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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 paper is to find the optimal values of process parameters in injection molding when both warpage and shrinkage are minimized.

In finding the optimal values, advantages of finite element software, Moldflow, and dual response surface method (dual RSM) combined with the nonlinear programming technique by Lingo are exploited. Considering the nine process parameters, injection time, injection pressure, packing pressure, packing time, cooling time, coolant temperature, mold‐open time, melting temperature and mold surface temperature, a series of mold analyses are performed to exploit the warpage and shrinkage data. In the analyses, warpage is considered the primary response, whereas shrinkage is the secondary response.

The results indicate that dual RSM combined with the nonlinear programming technique can outperform the Taguchi's optimization method. The optimal process values are also confirmed by re‐running experiments on Moldflow. Additionally, an auxiliary dual RSM model is proposed to search for a better result based on the given findings by dual RSM at the cost of running extra experiments. Based on dual RSM, a multiple objective optimization for the whole plastic product is finally suggested to integrate the dual RSM models that are developed for the individual nodes or edges.

This paper proposes a new method to find the optimal process for plastic injection molding.

The purpose of this paper is to investigate experimentally the effect of volume of casting, pouring temperature of different materials and shell mould wall thickness on the surface roughness of the castings obtained by using ZCast direct metal casting process.

Taguchi's design of experiment approach was used for this investigation. An L9 orthogonal array (OA) of Taguchi design which involves nine experiments for three factors with three levels was used. Analysis of variance (ANOVA) was then performed on S/N (signal‐to‐noise) ratios to determine the statistical significance and contribution of each factor on the surface roughness of the castings. The castings were obtained using the shell moulds fabricated with the ZCast process and the surface roughness of castings was measured by using the surface roughness tester.

Taguchi's analysis results showed that pouring temperature of materials was the most significant factor in deciding the surface roughness of the castings and the shell mould wall thickness was the next most significant factor, whereas volume of casting was found insignificant. Confirmation test was also carried out using the optimal values of factor levels to confirm the effectiveness of this approach. The predicted optimal value of surface roughness of castings produced by ZCast process was 6.47 microns.

The paper presents experimentally investigated data regarding the influence of various control factors on the surface roughness of castings produced by using ZCast process. The data may help to enhance the application of ZCast process in traditional foundry practice.

The purpose of this paper is to present computer‐generated combined arrays as efficient alternatives to Taguchi's crossed arrays to solve robust parameter problems.

The alternative combined array designs were developed for the cases including six to twelve variables where CMR designs are not smaller than Taguchi's designs. The efficiency to estimate the effects of interest was calculated and compared to the efficiency of the corresponding CMR designs.

For all the cases investigated at least one computer generated combined array design was found with the same size as the CMR design and with higher efficiency.

Robust parameter design identifies appropriate levels of controllable variables in a process for the manufacturing of a product. The designed experiments involve the controllable variables along with the uncontrollable or noise variables to design a product or process that will be robust to changes in these noise variables. Response surface methodology estimates the actual relationship between the response and the input variables with an empirical model based on the designed experiment. Once the empirical model is fitted, the surface described by the model can be used to describe the behavior of the response over the experimental region. The higher efficiency of the computer generated combined array designs proposed in this research produces lower variances for the parameter estimates and lower variance of prediction for the model. As a result, the response will be described in a more realistic form.

The paper shows that using a computer‐generated design to solve a robust parameter problem will result in a better approximation to the true response of the process. Consequently, optimizing the fitted model will produce settings for the parameters closer to the real optimal settings.

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.

The aim of the paper is the fine‐tuning of proportional integral derivative (PID) controllers under model parameter uncertainties (noise).

The fine‐tuning of PID controllers achieved using the Taguchi method following the steps given: selection of the control factors of the PID with their levels; identification of the noise factors that cause undesirable variation on the quality characteristic of PID; design of the matrix experiment and definition of the data analysis procedure; analysis of the data; decision regarding optimum settings of the control parameters and predictions of the performance at optimum levels of control factors; calculation of the expected cost savings under optimum condition; and confirmation of experimental results.

An example of the proposed method is presented and demonstrates that given certain performance criteria, the Taguchi method can indeed provide sub‐optimal values for fine PID tuning in the presence of model parameter uncertainties (noise). The contribution of each factor to the variation of the mean and the variability of error is also calculated. The expected cost savings for PID under optimum condition are calculated. The confirmation experiments are conducted on a real PID controller.

As a further research it is proposed the contiguous fine‐tuning of PID controllers under a number of a variant controllable models (noise).

The enhancement of PID controllers by Taguchi method is proposed with the form of a hardware mechanism. This mechanism will be incorporated in the PID controller and automatically regulate the PID parameters reducing the noise influence.

Application of Taguchi method in the scientific field of automation control.

In the Taguchi’s experimentations, orthogonal arrays, interaction tables, and linear graphs are provided for planning experiments, but they become quite unwieldy when the number of runs is large. The purpose of this article is to propose a quick and easy method for obtaining two‐factor interaction columns in two‐level orthogonal arrays. Geometrical designs proposed by Plackett and Burman are two‐level orthogonal arrays and can be obtained very easily by a successive doubling method. Based on the property of doubling, a NR method using a number representation system whose base is a power of 2 is derived in this article for obtaining two‐factor interaction columns in geometrical designs. Furthermore, since Taguchi’s two‐level orthogonal arrays are obtainable by successive doubling with some column permutations, it is shown that their two‐factor interaction columns can be obtained directly by using the NR method without looking up tables.

This paper aims to depict the erosion performance of two HVOF-coated micron layers (Colmonoy-88 and Stellite-6) on pump impeller steel (SS-410) by using Taguchi's method. Taguchi's array (L16) was used to optimize the erosion wear (in terms of weight loss) by using four influencing parameters such as rotational speed, solid concentration, average particle size and time which were varied at four different levels.

The experiments were carried out by using a Ducom slurry tester with rotational speed in the range of 750-1,500 rpm, solid concentration of 35-65 per cent by weight, time period of 75-210 min and average particle sizes in the range of < 53 to 250 µm. Bottom Ash with a nominal size range of < 53 to 250 µm was used as erodent. The process parameters were optimized by using Taguchi's method. The ANOVA method was used to validate the results given by Taguchi's method.

The results revealed that the presence of both carbides and borides and the additional presence of Cr in Colmonoy-88 coating enhancing the slurry erosion resistance of Colmonoy-88 coating. Moreover, the chromium and tungsten carbide particles help in increasing the bond strength between the coating and the substrate material. Further, it was also found that the time was the most dominant factor as compared to other factors.

The very less work has been reported on optimization of erosion wear response of Colmonoy-88 and Stellite-6 coatings by using different design of experiment techniques. Further, the erosion wear mechanism of both coatings has been studied by using image j analysis software.

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.

This paper aims to illustrate an application of Taguchi method of experimental design (TMED) for the development of a new ignition coil for an automotive vehicle.

The application of TMED for optimisation of manufacturing processes has been widely published in the existing literature. However, the applications of TMED in the design and development of new products are not yet widely reported. This case study presents the results of a designed experiment which utilises a 16‐trial experiment to study 14 design parameters and one interaction. The case study strictly follows a systematic and disciplined methodology outlined in the paper.

The optimal settings of the critical design parameters are determined. The optimal settings have resulted in increased customer satisfaction, improved market share and low defect rate in the hands of customers.

Although the optimal levels are determined from one large experiment, it was unable to determine the true optimal values of each design parameter.

Manufacturers may use TMED to optimise processes (either design or manufacturing) without expensive and time‐consuming experimentation. This case study demonstrates the true power of a well planned and designed experiment over the traditional varying one‐factor‐at‐a‐time approach to experimentation which is rather unreliable, not cost‐effective and may lead to false optimal conditions.

The paper provides an excellent resource for those people who are involved in the design optimisation of a new product.