Research in tool condition visual diagnosis methods of manufacturing process based on DNN

doi:10.16731/j.cnki.1671-3133.2018.06.015

Abstract

Abstract: Tool state monitoring in manufacturing process is an important step to ensure accurate,efficient and safe machining of workpiece.The basic principle of tooling state diagnosis method based on Deep Neural Network (DNN) is described.Based on the study of DNN structure and training algorithm,a tool state recognition method based on DNN is studied.The cylindrical turning process of CK6143\1000 CNC lathe,KC5010 turning tool and austenitic stainless steel 304L is taken as the experiment object,and the tool image is collected to carry out experiment.A set of DNN for tool state recognition is realized.The result shows that the accuracy of tool state recognition is over 98 %,which proves the feasibility and validity of the method and has good engineering application value.

Key words: DNN, tool state, visual diagnosis method, manufacturing process

CLC Number:

TH16

Hu Wei, Li Ming, Zhang Yu, Wang Zhipeng. Research in tool condition visual diagnosis methods of manufacturing process based on DNN[J]. Modern Manufacturing Engineering, 2018, 453(6): 81-85.

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Research in tool condition visual diagnosis methods of manufacturing process based on DNN

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