Abstract
Tool wear prediction plays an important role in guaranteeing the workpiece quality and improving the production efficiency. However, because of the uncertainty and complexity of tool wear process, it is hard to ensure that the samples related to all tool wear values can be collected during the training stage. Therefore, the accuracy of tool wear prediction for these uncovered data will deteriorate severely. In this paper, partial least square regression is presented to realize the tool wear prediction based on force signal. The main characteristic of this method is that the regression analysis is in the principal component space so that the multicollinearity between explanatory variables can be avoided effectively. Side milling experiment was carried out to validate the effectiveness of the proposed model. The analysis and comparison under different number of uncovered data show that the partial least square regression based tool wear prediction is more accurate.
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Recommended by Associate Editor Jihong Hwang
Guofeng Wang is currently an associate professor in School of Mechanical Engineering, Tianjin University, China. He received his Ph.D. degree from Tianjin University, China, in March 2002. His research interests include dynamic modeling and condition monitoring of machining process.
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Wang, G., Guo, Z. & Qian, L. Tool wear prediction considering uncovered data based on partial least square regression. J Mech Sci Technol 28, 317–322 (2014). https://doi.org/10.1007/s12206-013-0982-1
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DOI: https://doi.org/10.1007/s12206-013-0982-1