Robust design for multiple dynamic quality characteristics using data envelopment analysis

The Taguchi method is extensively adopted in various industries to continuously improve product design in response to customer requirements. The dynamic system of the Taguchi method is frequently implemented to design products with flexible applications. However, Taguchi's dynamic system can be employed only for individual quality characteristic, and the relationship between the quality characteristic and the signal factor is assumed to be linear. Because of these restrictions, Taguchi's dynamic system is ineffective for multiple quality characteristics or when the quality characteristic has a nonlinear relationship with the signal factor. This study describes a novel procedure for optimizing a dynamic system based on data envelopment analysis. The proposed procedure overcomes the limitations of Taguchi's dynamic system. Two cases are analyzed to demonstrate the effectiveness of the proposed procedure. The results show that the proposed procedure can enhance multiple dynamic quality characteristics. Copyright © 2008 John Wiley & Sons, Ltd.     

Confirmation testing of the Taguchi methods by artificial neural-networks simulation

In Taguchi's methods of parameter design, a confirmation test is usually necessary to remove concerns about the choice of control parameters, experimental design, or assumptions about responses. This paper investigated the use of artificial neural-networks simulation to validate the set of control parameters identified as significant through Taguchi's methods, and to verify that the recommended settings for the control parameters are indeed optimal or near-optimal. Using the experimental layout and measured responses from a Taguchi parameter-design experiment, we applied cross-validate training to ascertain that the trained neural-network can reproduce acceptable results on unseen experimental layouts. We then used the trained neural-network to simulate and search for the global optimal settings for the control parameters, and the results compared with the recommended settings from the Taguchi parameter-design experiment.     

Analysis of dynamic robust design experiments

Robust design is an important method for improving product or manufacturing process design by making the output response insensitive (robust) to diDcult-tocontrol variations (noise). Most of the robust design research in the literature focuses on problems with static responses. This paper investigates robust design problems with dynamic responses. For analysing robust design experiments with dynamic systems, Taguchi (1986) proposes a two-step procedure to identify the 'optimal' factor settings that minimize the average quadratic loss. In this paper we show that Taguchi's procedure is only appropriate under a multiplicative model. We develop the appropriate two-step procedure for dynamic systems under an additive model. The procedure reduces the dimension of the optimization problem and allows for future changes of the target slope without re-optimization. We illustrate the proposed procedure and Taguchi's procedure with real examples. We also discuss future research and extensions to general classes of models.     

Integrating statistical process control,engineering process control and Taguchi's quality engineering

In this paper, a scheme is proposed to integrate statistical process control (SPC), engineering process control (EPC) and Taguchi's quality engineering (TQE). Then, two models are proposed to implement the proposed scheme. The models employ the concept of Taguchi's quadratic loss function to determine whether to take an EPC action by comparing the cost of the action and the cost of quality. A case study is used to compare these two models with the model in the literature where SPC and EPC have been integrated. The results have shown that the first model resulted in about a 25% saving and the second model resulted in even greater saving of about 30% for the case under consideration.     

Using Taguchi design,regression analysis and simulation to study maintenance float systems

This paper proposes a new strategic approach for the maintenance float decision models. This new approach incorporates Taguchi experimental design, Taguchi's ANOVA procedure and regression analysis. Taguchi experimental design is used to generate the input variables into the simulation program. The results are analysed using Taguchi's ANOVA procedure. Input variables found significant are subsequently applied in a regression model. Predictor models for the system utilization are developed and their validity tested. Cost oriented decision models are further developed to show the applicability of our models to decision situations. The major advantage of the strategic approach proposed here is that it saves time and reduces the cost of running simulation. This approach will also offer a decision support and improve the effectiveness of maintenance float decisions.     

Testing in Industrial Experiments With Ordered Categorical Data

This article deals with some techniques for analyzing ordered categorical data from industrial experiments for quality improvement. Taguchi's accumulation analysis method is shown to have reasonable power fordetecting important location effects; however, it is an unnecessarily complicated procedure. For detecting dispersion effects, it need not even be as powerful as Pearson's chi-squared test. Instead two simple and easy to use scoring schemes are suggested for identifying the location and dispersion effects separately. The techniques are illustrated on data from an experiment to optimize the process of forming contact windows in complementary metal-oxide semiconductor circuits.     

Reliability improvement via taguchi's robust design

Taguchi's robust design strategy, whose aim is to make processes and products insensitive to factors which are hard or impossible to control (termed noise factors), is an important paradigm for improving products and processes. We present an overview of the strategy and tactics for robust design and demonstrate its usefulness for reliability improvement. Two important components of robust design are a criterion for assessing the effect of the noise factors and experimentation according to specialized experimental plans. Recent criticism of Taguchi's criterion and his analysis of its estimates has led to an alternative approach of modelling the response directly. We give additional reasons for using this response-model approach in the context of reliability improvement. Using the model for the response, appropriate criteria for assessing the effect of the noise factors can then be evaluated. We consider an actual experiment and reanalyse its data to illustrate these ideas and method.     

Compound Noise: Evaluation as a Robust Design Method

This paper evaluates compound noise as a robust design method. Application of compound noise as a robust design method leads to a reduction in experimental effort. The compound noise strategy was applied to two types of situation: the first type has been described with active effects up to two‐factor interactions and the second type has been described with effects up to three‐factor interactions. These two situations are illustrated with help of case studies. The paper provides theoretical justification for the effectiveness of the compound noise strategy as formulated by Taguchi and Phadke. For example, we found that the compound noise strategy is very effective for systems which exhibit effect sparsity. This paper gives an alternative procedure to formulate a compound noise, distinctly different from Taguchi's formulation. The alternative method requires less information to formulate compound noise as compared to Taguchi's formulation. Overall, the paper studies the effectiveness of such an alternative formulation, outlines scenarios where compound noise as a robust design method can be effectively used and gives alternative strategies for the systems on which compound noise cannot be effective. Copyright © 2006 John Wiley & Sons, Ltd.     

Parametric optimization of surface roughness castings produced by Evaporative Pattern Casting process

In the present paper Taguchi's approach has been applied to the Evaporative Pattern Casting (EPC) process of Al–7%Si alloy to determine the most influential control factors which will provide better and consistent surface roughness to the castings regardless of the noise factors present. In order to evaluate the effect of process parameters such as grain fineness number, time of vibration, degree of vacuum and pouring temperature on surface roughness of EPC process castings, the Taguchi parameter design and optimization approach is used. Through the Taguchi's parameter design approach, optimal levels of process parameters have been determined. The results indicated that the grain fineness number, time of vibration, degree of vacuum were the significant parameters in deciding the surface roughness of Al–7%Si alloy castings. Pouring temperature was the insignificant parameter. The predicted optimal value of surface roughness of Al–7%Si alloy castings produced by EPC process was 2.31 μm. The results were confirmed by further experiments.     

The use of a generalized signal-to-noise ratio to identify adjustment and dispersion factors in taguchi experiments

A signal-to-noise ratio proposed by Taguchi for ‘nominal the best’ can be made more flexible and used additionally for the cases ‘smaller the better’ and ‘larger the better’. The main feature which distinguishes this generalized ratio from Taguchi's signal-to-noise ratios is that its precise form is determined by the experimental data. Taken together with a transformation of the mean response it effectively identifies adjustment (signal) factors and dispersion factors. Examples are given to illustrate the routine operational procedure and also to demonstrate that Taguchi's signal-to-noise ratios can lead to inefficient, and sometimes incorrect, identification of design factors. Two important considerations are to preserve the evident appeal of the Taguchi method to engineers and also to provide a theoretical justification which is acceptable to statisticians. The principal objective of the joint transformation is to achieve approximate functional independence between the mean and variance in the new metric. This, in turn, leads to efficient identification of adjustment and dispersion factors. A comparison is made with a similar approach using Box and Cox transformations.     

Strategies for planning experiments using orthogonal arrays and confounding tables

Genichi Taguchi has popularized a robust design method which employs experimental design techniques to help identify the levels of design factors to improve the quality of products and manufacturing processes. Experimental design techniques are extremely effective for identifying improved factor levels in problems that involve a large number of factors. Taguchi's success in getting engineers to use experimental design techniques is due, at least in large part, to his use of tools and techniques that simplify the experiment planning process. Recognizing the advantages of this approach, this paper proposes a new set of tools, confounding tables, which offer more guidance to experimenters. Confounding tables provide a clear and systematic representation of confounding relationships. They are simple and useful tools for constructing experiment plans, and they enable users easily to evaluate the confounding patterns of a completed plan. We show how confounding tables provide more information than Taguchi's linear graphs, and are useful for a large class of experiment plans.     

An explanation and critique of taguchi's contributions to quality engineering

Recently there has been much interest and some controversy concerning the statistical methods employed by Professor Genichi Taguchi of Japan for improving the quality of products and processes. These methods include the use of fractional factorial designs and other orthogonal arrays, parameter design to minimize sensitivity to environmental factors, parameter design for minimizing transmitted variation, signal-to-noise ratios, loss functions, accumulation analysis, minute analysis and the analysis of life test data. This paper explains some of Taguchi's contributions to quality engineering and also provides a critical evaluation of his statistical methods. Our conclusion is that although on the one hand, Professor Taguchi's quality engineering ideas are of great importance and should become part of the working knowledge of every engineer, on the other hand, many of the techniques of statistical design and analysis he employs to put these ideas into practice are often inefficient and unnecessarily complicated and should be replaced or appropriately modified. In this short article only an overview is attempted, but references are appended where these matters are discussed in greater detail.     

Robust design with dynamic characteristics using stochastic sequential quadratic programming

Robust design with dynamic characteristics is an important off-line quality engineering technique for improving product quality over a range of input conditions by reducing variations caused by uncontrolled factors. Since several studies have indicated that there are important limitations to Taguchi's S/N ratio analysis, the solution procedure for dynamic systems deserves further investigation. This paper proposes a stochastic optimization modeling procedure to overcome the difficulty in Taguchi's method to accommodate dynamic characteristics. The main idea underlying the proposed method is to minimize the total variations on quality characteristics while attaining the target performance over a range of input conditions. Due to the nonlinear nature of the stochastic optimization model, two stochastic versions of sequential quadratic programming respectively embedded with a Monte Carlo simulation and numerical approximations are devised to solve the problem. In the robust design of a temperature control circuit often discussed in dynamic problems, the proposed method performs efficiently and effectively. Compared with the Taguchi method, the design solved in this paper has smaller variations, indicating that the proposed method is a promising technique for dynamic-characteristic robust design.     

Modelling and analysis of selective assembly using Taguchi's loss function

An assembly is the integrative process of joining components to make a completed product. It brings together the upstream process of design, engineering and manufacturing processes. The functional performance of an assembled product and its manufacturing cost are directly affected by the individual component tolerances. But, the selective assembly method can achieve tight assembly tolerance through the components manufactured with wider tolerances. The components are segregated by the selective groups (bins) and mated according to a purposeful strategy rather than being at random, so that small clearances are obtained at the assembly level at lower manufacturing cost. In this paper, the effect of mean shift in the manufacturing of the mating components and the selection of number of groups for selective assembly are analysed. A new model is proposed based on their effect to obtain the minimum assembly clearance within the specification range. However, according to Taguchi's concept, manufacturing a product within the specification may not be sufficient. Rather, it must be manufactured to the target dimension. The concept of Taguchi's loss function is applied into the selective assembly method to evaluate the deviation from the mean. Subsequently, a genetic algorithm is used to obtain the best combination of selective groups with minimum clearance and least loss value within the clearance specification. The effect of the ratio between the mating part quality characteristic's dimensional distributions is also analysed in this paper.     

Design for Quality Using Response Surface Methods: An Alternative to Taguchi's Parameter Design Approach

ABSTRACT

This article presents the response surface methodology as an alternative approach to Taguchi's parameter design methods for optimizing designs for quality. The method is briefly explained, and its application is illustrated by an example of a preliminary design study of an advanced space transportation vehicle. The results indicate that the response surface methodology is a systematic and efficient approach that can help engineering managers design for quality, performance, and cost.     

Dynamic management of cutting tools for flexible and quality machining

Traditional economic tool-life models assume a homogeneous cutting environment, where a tool's continued service is irrespective of its condition. It is wellknown that the quality of a machining process is significantly impacted by a tool's wear-and-tear. To ensure good machining quality, tool assignment should consider the wear level of the tool as well as the type of machining job to be performed. This paper presents a dynamic management model for cutting tools that emphasizes the cost of machining quality. The model describes a heterogeneous environment typical of computerized manufacturing systems, where a tool carries out variable machining assignments during its life. The formulation is a stochastic dynamic programme, which determines optimal preventive actions based on a periodic evaluation of the tool's operating conditions. Tool deterioration is described as movement to different operating states (increasing levels of tool wear) and job assignment of tools is state-dependent. A tool's optimal economic life is also determined within the context of variable machining. The cost of quality-deviation is assessed using Taguchi's quality-loss function.     

Off-line quality control for V-process castings

In the present work Taguchi's approach has been applied to the V-process castings of Al-11 per cent Si alloy to acertain the most influential control factors which will provide better and consistent surface finish to the castings regardless of the noise factors present. The control factors of the V-process that may affect the quality of the castings are the molding sand, vibration frequency, vibrating time, degree of vacuum imposed, and pouring temperature. In order to understand how these factors affect the surface roughness of the V-process castings, response surface methodology has been applied, and to obtain the optimal setting of the control factors Taguchi's method has been used. It is found that the pouring temperature has a significant effect on the surface roughness of Al-11 per cent Si alloy castings made by a V-process. Thus the pouring temperature must be kept at the lower level. All other factors are insignificant. Therefore, any setting of the insignificant factors/variables that give the minimum cost can be used.     

ROBUST DESIGN USING COMPROMISE DECISION SUPPORT PROBLEMS

Compromise Decision Support Problems (DSPs) are used to model engineering decisions involving multiple trade-offs. In this paper, the focus is on how to apply such decision models in robust design. Suh's independence and information content axioms and Taguchi's signal to noise ratio are used as metrics for the assessment and improvement of the quality in this decision model. As an example, a compromise DSP for the robust design of an electrical network is used. Traditionally, in robust design, parameter and tolerance design are done sequentially and not concurrently. Furthermore, each time parameter and tolerance design are done in practice, the focus is usually on looking at one parameter at a time and not on looking at multiple parameters simultaneously. Using the electrical network as an example, it is shown how parameter and tolerance design involving multiple parameters can be performed concurrently.     

The Taguchi's Performance Statistic to Optimize Theophylline Beads Production in a High-Speed Granulator

An original process using a simple procedure is developed to produce theophylline active pellets. In order to improve this process, an optimization approach is applied. But rather than only trying to bring the process to the target optimal values, attempt is made to find operating conditions leading also to stable and non-sensitive pellets characteristics. In this purpose, the classic experimental design approach and response surface methodology are completed by using Taguchi's philosophy.     

Economic–statistical Design of Maximum Exponentially Weighted Moving Average Control Charts

The maximum exponentially weighted moving average (MaxEWMA) control chart effectively combines the two EWMA charts into one chart and monitors both increases and decreases in the mean and/or variability. In this paper, we develop the economic–statistical design of the MaxEWMA control chart in which the Taguchi's quadratic loss function is incorporated into the Duncan's economical model. Numerical simulations are executed to minimize the expected total cost model and determine the optimal decision variables, including the sample size, sampling interval, control limit width, and the smoothing constant of the MaxEWMA control chart. It is shown that the optimal control limit width and smoothing constant increase as the optimal cost value increases and that both the optimal sample size and sampling interval always decrease as the magnitudes of mean and/or variance shifts increase. Copyright © 2012 John Wiley & Sons, Ltd.