Optimization of cutting conditions using an evolutive online procedure
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This paper proposes an online evolutive procedure to optimize the Material Removal Rate in a turning process considering a stochastic constraint. The usual industrial approach in finishing operations is to change the tool insert at the end of each machining feature to avoid defective parts. Consequently, all parts are produced at highly conservative conditions (low levels of feed and speed), and therefore, at low productivity. In this work, a framework to estimate the stochastic constraint of tool wear during the production of a batch is proposed. A simulation campaign was carried out to evaluate the performances of the proposed procedure. The results showed that it was possible to improve the Material Removal Rate during the production of the batch and keeping the probability of defective parts under a desired level.
KeywordsTool wear Stochastic constraint Machining Optimization
The authors sincerely thank the reviewers for their very helpful comments on earlier drafts of this manuscript, for their time and for their encouragement.
- Box, G. E. P., & Wilson, K. B. (1992). “On the experimental attainment of optimum conditions”. Breakthroughs in statistics (pp. 270–310). New York: Springer.Google Scholar
- Davim, J. P. (Ed.). (2008). Machining: Fundamentals and recent advances. London: Springer.Google Scholar
- Del Castillo, E. (2007). Process optimization: A statistical approach (Vol. 105). New York: Springer.Google Scholar
- Draper, N., & Smith, H. (2005). Applied regression analysis (3rd ed.). New York: Wiley.Google Scholar
- Ganesan, H., Mohankumar, G., Ganesan, K., & Ramesh Kumar, K. (2011). Optimization of machining parameters in turning process using genetic algorithm and particle swarm optimization with experimental verification. International Journal of Engineering Science and Technology (IJEST), 3(2), 1091–1102.Google Scholar
- Kalpakjian, S., & Schmidt, S. R. (2001). Manufacturing engineering and technology (4th ed.). Upper Saddle River: Prentice Hall International.Google Scholar
- Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response surface methodology: Process and product optimization using designed experiments (3rd ed.). New York: Wiley.Google Scholar
- Taylor, F. W. (1907). On the art of cutting metals. New York: American Society of Mechanical Engineers.Google Scholar
- Venkata Rao, R. (2016). Teaching learning based optimization algorithm and its engineering applications. New York: Springer.Google Scholar