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Taguchi's technique is best suited to optimize a single performance characteristic yielding an optimal setting of process parameters. A single setting of process parameters may be optimal for one quality characteristics but the same setting may yield detrimental results for other quality features. Thus the purpose of this paper is to describe simultaneous optimization of multi‐characteristics.

The multi‐machining characteristics have been optimized simultaneously using Taguchi's parameter design approach and the utility concept. The paper used a single performance index, utility value, as a combined response indicator of several responses.

A simplified model based on Taguchi's approach and utility concept is used to determine the optimal settings of the process parameters for a multi‐characteristic product. The model is used to predict optimal settings of turning process parameters to yield the optimum quality characteristics of En24 steel turned parts using TiC coated carbide inserts. The optimal values obtained using the multi‐characteristic optimization model have been validated by confirmation experiments. The model can be extended to any number of quality characteristics provided proper utility scales for the characteristics are available from the realistic data.

The proposed methodology can be applied to those industrial situations where a number of responses are to be optimized simultaneously.

The paper discusses a case study on En24 steel turned parts using titanium carbide coated tungsten carbide inserts. The material, En24 steel, has wide applications in aerospace, machine tools, automobiles, etc. The proposed algorithm is easy to apply.

To provide a modified orthogonal array‐based model for enabling the researchers and practitioners to exploit the technique, “design of experiments” in an agile manufacturing environment.

The characteristics of Taguchi's off‐line models and agile manufacturing were studied. A theoretical model of modified orthogonal array‐based experimentation was designed. This model was subjected to implementation study in an Indian pump‐manufacturing company.

The model contributed in this paper has shown its feasibility in achieving quality in agile manufacturing environment.

The authors are residing in an Indian city where the majority of the companies have not adopted agile manufacturing criteria. Hence, it was not possible to carry out implementation study in an agile manufacturing company. Future researchers should examine the practical validity of the proposed model in agile manufacturing companies.

Since the manufacturing organizations are fast becoming agile, due to the customers' dynamic demands coupled with competition, the traditional quality improvement techniques are becoming obsolete. The model contributed in this paper is found to be useful in achieving continuous quality improvement in AM environment. Hence the model would be a useful technique for today's practitioners whose activities are increasingly focused towards achieving agility in manufacturing.

The literature survey covering articles on agile manufacturing indicates that no researcher or practitioner has contributed a model that would exploit the technique, “design of experiments” in an agile manufacturing environment. Hence the proposed model is expected to be of high value for researchers and practitioners to explore the way of achieving continuous quality improvement in agile manufacturing environment.

The purpose of this study is to investigate the prediction and optimization of multiple performance characteristics in the face milling process of tool steel ASSAB XW-42.

The face milling parameters (cutting speed, feed rate and axial depth of cut) and flow rate (FR) of cryogenic cooling were optimized with consideration of multiple performance characteristics, i.e. surface roughness (SR), cutting force (F_c) and metal removal rate (MRR). FR of cryogenic cooling has two levels, whereas the three face milling parameters each have three levels. Using Taguchi method, an L₁₈ mixed-orthogonal array was selected as the design of experiments. The rough estimation of the optimum face milling parameters was determined by using grey fuzzy analysis. The global optimum face milling parameters were searched by applying the backpropagation neural network-based genetic algorithm (BPNN-GA) method.

The optimum SR, cutting force (F_c) and MRR could be obtained by setting FR, cutting speed, feed rate and axial depth of cut at 0.5 l/min, 280 m/min, 90 mm/min and 0.2 mm, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the multiple performance characteristics.

To the best of the authors’ knowledge, there were no publications available regarding multi-response optimization using the combination of grey fuzzy analysis and BPNN-based GA methods during cryogenically face milling process.

Reports on a study carried out on an injection moulding process to produce agitators for washing machines, following complaints from customers. The study revealed that there was a lot of variation in the product dimensions produced. Attempts to demonstrate how robust design methodology is helpful in achieving variation reduction of product dimensions and achieving target values. Various analyses carried out using ANOVA and ANOM helped to understand the dynamics of the process. In the presence of multi‐responses and specially when the responses have conflicting behavior to each other, selection of optimum conditions for the process is explained in detail. Reports on the importance of confirmation experiments and the outcome of this detailed exercise.

The purpose of this paper is to demonstrate application of the Taguchi method to determine the optimum level of three important parameters (factors) related to the use of a mobile phone for the text message entering task, namely illumination level, noise level and mobile angle that maximizes the performance of mobile phone users.

Three levels of each parameter as available in the literature, except for the mobile angle, were considered. The design of the experiment, as proposed by Genichi Taguchi, was used to conduct nine experiments. A total of 30 male subjects participated in the experimental study. The text message entry task, in the form of Arabic text, was presented to the participating subjects and their performance, measured in terms of mean number of characters entered per minute, was recorded. The signal‐to‐noise (S/N) ratio and the analysis of variance (ANOVA) were employed to investigate the users' performance. Finally, a confirmation test was conducted to verify the validity of the results.

Results showed that, at the illumination level of 475 lux, noise level of 45 dB(A), and mobile angle of 70 degrees, the subjects were quite comfortable, efficient and entered the maximum number of characters in the mobile phone per minute. The noise was found to be the dominant parameter with a contribution of 95.53 percent towards the laid down objective followed by mobile angle, 3.25 percent and illumination level, 0.66 percent.

To the best of the authors' knowledge no study has been conducted in the past to investigate the effect of these parameters on the performance of the mobile phone users. In addition, no attempt has yet been made to find the optimal level of these parameters from a text‐entering viewpoint. The paper represents original research and in the authors' opinion carries significantly important values as it provides new information for those involved in the design of the mobile phone environment.

Reviews the global status of total quality management (TQM). Emphasizes continuous quality improvement as one of the main pillars of TQM. Illustrates a part of the research that was carried out to examine Taguchi′s on‐line quality control (TOLQC) methods as the means to effect continuous quality improve‐ment. Describes a case study that was carried out to study the implementation feasibilities of TOLQC methods. Highlights the inferences drawn from this case study which assert the need for managerial approach rather than mere technical computations for successful implementation. Insists on intensified training and awareness programmes on the implementation strategies of these methods to attain the ultimate goals of TQM.

Binder jetting (BJ) process is an additive manufacturing (AM) process in which powder materials are selectively joined by binder materials. Products can be manufactured layer-by-layer directly from three-dimensional model data. The quality properties of the products fabricated by the BJ AM process are significantly affected by the process parameters. To improve the product quality, the optimal process parameters need to be identified and controlled. This research works with the 420 stainless steel powder material.

This study focuses on four key printing parameters and two end-product quality properties. Sixteen groups of orthogonal experiment designed by the Taguchi method are conducted, and then the results are converted to signal-to-noise ratios and analyzed by analysis of variance.

Five sets of optimal parameters are concluded and verified by four group confirmation tests. Finally, by taking the optimal parameters, the end-product quality properties are significantly improved.

These optimal parameters can be used as a guideline for selecting proper printing parameters in BJ to achieve the desired properties and help to improve the entire BJ process ability.

Recent advances in modern technology have generated the need to develop newer materials for better antifriction and wear properties. The objective is to analyse the significance of design parameters that significantly affects the dry sliding wear.

The tribological behaviour of aluminium alloy (Al‐Si10Mg) reinforced with alumina and graphite produced by liquid metallurgy is studied using pin‐on‐disc wear test apparatus under dry sliding condition. Experiments are conducted based on the plan of experiments generated through Taguchi technique. A L27 Orthogonal array is selected for analysis of the data. Influence of applied load, sliding speed and weight percentage of reinforcements on wear rate as well as the coefficient of friction during wearing process is studied using analysis of variance technique and regression equations for each response are developed. Finally, confirmation tests are carried out to verify the experimental results.

Mechanical property such as hardness has been evaluated and it was found that the hardness increases as reinforcement content increases. The wear rate and coefficient of friction increases by increasing load and decreases by increasing sliding speed and weight percentage of reinforcements. Results from analysis of variance reveals that the applied load has the highest influence on both wear rate and coefficient of friction, followed by sliding speed and weight percentage of reinforcement.

Aluminium hybrid metal matrix composites showing ample success in improving strength and wear resistance by utilising the optimal process condition.

The results obtained by this method are useful in improving the dry sliding wear resistance.

The aim of the this study is to develop a new class of composites which would be more commercially viable and environmentally sustainable via reduced resource depletion, as there has been global interest in utilization of natural resources. The dry sliding wear behavior of glass-epoxy (G-E)-based composites filled with tamarind kernel powder (TKP) in different volume fractions of fillers (0 per cent, 3 per cent and 6 per cent) was studied as per standards.

In the present study, the analysis and optimization of the wear process has been studied. The Taguchi approach to experimental design was used to identify the effect of wear parameters such as applied load, sliding velocity and sliding distance. Taguchi tools such as analysis of variance and multiple linear regression models have been used to analyze, obtain the significant parameters and evaluate the optimum combination levels of wear process parameters. The results of Taguchi analysis indicate that sliding distance was found to be the prominent parameter affecting wear volume loss compared to other wear parameters.

The G-E composites with 3 and 6 vol.% of TKP had the lowest wear volume loss. Multiple linear regression models for all the tested composites’ results well match with experimental results. Confirmation tests were conducted to validate the analysis. There was a close relationship between the experimental results and the statistical model.

However, to the best of author’s knowledge, these literature reports related to natural organic filler materials are limited to analysis of polymer matrix composite. Further, the addition of TKP particle as a potential filler has not been addressed. An attempt has been made to clarify the technical viability of TKP as a potential filler for G-E composite.

The purpose of this paper is to investigate prediction and optimization of multiple performance characteristics in the wire electrical discharge machining (wire-EDM) process of SKD 61 (AISI H13) tool steel.

The experimental studies were conducted under varying wire-EDM process parameters, which were arc on time, on time, open voltage, off time and servo voltage. The optimized responses were recast layer thickness (RLT), surface roughness (SR) and surface crack density (SCD). Arc on time was set at two different levels, whereas the other four parameters were set at three different levels. Based on Taguchi method, an L18 mixed-orthogonal array was selected for the experiments. Further, three methods, namely grey relational analysis (GRA), backpropagation neural network (BPNN) and genetic algorithm (GA), were applied separately. GRA was performed to obtain a rough estimation of optimum drilling parameters. The influences of drilling parameters on multiple performance characteristics were determined by using percentage contributions. BPNN architecture was determined to predict the multiple performance characteristics. GA method was then applied to determine the optimum wire-EDM parameters.

The minimum RLT, SR and SCD could be obtained by setting arc on time, on time, open voltage, off time and servo voltage at 2 ms, 3 ms, 90 volt, 10 ms and 38 volt, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the responses.

There were no publications regarding multi-response optimization using a combination of GRA and BPNN-based GA methods during wire-EDM process available.