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Structural Engineering and Mechanics Volume 31, Number 1, January10 2009 , pages 57-74 DOI: https://doi.org/10.12989/sem.2009.31.1.057 |
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Milling tool wear forecast based on the partial least-squares regression analysis |
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Xu Chuangwen and Chen Hualing
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| Abstract | ||
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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. | ||
| Key Words | ||
| partial least-squares regression; singular value decomposition; tool wear; cutting experiment. | ||
| Address | ||
| Xu Chuangwen School of Mechanical Engineering, Xi?an Jiaotong University, Xi?an, Shanxi, China Department of Mechanical Engineering, Lanzhou Polytechnic College, Lanzhou, Gansu, China Chen Hualing School of Mechanical Engineering, Xi?an Jiaotong University, Xi?an, Shanxi, China | ||