Estimation of optimal machining control parameters using artificial bee colony

Abstract

Modern machining processes such as abrasive waterjet (AWJ) are widely used in manufacturing industries nowadays. Optimizing the machining control parameters are essential in order to provide a better quality and economics machining. It was reported by previous researches that artificial bee colony (ABC) algorithm has less computation time requirement and offered optimal solution due to its excellent global and local search capability compared to the other optimization soft computing techniques. This research employed ABC algorithm to optimize the machining control parameters that lead to a minimum surface roughness (R a ) value for AWJ machining. Five machining control parameters that are optimized using ABC algorithm include traverse speed (V), waterjet pressure (P), standoff distance (h), abrasive grit size (d) and abrasive flow rate (m). From the experimental results, the performance of ABC was much superior where the estimated minimum R a value was 28, 42, 45, 2 and 0.9 % lower compared to actual machining, regression, artificial neural network (ANN), genetic algorithm (GA) and simulated annealing (SA) respectively. IntroductionThe manufacturing industries nowadays face many challenges such as market competition, expensive machining cost, customer high request and complexity of the product. For manufacturers, the main objective is to produce high quality of product with less cost and time constraints. Today, modern machining processes are widely used in manufacturing industries because it has some advantages (for example in terms of cost) compared to traditional machining processes (Ridwan et al. 2012; Mokhtar and Xu 2011; Zain et al. 2012a). According to Nagendra Parashar and Mittal 2007, traditional machining processes are costly and inefficient because it is incapable to machine the materials cost-effectively because of the tools is harder than the workpiece. The alteration or new traditional machining methods are also needed because in several cases, the methods might not be operated. Roy and Mehnen (2008) suggest that new method need to be developed in order to guarantee fast, safe and cost efficient production. The modern machining process can be categorized into four types which are (i) mechanical (e.g. abrasive waterjet (AWJ), ultrasonic machining (USM)), (ii) chemical (e.g. chemical machining (CHM)), (iii) electrochemical (e.g. electrochemical machining (ECM), electrochemical grinding (ECG)) and (iv) thermoelectric (e.g. electrobeam machining (EBM), laserbeam machining (LBM)).AWJ machining was considered in this research to compute a minimum R a value. (Zain et al. 2012b) AWJ used a high powerful flow of water in order to cut the workpiece. The high pressure of water (usually more than 900 mph) enables it to cut metal, non-metal, composite and heat sensitive workpiece. The advantage of AWJ is that it never gets dry 123

Optimization of machining processes using pattern search algorithm

“…It can be observed from Table 1 that the PS algorithm gives better results than the genetic algorithm (GA) and simulated annealing (SA) obtained previously by (Zain et al, 2011). The optimization results are comparable with the results of ABC algorithm as previously reported by Yusup et al (2013). The optimization results indicate that PS algorithm, starting from three different initial points, successfully avoided the local minimum entrapment problem.…”

“…(1). The obtained optimization solutions and optimization solutions obtained by past researches using metaheuristic algorithms (Zain et al, 2011;Yusup et al, 2013) are given in Table 1. It can be observed from Table 1 that the PS algorithm gives better results than the genetic algorithm (GA) and simulated annealing (SA) obtained previously by (Zain et al, 2011).…”

“…In the past decade, the new trend in the optimization of the machining processes has been based on the use of meta-heuristic algorithms (Zain et al, 2011;Yusup et al, 2013;Bhushan et al, 2012;Savas & Ozay, 2008;Kilickap et al, 2011;Maji & Pratihar, 2011;Rao & Pawar, 2009;Rao & Pawar, 2010;Rao, 2011;Rao & Kalyankar, 2013;Goswami & Chakraborty, 2014). It has been widely reported that these algorithms have the possibility to deal with discontinuous, non-differentiable, and multidimensional machining process models.…”

Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

“…Metaheuristics are also applied in some nonconventional machining processes. Yusup, et al [28] applied ABC, GA and SA algorithms for optimizing the control parameters of Abrasive Waterjet (AWJ) machining that leads to a minimum surface roughness. They have shown that performance of ABC was much superior.…”

A restructured artificial bee colony optimizer combining life-cycle, local search and crossover operations for droplet property prediction in printable electronics fabrication

“…The application of evolutionary algorithm (EA) and swarm intelligence (SI) for solving complex optimization problems in engineering has gained significant attention in the literature (Sheikhalishahi et al 2014;Yusup et al 2014;Olivera et al 2015;Kahourzade et al 2015). SI is a newer paradigm than EA of artificial intelligence system whereby small entities working together to produce an intelligent behavior from simple rules (Kennedy and Eberhart 1995;Dorigo and Gambardella 1997;Passino 2002;Clerc and Kennedy 2002;Sumathi et al 2008;Hansen and Ostermeier 2001;Karaboga 2005).…”