Dynamic speed control of a machine tool with stochastic tool life: analysis and simulation
- 271 Downloads
We discuss a tool management model for a flexible machine equipped with a tool magazine, variable cutting speed, and sensors to monitor tool wear, when tool life due to flank wear is stochastic. The objective is to adjust the cutting speed as a function of remaining distance, each time a tool change occurs, in order to minimize the expected processing time (sum of cutting and tool setup time). We address the computational aspects of finding optimal decision rules and we present numerical results suggesting that easily computed decision rules of a simple static model are near-optimal for our dynamic programming model. Dynamic adjustment is assessed with simulation experiments.
KeywordsStochastic control Optimal control Dynamic programming Flexible manufacturing systems Machining Machine tools Random lifetime Stochastic modelling Renewal processes
- Assani, O. A. (2008). Méthodes d’optimisation stochastique sur la commande d’une machine-outil. Master’s thesis, Université Laval, Québec, Canada.Google Scholar
- Barlow, R. E., & Proschan, F. (1965). Mathematical theory of reliability. New York: Wiley.Google Scholar
- Dernardo, E. V. (1982). Dynamic programming: Models and applications. New York: Prentice-Hall.Google Scholar
- Iakovou, E., Ip, C. M., & Koulamas, C. (1996). Optimal machining speed and tool inventory policies in machining economic systems. IIE Transactions, 28, 601–606.Google Scholar
- Lamond, B. F. (2010). Dynamic speed adjustment of a machine tool. In Proceedings of 5th international IEEE conference on intelligent systems (IEEE-IS’10), (pp. 248–253). University of Westminster, Harrow, London, UK.Google Scholar
- Lamond, B. F., Sodhi, M. S. (2005) Managing tool magazine capacity in flexible manufacturing system with random tool life: Convexity of optimal expected processing time. Working paper FSA-2005-05, Université Laval, Québec, Canada.Google Scholar
- Lamond, B. F., Sodhi, M. S., & Noël, M. (1998). Stochastic tool life models in flexible manufacturing. Proceedings CSME Forum, 3, 127–133.Google Scholar
- Lamond, B. F., & Sodhi, M. S. (1997). Using tool life models to minimize processing time on a flexible machine. IIE Transactions, 29, 611–621.Google Scholar
- Liu, P. H., Makis, V., & Jardine, A. K. S. (2001). Scheduling of the optimal tool replacement times in a flexible manufacturing. IIE Transactions, 33(6), 487–495. Google Scholar
- Noël, M. (2006). Stochastic models for tool management on a flexible machine. Doctoral dissertation, Université Laval, Québec, Canada.Google Scholar
- Ramalingam, S., & Watson J. D. (1977). Tool-life distributions. Part 1: Single-injury tool-life model. Journal of Engineering for Industry-Transactions ASME, 99, 519–522.Google Scholar
- Ramalingam, S. (1977). Tool-life distributions. Part 2: Multiple-injury tool-life model. Journal of Engineering for Industry-Transactions ASME, 99, 523–530.Google Scholar
- Silva, R. G., & Wilcox, S. J. (2008). Sensor based condition monitoring feature selection using a self-organizing map. In Proceedings of world congress on engineering 2008, volume II, (pp. 1247–1252). WCE 2008, London, U.K.Google Scholar
- Song, D. P., Hicks, C. & Earl, C. F. (2003). Dynamic production planning and rescheduling for complex assemblies. Retrieved August 2004, from http://www-mmd.eng.cam.ac.uk/mcn/pdf_files/part6_10.pdf.
- Wang, G., & Cui, Y. (2012). On-line tool wear monitoring based on auto associative neural network. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-012-0636-7.