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

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作者： Jiaqi,Wang ^[1] ; Zhong,Xiang ^[1] ; Xiao,Cheng ^[1] ; Ji,Zhou ^[2] ; Wenqi,Li ^[1]

作者单位： School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China. ^[1] Longgang Institute of Zhejiang Sci-Tech University, Wenzhou 325802, China. ^[2]

关键词 improved northern goshawk optimization recursive feature elimination support vector machine tool wear state identification

DOI 10.3390/s23208591

PMID 37896684

发布时间 2023-10-30

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Sensors (Basel, Switzerland)

2023年23卷20期

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

Sensors (Basel, Switzerland)

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

Sensors (Basel, Switzerland)

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