Cutting Parameter Optimization for End Milling Operation Using Advanced Metaheuristic Algorithms

In die manufacturing industries surface roughness is considered as a vital quality characteristic in order to retain the consumers’ satisfaction. On the other hand, manufacturers want to minimize the machining time which eventually reduces their cost. This research deals with an optimization problem to minimize the machining time (T) for end milling operation on hot die steel (H13), subject to specified surface roughness (Ra) limits. Six machining parameters and corresponding T and Ra were recorded from 74 independent experiments. After exhaustive search, three machining parameters (tool inclination angle, tool diameter and radial depth of cut) for Ra and two machining parameters (feed rate and radial depth of cut) for T are found to be highly influential. In terms of these corresponding parameters, two ANFIS models are developed for the prediction of Ra and T, respectively. These models are utilized to find the optimum values of machining parameters. Five advanced metaheuristic algorithms, Artificial Bee Colony (ABC), quick artificial bee colony, modified differential evolution, ant colony optimization for real numbers and simulated annealing, with or without local search, are applied for solving this optimization problem. Each of the algorithms is run for 30 times, allowing 100,000 number of function evaluations in each run. Statistical analysis (F-test, t-test) are done to evaluate the performance of the algorithms. Hybrid ABC with local search is proposed as the best algorithm for solving this problem based on average of minimum machining time obtained. The proposed optimization approach can be used for parameter selection in real time machining with artificially intelligent Computer Numerical Control (CNC) machine tools.