Earticle

Robust design is an approach to reducing performance variation of quality characteristic values in quality engineering. Product array approach which is used in the Taguchi parameter design has a number of advantages by considering the noise factor. Taguchi has an idea that mean and variation are handled simultaneously to reduce the expected loss in products and processes. Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. In this paper we propose a substantially simpler optimization procedure for robust design using desirability function without resorting to SN.

Robust design is an approach to reducing performance variation of quality characteristic values in quality engineering. Taguchi has an idea that mean and variation are handled simultaneously to reduce the expected loss in products and processes. In the Taguchi parameter design, the product-array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined-array approach, was studied. In these studies, only single quality characteristic (or response) was considered. In this paper we propose how to simultaneously optimize for multiple quality characteristics (or multiresponse) using desirability function when we used the combined-array approach to assign control and noise factors.

This paper presents the optimization steps with weight and importance of estimated characteristic values in the multiresponse surface analysis(MRA). The research introduces the shape parameter of individual desirability function for relaxation and tighening of specification bounds. The study also proposes the combinded desirability function using arithmetic, geometric and harmonic means considering the measurement unit and numerical pattern.

In a sheet metal forming process, fracture and wrinkle are the most difficult task in new parts launching. The variation in process condition generates the fracture and wrinkle fluctuation. The fracture and wrinkle are very sensitive to the process conditions, then the main effects of the design variables cannot be obtained from the standard mean analysis. Therefore, in order to minimize the fracture and wrinkle in parts of automobile, a special method to counterpart is required. In this study, a new design method to achieve the optimal in the sheet metal forming process is proposed. The effectiveness of the proposed methods is shown with an example of the parts of fracture and wrinkle.

Many quality engineering practitioners continue to have a considerable interest in implementing the concept of response surface methodology to real situations. Recently, dual response surface approach is extensively studied and recognized as a powerful tool for robust design. However, existing methods do not consider the information provided by customers and design engineers. In this regard, this article proposes a flexible optimization model that incorporates that information via desirability function modeling. The optimization scheme and its modeling flexibility are demonstrated through an illustrative example by comparing the proposed model with existing ones.

Taguchi parameter design, the product-array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined-array approach, was suggested by Welch et. al. and studied by others. In these studies, only single quality characteristic was considered. We propose how to simultaneously optimize multiple quality characteristics using desirability function when we used the combined-array approach to assign control and noise factors. An example is illustrated to the combined-array approach.

The desirability function approach to multiple response optimization is a useful technique for the analysis of experiments in which several responses are optimized simultaneously. But the existing methods have some defects, and have to be modified to some extent. This paper proposes a new method to combine the individual desirabilities.

Robust design is to identify appropriate settings of control factors that make the system's performance robust to to changes in the noise factors that represent the source of variation. In the Taguchi parameter design, the product array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined array approach, was suggested by Welch et. al. (1990) and studied by others. In these studies, only single response variable was considered. We propose how to simultaneously optimize multiple responses when we use the combined array approach.

The object of multiresponse optimization is to determine conditions on hte independent variables that lead to optimal or nearly optimal values of the response variables. Derringer and Suich (1980) extended Harrington's (1965) procedure by introducing more general transformations of the response into desirability functions. The core of the desirability a, pp.oach condenses a multivariate optimization into a univariate one. But because of the subjective nature of this a, pp.oach, inexperience on the part of the user in assessing a product's desirability value may lead to inaccurate results. To compensate for this defect, a weighted desirability function is introduced which takes into consideration the vriances of the responses.

Robust design is an approach to reducing performance variation of response values in products and processes. In the Taguchl parameter design, the product-array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined-array approach, was suggested by Welch et. al. (1990) and studied by others. In these studies, only single response variable was considered. We propose how to simultaneously optimize multiple responses when there are correlations among responses, and when we use the combined-array approach to assign control and noise factors. An example is illustrated to show the difference between the Taguchi's product-array approach and the combined-array approach.

In the Taguchi parameter design, the product array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined array approach, was suggested by Welch et. al. (1990) and studied by others. In these studies, only single response variable was considered. We propose how to simultaneously optimize multiple responses when we use the combined array approach.

Abrasive water jet cutting is a recent advanced machining process effectively used for machining several materials irrespectiveof hardness. MONEL K500 is hard to machine material used for various high temperature components like aircraft enginecomponents, turbine blades, missiles, components for supercritical power plants etc. Non-traditional machining methods aremore suitable for the machining of these kinds of hard alloys and abrasive water jet machining are highly preferred andfound suitable for machining these materials in several industries. The multi-response process parameter optimization inabrasive water jet machining of MONEL K500 using composite desirability function analysis is carried out in this work. The significant process parameters are standoff distance, speed rate and abrasive flow rate. The responses considered in thiswork are surface roughness, material removal rate and kerf width. The individual desirability functions of all responses areconsidered and the weightages of each response are assigned to calculate the composite desirability. The higher compositedesirability value provides the optimum process parameters and they are validated. ANOVA analysis and S/N plots forthe responses and the composite desirability function are also carried out to predict the high influencing parameters whilemachining MONEL K500.

The present investigation is to optimize the welding parameters designed for friction stir welding (FSW) of aluminiummagnesium alloy (AA5052). The authentic configuration of automated linear friction stir welding machine used to weld theAA5052. The experiments were conducted by selecting the different welding process parameters like tool rotation speed (rpm),traverse speed (mm/min), and tool pin profiles. The pin profile made with ceramic tool type (high carbon high chromium). Taguchi based desirability function analysis engaged in establishing the optimal process parameters with multi-objectivefunction in order to maximize the tensile strength and the nugget hardness. The welding parameter of optimum level wasattained by the highest composite desirability value. An optimal level of welding parameters acquired the tool rotation speedat 1200 rpm, traverse speed at 30 mm/min, and the pentagonal tool pin profile. Further, ANOVA (analysis of variance)implemented to intimate the major impact of welding parameters on the output responses (tensile strength and nuggethardness). An outcome perceived that the tool pin profiles and tool rotational speed are the important consequence factors tomanipulate the mixed output responses. Contour plots and mean effect show that the interaction of parameters of welding onthe required output response.

In the recent years, mankind has established that the protection of the environment becomes a vital part in the discovery ofany engineering applications. Several engineering applications in the areas of agriculture, forestry, energy industries, havedeveloped several composite materials in place of other materials for its effectiveness. This work focuses on synthesis of acomposite material and the matrix material used in the research work was epoxy resin. Glass fibres were used as thereinforcement material for the preparation of the hybrid epoxy based composite. The epoxy used in this work is LB011 epoxyresin lapox B_11. The hardener used is Triethylenetetraamine (TETA). The composite laminates are prepared by varying theweight proportion of the glass fibres and tested for its mechanical properties. The composite laminate with high tensile strengthis selected for the investigation of machining properties of the composite laminate. Optimization of machining parameter arecarried out by a novel analysis called Desirability Function Analysis (DFA), which is an optimal tool considered for theproblems with multi objective optimization function.

In this study, the optimal design of the multi-cavity mold layout and packing pressure for the automobile TCU connector cover is determined based on the injection molding analysis and the desirability function method for multi-characteristic optimization. The design characteristics to be optimized are the warpage and sink marks of the product, the scrap of the feed system, and the clamping force. The optimal design is determined by performing injection molding analysis and desirability analysis for design alternatives defined by a complete combination of five mold layouts and six-level packing pressure. The optimal design shows that the desirability values for individual characteristics are quite high and balanced, and the resulting values of individual characteristics are satisfactorily low.

Taguchi method is one of the most popular approaches for design optimization such that performance characteristics become robust to uncontrollable noise variables. However, most previous Taguchi method applications have addressed a single-characteristic problem. Problems with multiple characteristics are more common in practice. The multi-criteria decision making(MCDM) problem is to select the optimal one among multiple alternatives by integrating a number of criteria that may conflict with each other. Representative MCDM methods include TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution), GRA(Grey Relational Analysis), PCA(Principal Component Analysis), fuzzy logic system, and so on. Therefore, numerous approaches have been conducted to deal with the multi-characteristic design problem by combining original Taguchi method and MCDM methods. In the MCDM problem, multiple criteria generally have different measurement units, which means that there may be a large difference in the physical value of the criteria and ultimately makes it difficult to integrate the measurements for the criteria. Therefore, the normalization technique is usually utilized to convert different units of criteria into one identical unit. There are four normalization techniques commonly used in MCDM problems, including vector normalization, linear scale transformation(max-min, max, or sum). However, the normalization techniques have several shortcomings and do not adequately incorporate the practical matters. For example, if certain alternative has maximum value of data for certain criterion, this alternative is considered as the solution in original process. However, if the maximum value of data does not satisfy the required degree of fulfillment of designer or customer, the alternative may not be considered as the solution. To solve this problem, this paper employs the desirability function that has been proposed in our previous research. The desirability function uses upper limit and lower limit in normalization process. The threshold points for establishing upper or lower limits let us know what degree of fulfillment of designer or customer is. This paper proposes a new design optimization technique for multi-characteristic design problem by integrating the Taguchi method and our desirability functions. Finally, the proposed technique is able to obtain the optimal solution that is robust to multi-characteristic performances.

We often have multiple quality characteristics to develop, improve and optimize industrial processes and products. It is not easy to find optimal control factor setting when there are multiple quality characteristics, since there will be conflict among the selected levels of the control factors for each individual quality characteristic. In this paper we propose a desirability function approach and devise a scheme which gives a systematic way of solving multiple quality characteristic problems. A numerical example is provided.

A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation as well as distance-to-target of response and response variance. The variation of process parameters amplifies the variance of responses. It is called POE (propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameters, this variability should be considered in the optimization problem. The proposed method is illustrated using a rubber product case.