Computer Science – Computational Engineering – Finance – and Science
Scientific paper
2009-02-04
Computer Science
Computational Engineering, Finance, and Science
Scientific paper
This paper presents an optimization technique for the multi-pass face milling process. Genetic algorithm (GA) is used to obtain the optimum cutting parameters by minimizing the unit production cost for a given amount of material removal. Cutting speed, feed and depth of cut for the finish and rough passes are the cutting parameters. An equal depth of cut for roughing passes has been considered. A lookup table containing the feasible combinations of depth of cut in finish and rough passes is generated so as to reduce the number of variables by one. The resulting mixed integer nonlinear optimization problem is solved in a single step using GA. The entire technique is demonstrated in a case study. Post optimality analysis of the example problem is done to develop a strategy for optimizing without running GA again for different values of total depth of cut.
No associations
LandOfFree
Genetic algorithm based optimization and post optimality analysis of multi-pass face milling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Genetic algorithm based optimization and post optimality analysis of multi-pass face milling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Genetic algorithm based optimization and post optimality analysis of multi-pass face milling will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-553731