Optimization of Multi-Pass Face Milling Parameters Using Metaheuristic Algorithms

Abstract

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives.

Teaching-Learning-Based Parametric Optimization of an Electrical Discharge Machining Process

“…The knowledge acquired by the students from the teacher is known as the teacher's phase. On the other hand, enrichment in knowledge through mutual interactions among the students is known as the student's phase [24].…”

An Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network

“…4. 126 sequence values corresponding to node vector U, such as 10,11,12,13,14,15,17,18,19,20,21,23,24,25,26,27,29,30,31,32,33,35, were selected as training samples. The corresponding node vector U of sequences 16,22,28,34,40,46,52,58,64,70,76,82,88,94,100,106,112,118,124,130,136,142 were selected as the test sample.…”

“…In recent years, neural network has been applied in various fields, and it's growing very rapidly [10], [11]. Six metaheuristic algorithms were applied to parametric optimization of multi-path milling process [12], [13]. However, In the application of the NURBS surface of the reverse engineering, no researcher has studied before.…”

Experimental Analysis and Optimization of the Controllable Parameters in Turning of en Aw-2011 Alloy; Dry Machining and Alternative Cooling Techniques

“…Among various optimization methods are grey relational analysis (GRA), technique for order preferences by similarity to ideal solution (TOPSIS), genetic algorithms (GA), desirability analysis (DA), metaheuristic algorithms and other methods that allow multiple performance characteristics to be optimized simultaneously. Diyaley and Chakraborty [18] used six most popular metaheuristic algorithms to determine optimal values of the cutting parameters during roughing and finishing milling operations in order to minimize total production time and total production cost. Gopal and Prakash [19] used GRA and TOPSIS to optimize milling parameters in order to minimize cutting force, surface roughness and temperature during end milling process, and both methods gave the similar optimal cutting parameters.…”