Lifespan prediction of cutting tools for high-value-added products

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• 作者：Cun Rong Li (1)
  Bhaba R. Sarker (2)
  
• 关键词：Tool life prediction ; Multifactor regression ; Cutting tools ; Stochastic variables
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2013
• 出版时间：November 2013
• 年：2013
• 卷：69
• 期：5-8
• 页码：1887-1894
• 全文大小：
• 作者单位：Cun Rong Li (1)
  Bhaba R. Sarker (2)
  
  1. Department of Industrial Engineering, School of Mechanical & Electronics Engineering, Wuhan University of Technology, 122 Luoshi Road, Hongshan District, Wuhan City, People’s Republic of China, 430070
  2. Department of Mechanical & Industrial Engineering, Louisiana State University, 2153 Patrick F. Taylor Hall, Baton Rouge, LA, 70803, USA
  
• ISSN：1433-3015

文摘

The traditional system for estimating lifespan of machining tools is based on the F. W. Taylor’s equation that uses fixed cutting factors. In this paper, an integrated model is proposed to get a better prediction of tool lifespan in machining process. The lifespan prediction of cutting tools having multiple affecting factors is studied here for machining parts with stochastic behavior of Taylor’s measuring parameter, k. The measuring variable k used to be regarded normally as a constant under predetermined conditions. In practice, lifespan of a tool lies in the range of a time interval rather than a fixed value. So, by calculating the impact of the stochastic variable k, a relative precise range of tool lifespan can be computed and this is particularly important for high-value-added products in manufacturing process because high-value-added products are usually extremely sensitive to the performance quality and failure cost of cutting tools, resulting in economic loss of the company both in terms of tool cost and product cost. A multiple linear regression approach is employed here to estimate the range of tool lifespan for the predictive model. A case study is conducted to show how this model works. The results of the improved manipulation of the parameters could influence the manufacturing system with a better control accuracy. To illustrate the significance of the transformed model, a case study was introduced that shows how the stochastic variable k influences the lifespan of a cutting tool. Relatively precise numerical results can be computed with this kind of analysis to direct a better comprehension of the factors for the cutting tools.