Optimizing multi‐response problem in the Taguchi method by DEA based ranking method

Liao, Hung‐Chang; Chen, Yan‐Kwang

doi:10.1108/02656710210434766

Cited by 54 publications

(47 citation statements)

References 9 publications

(11 reference statements)

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“…The PCA does absolutely nothing when the responses are uncorrelated and hence the best ISRN Industrial Engineering 7 results are obtained when the responses or quality characteristics are highly correlated, positively or negatively [55]. The application of PCA includes a series of steps capable of solving the weakness of the standalone Taguchi method which requires engineering judgement to deal with multiple quality characteristics because an engineer's judgement increases the uncertainty during the decision-making process [56].…”

Section: Integration Of Taguchi Methods With Principal Component Analymentioning

confidence: 99%

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

Fei

Mehat²,

Kamaruddin

2013

ISRN Industrial Engineering

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Determining the optimal processing parameter is routinely performed in the plastic injection moulding industry as it has a direct and dramatic influence on product quality and costs. In this volatile and fiercely competitive market, traditional trial-and-error is no longer sufficient to meet the challenges of globalization. This paper aims to review the research of the practical use of Taguchi method in the optimization of processing parameters for injection moulding. Taguchi method has been employed with great success in experimental designs for problems with multiple parameters due to its practicality and robustness. However, it is realized that there is no single technique that appears to be superior in solving different kinds of problem. Improvements are to be expected by integrating the practical use of the Taguchi method into other optimization approaches to enhance the efficiency of the optimization process. The review will shed light on the standalone Taguchi method and integration of Taguchi method with various approaches including numerical simulation, grey relational analysis (GRA), principal component analysis (PCA), artificial neural network (ANN), and genetic algorithm (GA). All the features, advantages, and connection of the Taguchi-based optimization approaches are discussed.

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Section: Integration Of Taguchi Methods With Principal Component Analymentioning

confidence: 99%

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

Fei

Mehat²,

Kamaruddin

2013

ISRN Industrial Engineering

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“…In this study, just the experimented treatments have been evaluated. Liao and Chen (2002) proposed an input-oriented basic DEA ratio known as the Charnes, Cooper and Rhodes (CCR) model introduced by Charnes et al (1978) that uses the normalized mean responses as input variables when the responses are the NTB or the smaller-thebetter (STB) type; also, Goel et al (2007) proposed a new method in multiple-response optimization using the Pareto optimal solution.…”

Section: Introductionmentioning

confidence: 99%

“…In the model of Liao and Chen (2002), when the responses are LTB type, the normalized mean responses are considered as the output variable. Herein again, only the real experimented treatments and their corresponding responses are considered.…”

Section: Introductionmentioning

confidence: 99%

A neuro-data envelopment analysis approach for optimization of uncorrelated multiple response problems with smaller the better type controllable factors

2013

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In this paper, a new method is proposed to optimize a multi-response optimization problem based on the Taguchi method for the processes where controllable factors are the smaller-the-better (STB)-type variables and the analyzer desires to find an optimal solution with smaller amount of controllable factors. In such processes, the overall output quality of the product should be maximized while the usage of the process inputs, the controllable factors, should be minimized. Since all possible combinations of factors' levels, are not considered in the Taguchi method, the response values of the possible unpracticed treatments are estimated using the artificial neural network (ANN). The neural network is tuned by the central composite design (CCD) and the genetic algorithm (GA). Then data envelopment analysis (DEA) is applied for determining the efficiency of each treatment. Although the important issue for implementation of DEA is its philosophy, which is maximization of outputs versus minimization of inputs, this important issue has been neglected in previous similar studies in multi-response problems. Finally, the most efficient treatment is determined using the maximin weight model approach. The performance of the proposed method is verified in a plastic molding process. Moreover a sensitivity analysis has been done by an efficiency estimator neural network. The results show efficiency of the proposed approach.

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“…It finds major applications in aerospace, nuclear and automotive industries to cut intricate shapes in electrically conductive materials. Liao and Chen [3] used DEAR method for multi-objective optimization and found that the proposed method eliminates uncertainty and complication associated with PCA and Taguchi method. Sahu et.al.…”

Section: Introductionmentioning

confidence: 99%

Multi-quality characteristics optimization of WEDM for IN-625 by applying Taguchi DEAR technique

Sonawane¹,

Kulkarni²

2017

Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

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Abstract:To manufacture quality products at lowest cost in industries, optimization is an effective technique which can be applied to find out the best manufacturing conditions. The objective of this research work is to examine the effect of control factors and to choose the correct settings of these factors to maximize MRR and minimize surface roughness and overcut while WEDM of Inconel-625. The control factors chosen for the study are pulse on time, pulse off time, servo voltage, peak current, wire feed rate and wire tension. Experiments were planned as per Taguchi's L27 orthogonal design under diverse cutting conditions of control factors. Data envelopment analysis based ranking technique (DEAR) was employed for multi-quality characteristic optimization. The optimal values of control factors was found as pulse on time 118 µs, pulse of time 41µs, servo voltage 30 volts, peak current 230 Amp, wire feed rate 1 m/min and wire tension 5 Gram. From the outcomes of the ANOVA, Pulse-on time was found to be the major significant factor affecting the quality characteristics.

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Optimizing multi‐response problem in the Taguchi method by DEA based ranking method

Cited by 54 publications

References 9 publications

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

A neuro-data envelopment analysis approach for optimization of uncorrelated multiple response problems with smaller the better type controllable factors

Multi-quality characteristics optimization of WEDM for IN-625 by applying Taguchi DEAR technique

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