Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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Machine Learning Data Analysis for Tool Wear Prediction in Core Multi Process Machining

코어 다중가공에서 공구마모 예측을 위한 기계학습 데이터 분석

  • Choi, Sujin (Department of Smart Convergence Mold, Korea Polytechinc) ;
  • Lee, Dongju (Industrial & Systems Engineering, Kongju National Universisty) ;
  • Hwang, Seungkuk (Department of Mechanical system, Korea Polytechinc)
  • 최수진 (한국폴리텍VII대학 스마트융합금형과) ;
  • 이동주 (공주대학교 산업시스템공학과) ;
  • 황승국 (한국폴리텍VII대학 기계시스템과)
  • Received : 2021.08.09
  • Accepted : 2021.08.22
  • Published : 2021.09.30
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Abstract

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

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References

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