APEM jowatal Advances in Production Engineering & Management Volume 9 | Number 2 | June 2014 | pp 95-103 http://dx.doi.Org/10.14743/apem2014.2.179 ISSN 1854-6250 Journal home: apem-journal.org Original scientific paper Hybrid Taguchi method for optimizing flux cored arc weld parameters for mild steel Satheesh, M.a, Edwin Raja Dhas, J.b* department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, India department of Automobile Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, India A B S T R A C T A R T I C L E I N F O Flux cored arc welding has been applied in manufacturing industries for more than fifteen years. The quality of weld mainly depends on the mechanical properties of the weld, which in turn relays on the interaction of the weld parameters. This paper discusses the multi response optimization of weld parameters using grey based Taguchi method. Grey relational analysis was carried out to convert multi objective criterion into equivalent single objective function; overall grey relational grade, which is optimized by the Taguchi technique. Experiments are conducted using Taguchi's L27 orthogonal array. The weld parameters used in this study were welding current, welding speed, and arc voltage with bead hardness and material deposition rate as responses. Taguchi's Signal-to-Noise (S/N) ratio is computed based on their performance characteristics. Grey relational grade was obtained using Signal-to-Noise ratio values of responses. Based on the grey relational grade, optimum levels of parameters have been identified. Significant contributions were estimated using Analysis of Variance (ANOVA). A confirmation test was conducted to validate the proposed method. This evaluation procedure could be used in decision-making to select process parameters for a welding operator. The proposed and developed method has good accuracy and competency with the predicted value enhancing automation and robotization. © 2014 PEI, University of Maribor. All rights reserved. Keywords: Flux cored arc welding Optimization Process parameters Grey based Taguchi method Orthogonal array *Corresponding author: edwinrajadhas@rediffmail.com (Edwin Raja Dhas, J.) Article history: Received 23 August 2013 Revised 24 May 2014 Accepted 2 June 2014 1. Introduction Generally, the quality of a weld joint is directly influenced by the welding input parameters during the welding process; therefore, welding can be considered as a multi-input multi-output process. Unfortunately, a common problem that has faced the manufacturer is the control of the process input parameters to obtain a good welded joint with the required bead geometry and weld quality with maximum deposition rate. Weld deposition rate is the weight (in kg) of weld metal deposited per unit of arc-on-time (usually one hour). The weight deposited is less than the weight of the filler metal used, because of various losses. The ratio of the weight of metal deposited in the weld to that of filler metal employed, expressed in percent, is called the deposition efficiency. Flux cored arc welding process is a fully automated process, in which the welding electrode is a tubular wire that is continuously fed to the weld area [1]. The flux materials are in the core of the tube. The outer shell of the tube conducts the electricity that forms the arc and then becomes the filler metal as it is consumed. Recent studies indicate that FCAW has a number of advantages over the common welding techniques available that use solid wires such as manual metal arc welding and gas metal arc welding [2]. Using FCAW in any repair technique can pro- 95 Satheesh, Edwin Raja Dhas vide better control over current and heat input that is necessary to carry out the temper bead repair. As a fully automatic process, FCAW should also have cost advantages over other commonly used processes. Flux cored arc welding is considered a high deposition rate welding process that adds the benefits of flux to the simplicity of metal inert gas welding [3]. Many research attempts have been made by researchers to establish flux cored arc welding process. Mathematical modeling [4], particle swarm optimization algorithm [5], simulated annealing algorithm [6], memetic algorithm [7], Taguchi method [8], were used to optimize the parameters of flux cored arc welding process. Traditional Taguchi method can optimize a single objective function whereas it cannot solve multi objective function [9]. This paper explores the development of grey based Taguchi method for multi response optimization of flux cored arc weld parameters. 2. Grey based Taguchi method To resolve the problems subjected to multiple quality characteristics, a decision maker should rely on the subjective experiences of engineers to attain a compromise. As a result, uncertainty will be increased during the process. Hence some researchers have concentrated on achieving multiple quality characteristic at a time as a function of different appropriate level of input parameter settings. Orthogonal array with principle component analysis and Taguchi method and response surface methodology applied [10, 11] to optimize multiple quality characteristics during laser cutting of different thin sheets. Fuzzy based desirability function is used to optimize parameters of weld [12]. Grey relational analysis aims to fulfil the crucial mathematical criteria for multiple quality characteristic problems [13]. It avoids the inherent shortcomings of conventional, statistical methods and requires a limited data to estimate the behavior of an uncertain system. It provides an efficient solution to the uncertain, multi-input and discrete data problem. The main function of Grey relational analysis is to indicate the relational degree between two sequences by using discrete measurement method the distance. It can be effectively recommended as a method for optimizing the complicated interrelationships among multiple performance characteristics. 2.1 Taguchi method The quality engineering methods of Taguchi, employing design of experiments provide an efficient and systematic way to optimize designs for performance, quality and cost It is one of the most important statistical tools for designing high quality systems at reduced cost [14, 15]. The use of Taguchi method simplifies the optimization procedure for determining the optimal welding parameters in the FCAW process. The Taguchi method is performed to reduce the sources of variation on the quality characteristics of product, and reach a target of process robustness. The control factors that may contribute to reduce variation (improved quality) can be quickly identified by looking at the amount of variation present as a response. Taguchi recommends the use of the loss function to measure the performance characteristic deviating from the desired value. The value of the loss function is then transformed into an S/N ratio. Usually, there are three categories [16] of performance characteristic in the analysis of the S/N ratio, i.e. lower-the-better, higher-the-better, and nominal-the-best. The deposition rate and hardness are the higher the better performance characteristic. The S/N ratio nij for the ith performance characteristic in the jth experiment can be expressed as Eq. 1. The loss function Lj for higher-the-better performance characteristic is expressed in Eq. 2: Vu = -10lOg(iy) (1) n (2) 96 Advances in Production Engineering & Management 9(2) 2014 Hybrid Taguchi method for optimizing flux cored arc weld parameters for mild steel where n is the number of replication, k is the number of tests, yjk is experimental value of the ith performance characteristic in the jth experiment at the kth tests. For lower-the-better performance characteristic, Lij is expressed in Eq. 3. n k=1 For nominal-is-best performance characteristics, the S/N ratio is expressed in Eq. 4. ^■ = 10logg) (4) The S/N ratio for each level of process parameters is computed based on the S/N analysis. This S/N ratio value can be considered for the optimization of single response problems. However, optimization of multiple performance characteristics cannot be straightforward as in the optimization of a single performance characteristic. 2.2 Grey relational analysis The grey relational analysis is based on the grey system theory used to solve complicated interrelationship multiple performance characteristics problems effectively. A grey system has a level of information between black and white. Black represents having no information and white represents having all information. Grey based Taguchi method is successfully applied to optimize film coating process [17], drilling process [18], plasma arc weld parameters [19], bead geometry in SAW process, and wire electrical discharge machining process [20, 21]. Depending upon the characteristics of a data sequence, there are various methodologies of datapre-processingavailablefor this analysis. Experimentaldatayij isnormalized as Zj (0 < Zj < 1) for the ith performance characteristics in the jth experiment is expressed as: For S/N ratio with larger-the-better: __yij-min(yij,i = U, ...,n)_ ¿ij ~ ( \ ■ ( - il A (5) max {yij,i = 1,2, ...,n) - min(yij,i = 1,2, ...,n) For S/N ratio with smaller-the-better: max{ytj,i = 1,2, ...,n) max{ytj,i = 1,2, ...,n) — m.in(yij,i = 1,2, ...,n) ^ = __..,.. .. ^ ...,.,.. .. ^ (6) For S/N ratio with nominal-the-best: z __{yg -Target)-rnm(|y0- -Target], i = 1,2, ...,n)__(7) 11 max(jyij — Target], i = 1,2, ...,n) — min^y^ — Target], i = 1,2, ...,n) Then, the grey relational coefficients are calculated to express the relationship between the ideal and the actual experimental results. The grey relational co-efficient is expressed in Eq. 8: Amin +