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The paper describes the methods of manufacturing process optimization, using Taguchi experimental design methods with historical process data, collected during normal production.

The objectives are achieved with two separate techniques: the Retrospective Taguchi approach selects the designed experiment's data from a historical database, whilst in the Neural Network (NN) – Taguchi approach, this data is used to train a NN to estimate process response for the experimental settings. A case study illustrates both approaches, using real production data from an aerospace application.

Detailed results are presented. Both techniques identified the important factor settings to ensure the process was improved. The case study shows that these techniques can be used to gain process understanding and identify significant factors.

The most significant limitation of these techniques relates to process data availability and quality. Current databases were not designed for process improvement, resulting in potential difficulties for the Taguchi experimentation; where available data does not explain all the variability in process outcomes.

Manufacturers may use these techniques to optimise processes, without expensive and time‐consuming experimentation.

The paper describes novel approaches to data acquisition associated with Taguchi experimentation.

The purpose of this paper is to present some fundamental and critical differences between the two methods of experimental design (i.e. Taguchi and classical design of experiments (DOE)). It also aims to present an application of Taguchi method of experimental design for the development of an optical fiber sensor in a cost effective and timely manner.

The first part of the paper shows the differences between classical DOE and Taguchi methods from a practitioner's perspective. The second part of the paper illustrates a simple framework which provides guidance in the selection of a suitable DOE strategy. The last part is focused on a simple case study demonstrating the power of Taguchi methods of experimental design.

One of the key questions from many quality and production related personnel in organisations are “when to use Taguchi and Classical DOE?”. The purpose of this paper is to make an attempt to address the above question from a practitioner's perspective.

The case study is based on Taguchi method of experimental design. It would be great to see the results of the study if classical DOE is performed to this study.

The paper will be an excellent resource for both research and industrial fraternities who are involved in DOE projects.

Case study and frame work.

To highlight the application and to compare the effectiveness of the Taguchi and Shainin experimental design processes as applied to aerospace structural components.

This paper applies both the Taguchi and Shainin experimental design techniques to optimizing the design of honeycomb composite joints. The techniques are fully applied, the results analysed and their user friendliness is assessed.

This paper identifies an optimum parameter setting for composite joints gained from applying these experimental design techniques. Significant improvements in joint strength are achieved through identifying a new joint setting.

The adoption of the experimental design techniques outlined in this paper and their application to a real engineering problem will enable a company to apply the techniques and to attain improvements in terms of cost and quality.

The analysis of both the Taguchi and Shainin methodologies and the resulting conclusions as to their effectiveness for industry is the real value of this paper. This paper will be valuable for quality professionals, design engineers and manufacturing specialists in a wide range of industries.

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.

Manufacturers in Europe, Japan, and the USA have widely employed the Taguchi methods of robust experimental design in optimizing product designs and manufacturing/assembly processes. However, these methods have made relatively little inroads into the service industries, for rather obscure reasons. Develops a robust experimental design to study the variabilities of a service process, namely, a customer complaint correction process, used by a small export company. The goal of the study is to reduce system response time to failures resulting from human or equipment error, equipment malfunction or damage, or unspecified abnormalities in the hardware or software modules of the system. Successfully identifies factors that affected the system response time in a statistically significant manner and yielded the optimum combination of factor levels that produce best results as measured in terms of system response time. Also demonstrates the usefulness and applicability of Taguchi methods in a service environment ‐ thus chipping away at the myth that Taguchi methods work only in a manufacturing environment.

To provide a good insight into solving a multi‐response optimization problem using neuro‐fuzzy model and Taguchi method of experimental design.

Over the last few years in many manufacturing organizations, multiple response optimization problems were resolved using the past experience and engineering judgment, which leads to increase in uncertainty during the decision‐making process. In this paper, a four‐step procedure is proposed to resolve the parameter design problem involving multiple responses. This approach employs the advantage of both artificial intelligence tool (neuro‐fuzzy model) and Taguchi method of experimental design to tackle problems involving multiple responses optimization.

The proposed methodology is validated by revisiting a case study to optimize the three responses for a double‐sided surface mount technology of an electronic assembly. Multiple signal‐to‐noise ratios are mapped into a single performance statistic through neuro‐fuzzy based model, to identify the optimal level settings for each parameter. Analysis of variance is finally performed to identify parameters significant to the process.

The proposed model will be validated in future by conducting a real life case study, where multiple responses need to be optimized simultaneously.

It is believed that the proposed procedure in this study can resolve a complex parameter design problem with multiple responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready‐made neural and statistical software like Neuro Work II professional and Minitab.

This study adds to the literature of multi‐optimization problem, where a combination of the neuro‐fuzzy model and Taguchi method is utilized hand‐in‐hand.

The use of Taguchi experimental design techniques to examine the effects of package type, solder paste type and solder reflow technique on the quality of fine pitch surface mount IC package solder joints is described. In particular, the effect of the use of ceramic or plastic packages, copper or Alloy 42 leadframes, silver loaded or non‐silver loaded solder paste and infra‐red, laser or hot‐bar reflow on solder joint metallurgical structure, electrical resistance and mechanical strength is evaluated. In addition to these solder joint parameters, an associated visual inspection was used to find the best process parameters to minimise solder balling, bridging etc. and a correlation between paste contacts at placement and solder bridges after reflow was also conducted. The experiment used an L9 array to find the optimum parameters from three factors, each at three levels. An extension to the basic Taguchi array was included in the form of an outer (noise) factor to include the effect of climatic stress on the solder joints under investigation. Response tables separate out the contribution of each factor level to the mechanical strength and electrical resistance of the assemblies. By comparing the response tables before and after climatic testing it is possible to estimate the effect of each factor level on the long‐term quality of the solder joints. It is shown how Taguchi experimental design techniques can be used to minimise the number of experiments required to predict optimum solder assembly process parameters. The accuracy of the prediction is shown by the results of a confirmation run which yielded mechanical strengths very close to those predicted, both before and after highly accelerated stress testing of the solder assemblies.

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.

Experimental design (ED) is a powerful technique which involves the process of planning and designing an experiment so that appropriate data can be collected and then analysed by statistical methods, resulting in objective and valid conclusions. It is an alternative to the traditional inefficient and unreliable one‐factor‐at‐a‐time approach to experimentation, where an experimenter generally varies one factor or process parameter at a time keeping all other factors at a constant level. This paper presents a step‐by‐step approach to the optimisation of a production process (of retaining a metal ring in a plastic body by a hot forming method) through the utilisation of Taguchi methods of experimental design. The experiment enabled the behaviour of the system to be understood by the engineering team in a short period of time and resulted in significantly improved performance (with the opportunity to design further experiments for possible greater improvements).

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.