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Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

查看全文 作　　者：Qixin [1]Lan;Binqiang [1]Chen;Bin [1]Yao;Wangpeng [2]He 高影响力作者 机构地区：[1]School of Aerospace Engineering,Xiamen University,Xiamen,361005,China;[2]School of Aerospace Science and Technology,Xidian University,Xi’an,710126,China高影响力机构 出　　处：《Computer Modeling in Engineering & Sciences》索引2024年第138卷第3期,共20页高影响力期刊 基　　金：the National Key Research and Development Program of China(No.2020YFB1713500);the Natural Science Basic Research Program of Shaanxi(Grant No.2023JCYB289);the National Natural Science Foundation of China(Grant No.52175112);the Fundamental Research Funds for the Central Universities(Grant No.ZYTS23102). 摘　　要：The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains. 关 键 词：Multi-working conditions tool wear state recognition unsupervised transfer learning domain adaptation maximum mean discrepancy(MMD)
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