Structural Engineering and Mechanics

  • 제31권1호
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  • Pages.57-74
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  • 2009
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Milling tool wear forecast based on the partial least-squares regression analysis

  • Xu, Chuangwen (School of Mechanical Engineering, Xi'an Jiaotong University, Department of Mechanical Engineering, Lanzhou Polytechnic College) ;
  • Chen, Hualing (School of Mechanical Engineering, Xi'an Jiaotong University)
  • 투고 : 2006.10.25
  • 심사 : 2008.12.02
  • 발행 : 2009.01.10
⟨ 이전 논문 다음 논문 ⟩

초록

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

키워드

과제정보

연구 과제 주관 기관 : Natural Science Foundation of Gansu Province

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피인용 문헌

  1. The concept and progress of intelligent spindles: A review vol.112, 2017, https://doi.org/10.1016/j.ijmachtools.2016.10.005
  2. Milling Tool Wear Identification Using Information Fusion vol.496-500, pp.1662-7482, 2014, https://doi.org/10.4028/www.scientific.net/AMM.496-500.1681