Solving multi-criteria optimization problem in submerged arc welding consuming a mixture of fresh flux and fused slag

In the present work, application of the Taguchi method in combination with grey relational analysis has been applied for solving multiple criteria (objective) optimization problem in submerged arc welding (SAW). A grey relational grade evaluated with grey relational analysis has been adopted to reveal an optimal parameter combination in order to obtain acceptable features of weld quality characteristics in submerged arc bead-on-plate welding. The idea of slag utilization, in subsequent runs, after mixing it with fresh unmelted flux, has been introduced. The parentage of slag in the mixture of fresh flux and fused flux (slag) has been denoted as slag-mix%. Apart from two conventional process parameters: welding current and flux basicity index, the study aimed at using varying percentages of slag-mix, treated as another process variable, to show the extent of acceptability of using slag-mix in conventional SAW processes, without sacrificing any characteristic features of weld bead geometry and HAZ, within the experimental domain. The quality characteristics associated with bead geometry and HAZ were bead width, reinforcement, depth of penetration and HAZ width. Using grey relational grade as performance index, we have performed parametric optimization yielding the desired features of bead geometry and HAZ. Predicted results have been verified with confirmatory experiments, showing good agreement. This proves the utility of the proposed method for quality improvement in SAW process and provides the maximum (optimum) amount of slag-mix that can be consumed in the SAW process without any negative effect on characteristic features of the quality of the weldment in terms of bead geometry.     

Application of Taguchi philosophy for parametric optimization of bead geometry and HAZ width in submerged arc welding using a mixture of fresh flux and fused flux

Taguchi philosophy has been applied for obtaining optimal parametric combinations to achieve desired weld bead geometry and dimensions related to the heat-affected zone (HAZ), such as HAZ width in the present case, in submerged arc welding. The philosophy and methodology proposed by Dr. Genichi Taguchi can be used for continuous improvement in products that are produced by submerged arc welding. This approach highlights the causes of poor quality, which can be eliminated by self-adjustment among the values of the process variables if they tend to change during the process. Depending on functional requirements of the welded joint, an acceptable weldment should confirm maximum penetration, minimum reinforcement, minimum bead width, minimum HAZ width, minimum bead volume, etc. to suit its area of application. Hence, there exists an increasing demand to evaluate an optimal parameter setting that would fetch the desired yield. This could be achieved by optimization of welding variables. Based on Taguchi’s approach, the present study has been aimed at integrating statistical techniques into the engineering process. Taguchi’s L9 (3**3) orthogonal array design has been adopted and experiments have been accordingly conducted with three different levels of conventional process parameters using welding current and flux basicity index to obtain bead-on-plate weld on mild steel plates. Features of bead geometry and HAZ in terms of bead width, reinforcement, depth of penetration and HAZ width have been measured for each experimental run. The slag, generated during welding, has been consumed in further runs by mixing it with fresh unmelted flux. The percentage of slag in the mixture of fused flux (slag) and fresh flux has been defined as slag-mix%. Welding has been performed by using varying slag-mix%, treated as another process variable, in order to obtain the optimum amount of slag-mix that can be used without any alarming adverse effect on features of bead geometry and HAZ. This would lead to ‘waste to wealth’.     

Modeling and optimization of features of bead geometry including percentage dilution in submerged arc welding using mixture of fresh flux and fused slag

Quality has now become an important issue in today’s manufacturing world. Whenever a product is capable of conforming to desirable characteristics that suit its area of application, it is termed as high quality. Therefore, every manufacturing process has to be designed in such a way that the outcome would result in a high quality product. The selection of the manufacturing conditions to yield the highest desirability can be determined through process optimization. Therefore, there exists an increasing need to search for the optimal conditions that would fetch the desired yield. In the present work, we aim to evaluate an optimal parameter combination to obtain acceptable quality characteristics of bead geometry in submerged arc bead-on-plate weldment on mild steel plates. The SAW process has been designed to consume fused flux/slag, in the mixture of fresh flux. Thus, the work tries to utilize the concept of ‘waste to wealth’. Apart from process optimization, the work has been initiated to develop mathematical models to show different bead geometry parameters, as a function of process variables. Hence, optimization has been performed to determine the maximum amount of slag--flux mixture that can be used without sacrificing any negative effect on bead geometry, compared to the conventional SAW process, which consumes fresh flux only. Experiments have been conducted using welding current, slag-mix percentage and flux basicity index as process parameters, varied at four different levels. Using four³ full factorial designs, without replication, we have carried out welding on mild steel plates to obtain bead-on-plate welds. After measuring bead width, depth of penetration and reinforcement; based on simple assumptions on the shape of bead geometry, we calculated other relevant bead geometry parameters: percentage dilution, weld penetration shape factor, weld reinforcement form factor, area of penetration, area of reinforcement and total bead cross sectional area. All these data have been utilized to develop mathematical models between predictors and responses. Response surface methodology (RSM), followed by the multiple linear regression method, has been applied to develop these models. The effects of selected process parameters on different responses have been represented graphically. Finally grey relational analysis coupled with the Taguchi method (with Taguchi’s orthogonal array) has been applied for parametric optimization of this welding technique. Confirmatory experiments have been conducted to verify optimal results.     

Multi-criteria decision making in the selection of machining parameters for Inconel 718

Taguchi’s methods and design of experiments are invariably used and adopted as quality improvement techniques in several manufacturing industries as tools for offline quality control. These methods optimize single-response processes. However, Taguchi’s method is not appropriate for optimizing a multi-response problem. In other situations, multi-responses need to be optimized simultaneously. This paper presents multi-response optimization techniques. A set of non-dominated solutions are obtained using non-sorted genetic algorithm for multi-objective functions. Multi-criteria decision making (MCDM) is proposed in this work for selecting a single solution from nondominated solutions. This paper addresses a new method of MCDM concept based on technique for order preference by similarity to ideal solution (TOPSIS). TOPSIS determines the shortest distance to the positive-ideal solution and the greatest distance from the negative-ideal solution. This work involves the high-speed machining of Inconel 718 using carbide cutting tool with six objective functions that are considered as attributes against the process variables of cutting speed, feed, and depth of cut. The higher-ranked solution is selected as the best solution for the machining of Inconel 718 in its respective environment.     

Slag recycling in submerged arc welding and its influence on weld quality leading to parametric optimization

Slag generated during conventional submerged arc welding (SAW) has been recycled by mixing varying percentages of crushed slag with fresh flux to use in subsequent runs. The influence of using flux-slag mixture on various aspects of SAW weld parameters of bead geometry have been investigated in a quantitative basis. Slag has been reprocessed and reused in submerged arc welding to produce bead-on-plate weld on mild steel plates. Apart from conventional process parameters: voltage (OCV), wire feed rate, nozzle to plate distance (stick-out) and traverse speed, welding has been carried out using various percentages of flux-slag mixture; the % of fused flux in the mixture has been treated as a process parameter. Various bead geometry parameters viz. bead width; reinforcement, depth of penetration and depth of HAZ have been measured for each of weld prepared in the study. Using experimental data, a grey-based Taguchi approach has been applied for parametric optimization of this non-conventional SAW process. The aim was to reveal the optimal amount of slag-mix%, which could be applied in SAW process without imposing any adverse effect on features of bead geometry and HAZ. Optimal result has been checked through confirmatory test.     

Methodology of Taguchi optimization for multi-objective drilling problem to minimize burr size

Formation of exit burr on part edges during drilling has several undesirable features with regard to product quality and functionality. Hence it is essential to select optimum drilling process parameters to minimize burr size at the production stage. This paper presents the application of the Taguchi optimization method for simultaneous minimization of burr height and burr thickness influenced by cutting conditions and drill geometry. The Taguchi design approach to the multi-objective optimization problem is based on the introduction of a new concept of fitness function for each trial of orthogonal array. The fitness function is derived through mapping the objective functions of the drill optimization problem. In the present work, optimal values of cutting speed, feed, point angle and lip clearance angle are determined for selected drill diameter values to minimize burr height and burr thickness during drilling of AISI 316L stainless steel workpieces. The details of experimentation, analysis of means and analysis of variance are presented in the paper.     

Using PCR-TOPSIS to optimise Taguchi's multi-response problem

The Taguchi method is an efficient method used in off-line quality control where experimental design is combined with quality loss. This method includes three stages—system design, parameter design, and tolerance design. In the real world it is obvious that more than one quality characteristic should be considered for most industrial products; i.e., in most applications the customer's concern is with multi-response problems. Nevertheless, the Taguchi method is not appropriate for optimising a multi-response problem since engineering judgment is the main optimisation procedure in Taguchi method. In order to overcome this problem, this paper proposes an effective procedure called PCR-TOPSIS that is based on process capability ratio (PCR) theory and on the theory of order preference by similarity to the ideal solution (TOPSIS) to optimise multi-response problems. Using PCR-TOPSIS, multiple responses in each experiment will be transformed into a performance index. Therefore, the optimal factors/levels combinations for the multi-responses can be determined. Two case studies in Tarng et al. and Reddy et al. are resolved using the proposed procedure. The result indicates that PCR-TOPSIS can yield a satisfactory solution for multi-response problems.     

Multi-response optimization using weighted principal component

Taguchi method is a very popular offline quality design. However, it cannot solve the multi-response problem which occurs often in today’s society. Research shows that the multi-response problem is still an issue with the Taguchi method. Researchers have tried to find a series of theories and methods in seeking a combination of factors/levels to achieve the situation of optimal multi-response instead of using engineers’ judgement to make a decision in the Taguchi method. In 1997, Su et al. submitted the multivariate method, and in 2000 Antony proposed principal component analysis (PCA), to solve this problem. But with the PCA method, there are still two main shortcomings. In this study, the weighted principal components (WPC) method is proposed to overcome these two shortcomings, and three cases in their papers will be illustrated and compared in the application of WPC method. The result shows that the WPC method offers significant improvements in quality.     

Comparison of nongradient methods with hybrid Taguchi-based epsilon constraint method for multiobjective optimization of cylindrical fin heat sink

The primary aim of the paper is to compare the different nongradient methods of multiobjective optimization for optimizing the geometry parameters of a cylindrical fin heat sink. The methods studied for comparison are Taguchi-based grey relational analysis, ε (epsilon) constraint method and genetic algorithm. The various responses that have been studied are electromagnetic emitted radiations, thermal resistance and mass of the heat sink. Since the responses are obtained using complex simulation softwares (HFSS—Ansoft for emitted radiations and CFD—Flotherm for thermal resistance), there is no way of calculating the derivates of the objective functions. Hence, the Taguchi design of experiments design is used to derive the linear regression equations for the responses studied, which are then taken as the objective functions to be optimized. A new hybrid method known as Taguchi-based epsilon constraint method has been proposed in this paper for obtaining nondominated Pareto solution set. The results obtained using the proposed method show that the Pareto optimal set is competitive in terms of diversity of the solutions obtained. It is not likely that there exists a solution, which simultaneously minimizes all the objectives using any of the multiobjective techniques implemented. The value path analysis has been done to compare the trade-off among the design alternatives for the chosen multiple objective parameter optimization problem.     

Simultaneous optimization of multi-response problems in the Taguchi method using genetic algorithm

The optimization of multiple responses (or performance characteristics) has received increasing attention over the last few years in many manufacturing organizations. Most previous applications of the Taguchi method only emphasize the single-response problems, while the multi-response problems have received relatively little attention. Many Taguchi practitioners have employed past experience and engineering knowledge or judgement when dealing with multiple responses. The approach presented in this paper takes advantage of both the Taguchi method and genetic algorithm, which forms a robust and practical methodology in tackling multiple response optimization problems. The paper also presents a case study to illustrate the potential of this powerful integrated approach for tackling multiple response optimization problems. The variance analysis is also an integral part of the study, which identifies the most critical and statistically significant parameters.     

Combined quality loss (CQL) concept in WPCA-based Taguchi philosophy for optimization of multiple surface quality characteristics of UNS C34000 brass in cylindrical grinding

The present study highlights a multi-objective optimization problem by applying Weighted Principal Component Analysis (WPCA) coupled with Taguchi method through a case study in cylindrical grinding of UNS C34000 Medium Leaded Brass. The study aimed at evaluating the best process environment which could simultaneously satisfy multiple requirements of surface quality. In view of the fact that traditional Taguchi method fails to solve a multi-objective optimization problem, to overcome this limitation, WPCA has been coupled with Taguchi method. Furthermore, to follow the basic assumption of Taguchi method, i.e., quality attributes should be uncorrelated or independent; which is not always satisfied in practical situation; the study applied WPCA to eliminate response correlation and to evaluate independent or uncorrelated quality indices called principal components which were aggregated by WPCA to compute overall quality index denoted as Multi-Response Performance Index. A combined quality loss was then estimated which was optimized (minimized) finally. The study combined WPCA and Taguchi method for predicting optimal setting. Optimal result was verified through confirmatory test. This indicates application feasibility of the aforesaid methodology proposed for multi-response optimization and off-line control of correlated multiple surface quality characteristics in cylindrical grinding.     

Multi-objective optimization of sheet metal forming die using FEA coupled with RSM

Present study describes the approach of applying response surface methodology (RSM) with a Pareto-based multi-objective genetic algorithm to assist engineers in optimization of sheet metal forming. In many studies, finite element analysis and optimization technique have been integrated to solve the optimal process parameters of sheet metal forming by transforming multi objective problem into a single-objective problem. This paper aims to minimize objective functions of fracture and wrinkle simultaneously. Design variables are blank-holding force and draw-bead geometry (length and diameter). Response surface model has been used for design of experiment and finding relationship between variables and objective functions. Forming limit diagram (FLD) has been used to define objective functions. Finite element analysis applied for simulating the process. Proposed approach has been investigated on a cross-shaped cup drawing case and it has been observed that it is more effective and accurate than traditional finite element analysis method and the ‘trial and error’ procedure.     

Multi-response optimization of machining parameters in hot turning using grey analysis

This paper envisages the multi-response optimization of machining parameters in hot turning of stainless steel (type 316) based on Taguchi technique. The workpiece heated with liquid petroleum gas flame burned with oxygen was machined under different parameters, i.e., cutting speed, feed rate, depth of cut, and workpiece temperature on a conventional lathe. The effect of cutting speed, feed rate, depth of cut, and workpiece temperature on surface roughness, tool life, and metal removal rate have been optimized by conducting multi-response analysis. From the grey analysis, a grey relational grade is obtained and based on this value an optimum level of cutting parameters has been identified. Furthermore, using analysis of variance method, significant contributions of process parameters have been determined. Experimental results reveal that feed rate and cutting speed are the dominant variables on multiple performance analysis and can be further improved by the hot turning process.     

Parameters optimization and objective trend analysis for fiber laser keyhole welding based on Taguchi-FEA

This paper proposes an integrated method for process parameters optimization and objective analysis in the fiber laser keyhole welding based on Taguchi and finite element method (FEA). The Taguchi-FEA framework is established and applied for the objective of increasing the ratio of weld penetration to width (P/W) in the welded joints. Numerical simulations are incorporated into identifying the desired responses and the process parameters effects on the objective without consuming time, materials, and labor effort. To validate the effectiveness of the integration methodology, the fiber laser keyhole welding of the hot-dip galvanized dual phase sheet (GA-DP590) has been carried out in this paper. The three process parameters, laser power (LP), welding speed (WS), and focal position (FP), have been taken into consideration during the optimization process. The optimized results are confirmed, and trend of the objective variation near the optimal process parameters is analyzed by the numerical simulation. The corresponding microstructure, phase transformation and microhardness variation of the optimized weld bead are also calculated. The results demonstrate that the proposed method is reliable and effective for improving the quality of welded joints in the practical production level.     

Multi-response optimization of low-pressure cold-sprayed coatings through Taguchi method and utility concept

Cold spray process is a relatively new coating deposition thermal spray process, and a lot of research is being carried out throughout the world towards the optimization of the process with an aim towards the performance improvement of the process. For optimization of process parameters, most of the existing approaches for multi-response optimization of process parameters focus upon the subjective and practical knowledge available about the process. Keeping in view these limitations, an approach based on a utility theory and Taguchi quality loss function has been applied to low-pressure cold spray process to deposit copper coatings, for simultaneous optimization of more than one response characteristics. In the present paper, three potential response parameters, i.e., coating thickness, coating density, and surface roughness have been selected. Utility values based upon these response parameters have been analyzed for optimization by using Taguchi approach.     

Comparison of performances of five prospective approaches for the multi-response optimization

The Taguchi method of experimental design is widely used for optimization of process performance. However, this method has been developed to optimize single-response processes. But, in many situations, the engineers are required to determine the process settings that can simultaneously optimize multiple responses. In the recent past, researchers have proposed several systematic procedures for multi-response optimization. Most of these methods use complicated statistical/mathematical models and are, therefore, not easily comprehendible to the engineers who do not have a strong background in mathematics. Only a few methods, e.g. weighted signal-to-noise (WSN) ratio, Grey relational analysis, multiple-response signal-to-noise ratio, VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian), and weighted principal component methods, use relatively simpler procedures. In this paper, the computational procedures for these five methods are standardized. Three sets of experimental data are analyzed using these standardized procedures and the predicted optimization performances of the five methods are compared. The results show that no method can give better optimization than the WSN method.     

Multi-response optimisation of WEDM process using principal component analysis

Like many other processes, the wire electrical discharge machining (WEDM) process has several performance characteristics. Determination of the optimal process settings with respect to all these performance measures (responses) is an important issue. Taguchi’s robust design method can only be applied to optimise a single-response problem. Some researchers have attempted to optimise WEDM operations using a multi-response signal-to-noise (MRSN) ratio and constraint optimisation methods. Both these methods suffer from some weaknesses. The principal component analysis (PCA)-based approach for multi-response optimisation can effectively overcome those weaknesses. In this paper, some modifications in the PCA-based approach are suggested and two sets of experimental data published by the past researchers are analysed using this modified procedure. It is observed that the PCA-based optimisation can give better results than the constrained optimisation and MRSN ratio-based methods, which can be attributed to the fact that the possible correlation among the multiple responses is taken care in the PCA-based approach.     

The principal component analysis (PCA)-based approaches for multi-response optimization: some areas of concerns

Over the years, Taguchi method for process optimisation has become very popular among the engineers. However, Taguchi method focuses on the optimisation of a single-response variable only, whereas most of the modern manufacturing processes demand for simultaneous optimisation of multiple response variables, and some of these responses are often correlated. Several methods have been proposed in literature which aims at making the Taguchi method useful for solving multi-response optimisation problems too. However, only few of these methods take into account the possible correlations that may exist among the response variables. Among these, principal component analysis (PCA)-based approaches are quite popular among the practitioners. However, we find that the PCA-based approaches suffer from some weaknesses, e.g. problem due to using signal-to-noise ratios as input data, problem due to scaling of the input data, problem due to difference in PCA results given by different software. This article aims at drawing attention of the researchers/practitioners to these problem areas of the PCA-based approaches so that appropriate research initiatives can be taken up by the researchers/practitioners to overcome those weaknesses.     

Optimization and prediction of weldment profile in bead-on-plate welding of Al-1100 plates using electron beam

Bead-on-plate welds were carried out on aluminum plates Al-1100 using an electron beam welding machine. The weld runs were conducted as per central composite design. Regression analysis was then carried out to establish input–output relationships of the process. The weldment area was minimized, after satisfying the condition of maximum bead penetration. The above constrained optimization problem was solved utilizing a genetic algorithm (GA) with a penalty function approach. The GA was able to determine optimal weld-bead geometry and recommend the necessary process parameters for the same. An attempt was also made to model the complicated dagger-like profile of electron-beam welded material by utilizing three third-order curves. The profiles were predicted by utilizing both back-propagation trained and GA-tuned neural networks. The latter was able to yield better predictions compared to the former.     

A study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces

The objective of this paper is to develop a Taguchi optimization method for low surface roughness in terms of process parameters when milling the mold surfaces of 7075-T6 aluminum material. Considering the process parameters of feed, cutting speed, axial-radial depth of cut, and machining tolerance, a series of milling experiments were performed to measure the roughness data. A regression analysis was applied to determine the fitness of data used in the Taguchi optimization method using milling experiments based on a full factorial design. Taguchi orthogonal arrays, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are used to find the optimal levels and the effect of the process parameters on surface roughness. A confirmation experiment with the optimal levels of process parameters was carried out in order to demonstrate the effectiveness of the Taguchi method. It can be concluded that Taguchi method is very suitable in solving the surface quality problem of mold surfaces.