Selection of cutting parameters in machining operations is an essential task to reduce cost of the products and increase quality. This paper presents an optimization approach based on artificial bee colony algorithm for optimal selection of cutting parameters in multi-pass turning operations. The objective is to find the optimized cutting parameters in the turning operations. A comparison of evolutionary-based optimization techniques to solve multi-pass turning optimization problems is presented. The results of the proposed approach for the case studies are compared with previously published results by using other optimization techniques in the literature.
Turning is one of the conventional and widely used machining methods for material removal in manufacturing industry. Optimization of cutting parameters in machining processes is very important to produce high quality products and reduce the product costs.
The literature survey shows that several research works [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14] and [15] have been made for optimization of cutting parameters in turning operations. Population-based optimization techniques such as cuckoo search algorithm, differential evolution algorithm (DE), particle swarm optimization algorithm (PSO) and genetic algorithm (GA) are becoming more popular in design and manufacturing tasks because of the availability and affordability of high-speed computers [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35] and [36]. Recently, a comparison of population-based optimization techniques for solving multi-pass turning optimization problems is presented by Yildiz [15]. Although some improvements regarding optimization of cutting parameters in multi-pass turning operations have been achieved, due to the complexity of machine parameters with conflicting objective and constraints, it still presents a matter of investigation.
In this research, a hybrid optimization approach entitled hybrid artificial bee colony algorithm (HABC) based on the ABC and Taguchi method is introduced and applied to the two case studies to optimize cutting parameters in multi-pass turning operations. It is inferred from the results that HABC is better than the previous works in terms of accuracy and precision. The number of generations for HABC is very much less compared with the previous works making it faster. The selection of cutting parameters for the two case studies has been carried out by using the HABC.
The rest of the paper is organized as follows: the Section 2 describes a detailed formulation of the objective and constraints in multi-pass turning. The standard ABC and Taguchi method are presented in Section 3. In Section 4 two case studies are solved. The results and discussions for case studies are given in Section 4. The paper is concluded in Section 5.
In this paper, a hybrid artificial bee colony algorithm based approach (HABC) has been studied for the optimization of cutting parameters in the multi-pass turning operations. Significant improvement is obtained with the HABC in comparison to the previous research works. The results show that the HABC is highly competitive to other recently published algorithms for solving the multi-pass turning optimization problems. It is also observed that the HABC can be applied to similar machining operations as well as other nonlinear optimization problems.
The future work is to improve the HABC by hybridizing local search algorithms and enhance the convergence ability of the algorithm.
