Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

Abstract

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 (R a ) limits. Six machining parameters and corresponding T and R a were recorded from 74 independent experiments. After exhaustive search, three machining parameters (tool inclination angle, tool diameter and radial depth of cut) for R a 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 R a 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.

Adjusting the Stiffness of Supports during Milling of a Large-Size Workpiece Using the Salp Swarm Algorithm

“…They are usually fast converging and can bypass local optima. Many are already applied in mechanical engineering problems [ 51 ], including milling [ 52 , 53 ] and turning operations [ 54 , 55 ].…”

Numerical Modeling and Analysis of Ti6Al4V Alloy Chip for Biomedical Applications

“…Likewise, Lu et al (2018) [ 54 ] applied RSM to evaluate the milling induced surface roughness. Shahriar et al (2017) [ 55 ] implemented optimization schemes to determine the optimum milling parameters and simulated annealing, using the artificial bee colony and ant colony optimization algorithms. Saleem et al (2017) [ 56 ] developed a numerical model in Abaqus and performed a parametric sensitivity analysis.…”

Numerical modeling and simulation of macro- to microscale chip considering size effect for optimum milling characteristics of AA2024T351

“…Waqas et al [37] worked out the optimum cutting parameters of AA2014 by employing artificial neural network (ANN) scheme. Shahriar et al [38] developed metaheuristic models to determine the optimum milling parameters. They adopted the optimization schemes, such as artificial bee colony, quick artificial bee colony, modified differential evolution, ant colony optimization, and simulated annealing technique, to solve the parametric sensitivity optimization problem.…”