising method is used to process the acquired multi-source signals and...

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Bibliographic Details
Published in:Sensors
Main Authors: Jiaqi Wang, Zhong Xiang, Xiao Cheng, Ji Zhou, Wenqi Li
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2023
Subjects:
tool wear state identification
recursive feature elimination
improved northern goshawk optimization
support vector machine
Chemical technology
TP1-1185
Northern Goshawk
Online Access:https://doi.org/10.3390/s23208591
https://doaj.org/article/038b4e8c163e43e1a12a824a49281c29
Description
Summary:Tool wear condition significantly influences equipment downtime and machining precision, necessitating the exploration of a more accurate tool wear state identification technique. In this paper, the wavelet packet thresholding denoising method is used to process the acquired multi-source signals and extract several signal features. The set of features most relevant to the tool wear state is screened out by the support vector machine recursive feature elimination (SVM-RFE). Utilizing these selected features, we propose a tool wear state identification model, which utilizes an improved northern goshawk optimization (INGO) algorithm to optimize the support vector machine (SVM), hereby referred to as INGO-SVM. The simulation tests reveal that INGO demonstrates superior convergence efficacy and stability. Furthermore, a milling wear experiment confirms that this approach outperforms five other methods in terms of recognition accuracy, achieving a remarkable accuracy rate of 97.9%.

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