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《China Mechanical Engineering》 2018-07
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Modeling Method for Tool Wear Prediction Based on ADNLSSVM

XIAO Pengfei;ZHANG Chaoyong;LUO Min;LIN Wenwen;School of Mechanical Science & Engineering,Huazhong University of Science and Technology;Mechanical Engineering & Mechanics,Ningbo University;  
In the building process of a tool condition prediction model with traditional machine learning methods,the limited number of available training samples and the fixed length of the sliding time window and prediction model resulted in lower modeling accuracy and efficiency.Dynamic model was set up to monitor the tool wear states by using an ADNLSSVM.Feature vectors were extracted by time-frequency-domain analysis from data set of open database of milling processes,and parts of them were selected by correlation analysis as model inputs.The experimental results shows better modeling efficiency and prediction accuracy.
【Fund】: 国家自然科学基金资助项目(51575211);国家自然科学基金国际(地区)合作交流资助项目(51561125002);; 高等学校学科创新引智计划资助项目(B16019);; 湖北省自然科学基金资助项目(2014CFB348)
【CateGory Index】: TG71
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