Research on the end mill life prediction method based on feature fusion

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Abstract In modern manufacturing, end mill wear monitoring is essential to ensure machining quality and enhance production efficiency. Traditional monitoring methods usually rely on a single source of information. However, they may face the problem of incomplete information in complex environment. Therefore, effective integration of multi-source information to accurately predict the life of end mills has become the focus of research. In this paper, we propose a method of multi-source information acquisition and processing based on the time window. Aiming at the limitations of traditional image processing methods. The image preprocessing technology based on CLAHE histogram equalization and Sobel operator is used to accurately extract the tool tip wear zone. Based on Pearson correlation analysis, the sensor signal features are fused by constructing polygonal radar chart. The fusion feature space polyhedron is introduced to fuse the multi-source wear information features with equal weight. The tool wear process is characterized by volume change of the polyhedron. In view of the temporal relationship of the tool wear process, considering the hyperparameter optimization of the model, the SCSBiLSTMSE neural network model is constructed. Experiments show that the model performs superiorly in predicting the remaining useful life of end mills. On this basis, the model is corrected through experimental data to further increase the accuracy of the prediction. This research provides a new solution for the acquisition, processing, fusion and life prediction of multi-source wear information of end mills, which has important theoretical significance and practical application value.
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Research on the end mill life prediction method based on feature fusion | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on the end mill life prediction method based on feature fusion Ying Tian, Kaining Zhao, Yuxiang Wang, Jing Kang, Taiyong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7308620/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In modern manufacturing, end mill wear monitoring is essential to ensure machining quality and enhance production efficiency. Traditional monitoring methods usually rely on a single source of information. However, they may face the problem of incomplete information in complex environment. Therefore, effective integration of multi-source information to accurately predict the life of end mills has become the focus of research. In this paper, we propose a method of multi-source information acquisition and processing based on the time window. Aiming at the limitations of traditional image processing methods. The image preprocessing technology based on CLAHE histogram equalization and Sobel operator is used to accurately extract the tool tip wear zone. Based on Pearson correlation analysis, the sensor signal features are fused by constructing polygonal radar chart. The fusion feature space polyhedron is introduced to fuse the multi-source wear information features with equal weight. The tool wear process is characterized by volume change of the polyhedron. In view of the temporal relationship of the tool wear process, considering the hyperparameter optimization of the model, the SCSBiLSTMSE neural network model is constructed. Experiments show that the model performs superiorly in predicting the remaining useful life of end mills. On this basis, the model is corrected through experimental data to further increase the accuracy of the prediction. This research provides a new solution for the acquisition, processing, fusion and life prediction of multi-source wear information of end mills, which has important theoretical significance and practical application value. Tool wear detection Tool life prediction Fusion feature space polyhedron Prediction correction Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Traditional monitoring methods usually rely on a single source of information. However, they may face the problem of incomplete information in complex environment. Therefore, effective integration of multi-source information to accurately predict the life of end mills has become the focus of research. In this paper, we propose a method of multi-source information acquisition and processing based on the time window. Aiming at the limitations of traditional image processing methods. The image preprocessing technology based on CLAHE histogram equalization and Sobel operator is used to accurately extract the tool tip wear zone. Based on Pearson correlation analysis, the sensor signal features are fused by constructing polygonal radar chart. The fusion feature space polyhedron is introduced to fuse the multi-source wear information features with equal weight. The tool wear process is characterized by volume change of the polyhedron. In view of the temporal relationship of the tool wear process, considering the hyperparameter optimization of the model, the SCSBiLSTMSE neural network model is constructed. Experiments show that the model performs superiorly in predicting the remaining useful life of end mills. On this basis, the model is corrected through experimental data to further increase the accuracy of the prediction. 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