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The purpose of this paper is to propose a simple methodology in solving multi‐response optimisation problems by employing Taguchi methods and a non‐parametric statistical technique.

There is a continuous interest in developing effective and statistically sound multi‐response optimisation methods such that they will provide a firm framework in global product and process improvement. A non‐parametric approach is proposed for the first time in a five‐step methodology that exploits Taguchi's fractional factorial designs and the concept of signal‐to‐noise ratio in data consolidation. The distinct feature of this method is the transformation of each response variable to a single rank variable. The subsequent incorporation of the squared ranks for each of the investigated responses issues a single master‐rank response suitably referred to conveniently as a “Super Rank” (SR) response, thus collapsing all dependent product characteristic information into a single non‐dimensional variable. This SR variable is handled by standard non‐parametric methods such as Wilcoxon's two‐sample, rank sum test or Mann‐Whitney's test eliminating at the same time multi‐distribution effects and small‐sample complications expected for this type of experimentation.

The proposed methodology is tested on already published data pertaining a design problem in the electronic assembly technology field. The case study requires six‐factor simultaneous optimisation of three response variables. A second example is analyzed by the proposed method focusing on the optimisation of a submerged arc‐welding process problem due to a group of five factors. The Mann‐Whitney's test contrasts the effects of factor settings one‐to‐one on the SR response in order to assign statistical significance to the optimal factor settings.

The application of this methodology is tested at the same time in a real three‐response optimisation case study where each response belongs to different optimisation category.

The methodology outlined in this work eliminates the need for sophisticated multi‐response data handling. In addition, small‐sample considerations and multi‐distribution effects that may be inherent do not restrict the applicability of the method presented herein by this type of experimentation.

This investigation provides a new angle to the published methods of multi‐response optimisation by supporting Taguchi's design of experiments methods through a multi‐ranking scheme that leads to non‐parametric factor resolution.

Looks at the Taguchi method, a traditional approach that seeks to obtain the best combination of factors with the lowest societal cost solution to achieve customer requirements, and also principal component analysis (PCA). States that the Taguchi method can only be used to optimize single response problems and not multi‐response problems and that PCA, although it has been considered to solve multi‐response problems, itself has shortcomings. Proposes a data envelopment analysis ranking (DEAR) approach as an effective means of optimizing the multi‐response problem. Includes a series of steps from the proposed approach which are capable of decreasing uncertainty caused by engineering judgement in the Taguchi method and overcoming the shortcomings of PCA. Concludes that the DEAR approach is more powerful for practical applications.

The Taguchi method is the conventional approach used in off‐line quality control. However, most previous Taguchi method applications have dealt only with a single‐response problem. The multi‐response problem has received only limited attention. Proposes an effective procedure on the basis of the quality loss of each response so as to achieve the optimization on multi‐response problems in the Taguchi method. The procedure is a universal approach which can simultaneously deal with continuous and discrete data. Evaluates a plasma‐enhanced chemical vapour deposition (PECVD) process experiment and a case study, indicating that the proposed procedure yields a satisfactory result.

The purpose of this paper is to propose a manufacturing product‐screening methodology that will require minimal resource expenditures as well as succinct improvement tools based on multi‐response prioritisation.

A six‐step methodology is overviewed that relies on the sampling efficiency of fractional factorial designs introduced and recommended by Dr G. Taguchi. Moreover, the multi‐response optimisation approach based on the super‐ranking concept is expanded to the more pragmatic situation where prioritising of the implicated responses is imperative. Theoretical developments address the on‐going research issue of saturated and unreplicated fractional‐factorial designs. The methodology promotes the “user‐friendly” incorporation of assigned preference weights on the studied responses. Test efficiency is improved by concise rank ordering. This technique is accomplished by adopting the powerful rank‐sum inference method of Wilcoxon‐Mann‐Whitney.

Two real‐life case studies complement the proposed technique. The first discusses a production problem on manufacturing disposable shavers. Injection moulding data for factors such as handle weight, two associated critical handle dimensions and a single mechanical property undergo preferential multi‐response improvement based on working specification standards. This case shows that regardless of fluctuations incurred by four different sources of response prioritisation, only injection speed endures high‐statistical significance for all four cases out of the seven considered production factors. Similarly, the technique identifies a single active factor in a foil manufacturing optimisation of three traits among seven examined effects.

This investigation suggests a technique that targets the needs of manufacturing managers and engineers for “quick‐and‐robust” decision making in preferential product improvement. This is achieved by conjoining orthogonal arrays with a well‐established non‐parametric comparison test. A version of the super‐ranking concept is adapted for the weighted multi‐response optimisation case.

The aim of this paper is to examine product formulation screening at the industrial level in terms of multi‐trait improvement by considering several pertinent controlling factors.

The study adopts Taguchi's orthogonal arrays (OAs) for sufficient and economical sampling in a mixture problem. Robustness of testing data is instilled in this method by employing a two‐stage analysis where controlling components are investigated together while the slack variable is tested independently. Multi‐responses collapse to a single master response has been incurred according to the Super Ranking concept. Order statistics are employed to provide statistical significance. The slack variable influence is tested by regression and nonparametric correlation.

Synergy among Taguchi methodology, super ranking and nonparametric testing was seamless to offer practical resolution to product component activeness. The concurrent modulation of two key product traits due to five constituents in the industrial production of muffin‐cake is invoked. The slack variable, rich cream, is strongly active while the influence of added amount of water is barely evident.

The method presented is suitable only for situations where industrial mixtures are investigated. The case study demonstrates prediction capabilities up to quadratic effects for five nominated effects. However, the statistical processor selected here may be adapted to any number of factor settings dictated by the OA sampling plan.

By using a case study from food engineering, the industrial production of a muffin‐cake is examined focusing on a total of five controlling mixture components and two responses. This demonstration emphasizes the dramatic savings in time and effort that are gained by the proposed method due to reduction of experimental effort while gaining on analysis robustness.

This work interconnects Taguchi methodology with powerful nonparametric tests of Kruskal‐Wallis for the difficult problem of non‐linear analysis of mixtures for saturated, unreplicated fractional factorial designs in search of multi‐factor activeness in multi‐response cases employing simple and practical tools.

This paper discusses multi‐response robust parameter design problems based on response surface method. Most research effort on multi‐response parameter design problem focuses much on finding out optimal parameters based on certain criteria or objectives. Research shows that optimal solution in terms of some criteria may not be robust. To achieve robust solution we should consider how sensitive the solution is when the factors change around it. A comparative study of methods for multi‐response robust parameter design is conducted. Solution with consideration of robustness and optimality is proposed with applications of the example.

The purpose of this paper is to propose a new method of robust parameter design for dynamic multi‐response system. The objectives are to resolve the correlations among multiple responses and the uncertainty of system with incomplete information.

First, desirability function is used to measure dynamic system sensitivity and system variation, and principal component analyses on the two indices are conducted. Second, the grey relational grade (GRD) between principal component sequences of the two indices and their respective ideal sequences, gained by grey relational analysis, is converted to an integrated GRD (IGRD) index by means of TOPSIS method, and then the optimal level combination of controllable factors is identified based on the IGRD index.

It was found that the optimal factor level combination obtained by the proposed method is nearest the ideal solution and farthest from the negative ideal solution. The validity and superiority of the proposed method are confirmed through two illustrative examples.

It should be noted that the proposed method fails to consider the interaction effects between controllable factors and noise factors.

The method proposed in the paper effectively integrates several common methods to optimize a dynamic multiple responses system based on Taguchi's robust parameter design. These methods do not involve complicated mathematical theory, and are therefore easy for practitioners to use in engineering practice.

Friction stir welding (FSW) is considered an environmentally sound process compared to traditional fusion welding processes. It is a complex process in which various parameters influence weld strength. Therefore, it is essential to identify the best parameter settings for achieving the desired weld quality. This paper aims to investigate the multi-response optimization of process parameters of the FSWed 6061-T6 aluminum (Al) alloy.

The input process parameters related to FSW have been sorted out from a detailed literature survey. The properties of weldments such as yield strength, ultimate tensile strength, percentage elongation and microhardness have been used to evaluate weld quality. The process parameters have been optimized using the Taguchi-based grey relational analysis (GRA) methodology. Taguchi L16 orthogonal array has been considered to design the experiments. The effect of input parameters on output responses was also determined by the analysis of variance (ANOVA) method. Finally, to corroborate the results, a confirmatory experiment was carried out using the optimized parameters from the study.

The ANOVA result indicates that the tool rotation speed was the most significant parameter followed by tool pin profile and welding speed. From the confirmation test, it was observed that the optimum FSW process parameters predicted by the Taguchi method improved the grey relational grade by 13.52%. The experimental result also revealed that the Taguchi-based GRA method is feasible in finding solutions to multi-response optimization problems in the FSW process.

The present study is unique in the multi-response optimization of FSWed 6061-T6 Al alloy using the Taguchi and GRA methodology. The weld material having better mechanical properties is essential for the material industry.

Since, wear is the one of the most commonly encountered industrial problems leading to frequent replacement of components there is a need to develop metal matrix composites (MMCs) for achieving better wear properties. The purpose of this paper is to fabricate aluminum MMCs to improve the dry sliding wear characteristics. An effective multi-response optimization approach called the principal component analysis (PCA) was used to identify the sets of optimal parameters in dry sliding wear process.

The present work investigates the dry sliding wear behavior of graphite reinforced aluminum composites produced by the molten metal mixing method by means of a pin-on-disc type wear set up. Dry sliding wear tests were carried on graphite reinforced MMCs and its matrix alloy sliding against a steel counter face. Different contact stress, reinforcement percentage, sliding distance and sliding velocity were selected as the control variables and the response selected was wear volume loss (WVL) and coefficient of friction (COF) to evaluate the dry sliding performance. An L25 orthogonal array was employed for the experimental design. Optimization of dry sliding performance of the graphite reinforced MMCs was performed using PCA.

Based on the PCA, the optimum level parameters for overall principal component (PC) of WVL and COF have been identified. Moreover, analysis of variance was performed to know the impact of individual factors on overall PC of WVL and COF. The results indicated that the reinforcement percentage was found to be most effective factor among the other control parameters on dry sliding wear followed by sliding distance, sliding velocity and contact stress. Finally the wear surface morphology of the composites has been investigated using scanning electron microscopy.

Various manufacturing techniques are available for processing of MMCs. Each technique has its own advantages and disadvantages. In particular, some techniques are significantly expensive compared to others. Generally the manufacturer prefers the low cost technique. Therefore stir casting technique which was used in this paper for manufacturing of Aluminum MMCs is the best alternative for processing of MMCs in the present commercial sectors. Since the most important criteria of a dry sliding wear behavior is to provide lower WVL and COF, this study has intended to prove the application of PCA technique for solving multi objective optimization problem in wear applications like piston rings, piston rods, cylinder heads and brake rotors, etc.

Application of multi-response optimization technique for evaluation of tribological characteristics for Aluminum MMCs made up of graphite particulates is a first-of-its-kind approach in literature. Hence PCA method can be successfully used for multi-response optimization of dry sliding wear process.

This paper aims to examine an investigation of high-pressure coolant (HPC) drilling process with regard to experimental models of output parameters, effect of input parameters on output parameters and simultaneous optimization of the output parameters.

Experimental plan was designed using response surface method and experiments were conducted on HPC drilling set up. Measurements for output parameters were carried out and mathematical models were obtained. Multi response optimization using a composite desirability function approach was used to obtain optimum values of input parameters for simultaneous optimization of output parameters.

Optimal value of input parameters for optimization of HPC drilling process were obtained as; coolant pressure: 21 bar, spindle speed: 3,970 rpm, feed rate: 0.084 mm/rev and peck depth: 5.50 mm. The composite desirability obtained is 0.9412, which indicates that the performance of HPC drilling process was significantly optimized. Developed mathematical models of the output parameters accurately represent the entire design space under investigation.

This is the first study that involves variation of higher coolant pressure and investigation of HPC drilling process using response surface methodology and multi response optimization technique with desirability function.