A quality function deployment-based model for materials selection

Materials play a key role during the entire product design and manufacturing phase as a wrongly selected material may often lead to premature product failure causing loss of revenue and repute of the concerned manufacturing organization. While selecting the most suitable material for a specific application, the designers often need a sound and systematic methodology to deal with this problem having multiple candidate alternative choices and conflicting objectives. Most of the previously applied methodologies for material selection have either adopted criteria weights estimated using subjective judgments of the designers or failed to give due emphasis on the voice of the customers to meet their requirements. In this paper, a maiden venture is taken to solve the material selection problems using a quality function deployment (QFD)-based approach that can integrate the voice of the customers for a product with its technical requirements. The applicability and solution accuracy of this QFD-based material selection model is demonstrated with the help of four illustrative examples. To ease out the materials selection decision-making process, a user-friendly software prototype in Visual BASIC 6.0 is also developed.     

Materials selection and multi-attribute utility analysis

Summary Multi-attribute utility analysis (MAUA) has emerged as a powerful tool for materials selection and evaluation. An operations research technique, MAUA has been used in a wide range of engineering areas, of which materials science and engineering is one of the more recent. Utility analysis affords a rational method of materials selection which avoids many of the fundamental logical difficulties of many widely used alternative approaches. However, MAUA has traditionally been used in materials selection problems only, in which there is certainty regarding the attribute levels of the alternatives. For many new technologies this is not the case. Another operations research technique, subjective probability assessment (SPA), can be used to address this issue. SPA makes it possible to measure a probabilistic distribution describing the confidence of the decision maker in the levels of attributes for which there is a high degree of uncertainty. These probability distributions can be used in conjunction with MAUA to provide a consistent framework for making materials selection decisions. Furthermore, the use of these techniques extends beyond the problem of materials selection into the more speculative areas of materials competitiveness and market demand in cases involving new, unproven technologies.     

Planning of expert systems for materials selection

This paper shows a methodology for materials selection structured in five steps: 1 definition of design, 2 analysis of material properties, 3 screening of candidate materials, 4 evaluation and decision for optimal solution and 5 verification tests. The steps are illustrated with actual practical examples. The aim of article is to supply a plan for building expert system of materials selection integrated into a CAD-CAM system.     

A selection of a sensor based on the “desirability” criterion

Translated from Izmeritel'naya Tekhnika, No. 4, pp. 8–10, April, 1992.     

Optimization of propranolol hydrochloride sustained-release pellets using box-behnken design and desirability function

The objective of the present study was to evaluate three process parameters for the application of ethylcellulose films from organic solutions to obtain multi-particulate controlled drug delivery of propranolol hydrochloride. The coating process was developed in a classical coating pan. A Box-Behnken central composite design was used to evaluate the effect of the film thickness (expressed as the amount of lacquer applied on pellets' surface unit), concentration of lacquer in the coating dispersion, and the plasticizer concentration on the independent variables. Those were t85, the degree of sticking in the coating pan, and the duration of the coating process. Contour and response surface plots were depicted based on the equation given by the model. Because the results were competitive, i.e., improving one response had an opposite effect on another one, an overall desirability function was described to ameliorate the interpretation of the results. The optimization procedure generated the maximum overall desirability value. A formulation was prepared under the optimized conditions yielding response values which were close to the predicted values. To understand the mechanism of drug release from the optimized pellets various models were used to fit the dissolution data. The Higuchi model appears to provide the best correlation.     

Minimum-weight materials selection for limited available space

A systematic materials selection procedure for spatially limited, light-weight structural components is derived. The basic strategy is to utilize the available space to its full extent and introduce a free variable that accounts for the shape variation in the interior of the component necessary to meet the requirements as the material is varied. This approach leads to a specific class of objective equations and performance indices. By introducing constraint indices describing the combined demands from spatial limitations and functional requirements the performance indices attain relatively simple mathematical forms, thus facilitating the identification and ranking of viable shape-material combinations.     

A target-based normalization technique for materials selection

Ranking and selection of the optimal material is an important stage in the engineering design process. However, most of the methods proposed for ranking in materials selection have tended to focus on cost and benefit criteria, with target values receiving much less attention in spite of their importance in many practical decision-making problems such as selecting materials to best match the properties of human tissue in biomedical engineering applications. In response to this perceived gap, the development of a new normalization technique is considered in this paper that provides an extension of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and objective weighting in materials selection. There are four example cases included to validate the accuracy of outcomes from the proposed model. It is believed that the proposed decision-making model is suitable for linking to material databases and has the potential to enhance the efficiency of computer-aided materials selection systems.     

A tool for meaning driven materials selection

There are several tools used in materials selection processes by designers. However, they are mostly engineering based tools, which are dominated by numerical (or technical) material data that is mostly of use in embodiment or detailed design phases of new product development. On the other hand, product designers consider certain aspects such as product personality, user-interaction, meanings, emotions in their material decisions. In this regard, existing tools and methods do not fully support designers in their materials selection processes. This paper describes the development of a new materials selection tool holding the idea of [meaning driven materials selection]. In addition, the paper consists of a study conducted to create data for a dummy application.     

A method of steepest ascent for multiresponse surface optimization using a desirability function method

Multiresponse problems are common in product or process development. A conventional approach for optimizing multiple responses is to use a response surface methodology (RSM), and this approach is called multiresponse surface optimization (MRSO). In RSM, the method of steepest ascent is widely used for searching for an optimum region where a response is improved. In MRSO, it is difficult to directly apply the method of steepest ascent because MRSO includes several responses to be considered. This paper suggests a new method of steepest ascent for MRSO, which accounts for tradeoffs between multiple responses. It provides several candidate paths of steepest ascent and allows a decision maker to select the most preferred path. This generation and selection procedure is helpful to better understand the tradeoffs between the multiple responses, and ultimately, it moves the experimental region to a good region where a satisfactory compromise solution exists. A hypothetical example is employed for illustrating the proposed procedure. The results of this case study show that the proposed method searches the region containing an optimum where a satisfactory compromise solution exists.     

Computer program for ferrous PM materials selection

The author first describes some of the basic ferrous PM materials in production and then outlines a novel computer-based program which has been developed specifically to aid the process of their selection.     

Multiresponse optimization based on statistical response surface methodology and desirability function for the production of particleboard

It is very difficult to determine the actual level of process parameters responsible for the quality production of particleboard due to the high degree of process variable interactions and lack of robust methodology for optimization. In this study, an attempt was made to optimize the process parameters of particleboard production by using multi-response optimization process. Plackett–Burman factorial design was first employed to eliminate some factors from selected seven important parameters: flake thickness, flake length, dried chips moisture content (MC%), amount of adhesive, pressing time, pressure, and press temperature. By using this screening procedure, three important factors: flake thickness, dried chips moisture content and press temperature were found to have significant effect on particleboard properties. Afterwards, Box–Behnken design was performed as response surface methodology (RSM) with desirability functions to attain the optimal flake thickness, MC% and press temperature that affect modulus of rapture (MOR) and modulus of elasticity (MOE) of particleboard production. The optimized parameters for maximum MOR and MOE determined were found to be: flake thickness, 0.15 mm; press temperature, 182 °C; and dried chip MC% 3.5. Finally, a confirmation study was executed by using optimized levels of parameters which showed well response to the predicted model.     

An alternative desirability function for achieving ‘six sigma’ quality

To achieve high process yields or ‘six sigma’ quality, engineers often need to evaluate and optimize processes that are characterized by multiple quality characteristics. Existing desirability functions weigh together multiple objectives but they have a number of limitations. Most importantly, available desirability functions do not explicitly account for the combined effect of the mean and the dispersion of the quality characteristic. Therefore, it is easy to incur excessive expenditures or unknowingly to fail to achieve targeted yields. In this paper, a desirability function is proposed that addresses these limitations. This function conservatively estimates the ‘effective yield’ under assumptions described in the ‘six sigma’ literature. We use an arc‐welding application to illustrate how the proposed desirability function can yield a substantially higher level of quality as well as a more accurate assessment of the process capability than available alternatives. We suggest that the proposed desirability function should be used to facilitate multicriterion optimization when dispersion data are available. Copyright © 2003 John Wiley & Sons, Ltd.     

Computer aided materials selection and design

The design of an end-product and the choice of the material from which it should be made are interdependent activities which nevertheless have separate and different requirements for supporting data. The provision of such data is not quite as straightforward as it might seem at first sight, mainly because the properties of a moulded item vary from point to point and differ from the properties exhibited by idealized specimens. At the very least this complicates the task of evaluation and on occasions it leads to the charge that the data provided bear little relation to service performance, but such situations are common whenever complex materials are used in exacting applications and are in no sense peculiar to plastics and the automotive industry which are the focus of this paper. Our comprehension of the materials is continually enhanced by new research results and the means by which that comprehension can be translated into data of value to the end-user industries improve in step with the advances in computer technology.One can now envisage an efficient flow of pertinent data across the interface between the end-user industries and the plastics industry. The first steps towards that ideal were discussed in two papers presented at SITEV 79^1,2; they described new results emerging from work in the research laboratories of thee Plastics Division¹ of ICY that offered a possible route to data that are both more concise and more relevant to downstream applications than earlier data described ways in which such data and more iraditional information can be manipulated by computer and “tailored” to the specific needs of particular users. This new paper develops that theme further and possibly marks the point at which responsibility for later developments should pass from research workers to those professionally concerned with the transfer of information from one group to another, for there now exists a prototype data system. The only issues to be resolved now are what information should be incorporated rather than how should the task be done. The paper is concerned primarily with the data and the underlying rationale rather than with the software that controls the various operations.     

Applications of the concept of J-integrals for calculation of generalized forces

Summary The J-integral technique is used to assess the risk of defects in a particular structure by explicit computation of the arising generalized forces. Three different situations are considered: stress concentrators in cycloidal specimens, the interaction of a dislocation with a circular inclusion, and the interaction between two dislocations. Special emphasis is given to an effective but analytical calculation of the corresponding path integrals.Dedicated to Professor Dr. rer. nat. Dr. h. c. Ingo Müller on the occasion of his 60th birthday     

Challenges in materials and process selection

Recent developments are outlined of materials and process selection methods leading towards industrial applications. The systematic implementation of selection for multiple criteria and multiple design elements is presented as a natural extension of Ashby's method. The importance of a “pre-defined questionnaire” approach in process selection is illustrated for casting, joining and surface treatments. The use of materials selection methods for materials and multi-materials development is described for the case of glass, composite materials and sandwich structures, and the application of various optimization techniques adapted to each problem is given. In conclusion, possible new developments toward a better integration of materials and process selection in the whole design procedure are proposed.     

Ecological criteria for the selection of materials in fatigue

This work provides some ecological criteria for fatigue designers so that they can quantitatively consider the ecological impact as a factor during the process of design. In particular, during the selection of materials for fatigue applications, two kinds of applications have been examined. The first one is related to components under cyclic loading with no energy consumption during their use, as a pipe under variable pressure. In this case, the highest impact takes place during the material production phase. The second application refers to components used in means of transport, as planes or cars components. For them, the highest environmental impact occurs during the use phase, through fuel consumption. For both applications, a parameter is provided, named Ecological‐Fatigue Factor, that combines both the ecological impact and the fatigue endurance of the materials. For the two applications, a ranking of materials based on the Ecological‐Fatigue Factor is given. Great differences can be found between the two rankings.     

The assessment and evaluation of property data for materials selection purposes

Special requirements of property data for materials selection are analysed. The need for information about usage, manufacturing and availability properties has been emphasised. To be able to handle a typical situation where large numbers of materials and properties are involved, it is suggested that simplifications are introduced concerning technological properties such as corrosion resistance, machinability etc. The representation of properties at different levels of precision is introduced. The lowest level would typically involve a simple rating from 0–5. At the higher levels precise values are given for individual environments or processes. Representation of properties in such a way that data for different materials are directly comparable for many properties has been proposed. It is also important that data of different accuracy are distinguished and a guideline for ranking of accuracy of data sources is given.     

A desirability function method for optimizing mean and variability of multiple responses using a posterior preference articulation approach

A desirability function approach has been widely used in multi‐response optimization due to its simplicity. Most of the existing desirability function‐based methods assume that the variability of the response variables is stable; thus, they focus mainly on the optimization of the mean of multiple responses. However, this stable variability assumption often does not apply in practical situations; thus, the quality of the product or process can be severely degraded due to the high variability of multiple responses. In this regard, we propose a new desirability function method to simultaneously optimize both the mean and variability of multiple responses. In particular, the proposed method uses a posterior preference articulation approach, which has an advantage in investigating tradeoffs between the mean and variability of multiple responses. It is expected that process engineers can use this method to better understand the tradeoffs, thereby obtaining a satisfactory compromise solution.