Robust product development for multiple quality characteristics using computer experiments and an optimization technique

Conventional parameter or tolerance designs focus on developing exact methods to minimize quality loss or manufacturing cost. The inherent assumption is that the response functions which represent the link between controllable variables and response values of quality characteristics are known before a design is developed. Moreover, parameter and tolerance values are assumed to be independent controllable variables in previous works; namely, they are determined separately in design activities. Currently, advanced computer software, such as computer-aided engineering, can help engineers to handle design problems with unknown response functions, at the stage of product design and process planning. Therefore, in this study, the software ANSYS was employed to obtain simulation data which represent the response values of quality characteristics. These response values will be used to fit a set of response functions for later analysis. However, previous works in computer simulation for design and planning usually lack consideration of the noise impact from an external design system. To approximate a realistic design environment, various levels of controllable variables, in conjunction with artificial noises created from uncontrollable variables, are used to generate simulated data for statistical analysis via Response Surface Methodology (RSM). Then, an optimization technique, such as mathematical programming, is adopted to integrate these response functions into one formulation so that optimal parameter and tolerance values are concurrently determined, with multiple quality characteristics taken into consideration. A bike-frame design was used to demonstrate the presented approach, followed by multiple quality characteristics of interest: material cost, bike-frame weight, structure reliability, and rigidity dependability. The goal is to minimize material cost and bike frame weight and to maximize structure reliability and rigidity dependability. This approach is useful for solving any complex design problems in the early stages, while providing enhanced functionality, quality, economic benefits, and a shorter design cycle.     

Robust computer-aided parameter and tolerance determination for an electronic circuit design

Conventionally, parameter design precedes tolerance design in the course of product design or process planning. To lower the production costs, as well as to improve quality, this study proposes the simultaneous determination of parameter and tolerance values when designing an electronic circuit. With the current development of CAD (Computer-Aided Design) software for electronic circuit design, engineers can determine parameter and tolerance values without providing transfer functions for circuit analysis. In this study, a computer experiment is performed by using CAD software (PSpice) to obtain outputs that will be converted into the total cost, which includes the quality loss, the tolerance cost and the failure cost. Then, Response Surface Methodology (RSM) is employed to minimize the total cost and to find the optimal parameter and tolerance values statistically. Consequently, a parameter and tolerance design for quality improvement and cost reduction can be achieved for any complex electronic circuit during the early stages of design.     

方差分析法在鲁棒公差设计中的应用

目前公差优化设计主要在于降低加工成本,而很少考虑公差对制造加工的敏感度问题。为此,在公差设计中采用了方差分析法,通过对加工模拟所产生的一系列数据的分析,找出对封闭环有显著影响的设计变量,并提高该设计变量的公差要求,从而减小封闭环对加工变动的敏感度,实现面向质量的鲁棒公差设计。     

Economic tolerance design for quality

This paper provides a few general mathematical models for determining product tolerances which minimize the combined manufacturing costs and quality loss. The models contain quality cost with a quadratic loss function and represent manufacturing costs with geometrical decay functions. The models are also formulated with multiple variables which represent the set of characteristics in a part. Applications of these models include minimizing the total cost with effective tolerance allocation in product design.     

A new tolerance design method for a secondary rechargeable battery using design of experiments with mixture

Mixing errors in the manufacturing process of a mixture may cause a sizeable variation in the performance of the product, leading to the need for the tolerance design. Even though a variety of procedures have been proposed for the optimal tolerance design based on quality loss and manufacturing costs, there are no available tolerance design methods when mixing errors exist in the manufacturing process of a mixture. In this article, we propose a new tolerance design method for the case where mixing errors are involved in massive manufacturing process of a secondary rechargeable battery. Using an approximation method, we derive quality loss function, reflecting the effects of mixing errors on the product performances. Statistical design of mixture experiments is applied to build empirical models of performances as functions of component proportions in the corresponding quality loss function. A real‐life case study on the tolerance design of a secondary battery is provided for the illustration of the proposed method. The results show the efficiency of the proposed method in designing the tolerances to minimize the quality loss and manufacturing costs. Copyright © 2008 John Wiley & Sons, Ltd.     

Product Robust Design and Process Robust Design: Are They the Same? (No.)

Taguchi's ideas of robust parameter design motivated the development of the dual-response approach, where both the mean and the variance of the quality response are modeled in terms of the design parameters and noise factors. These are then used to identify optimal settings that achieve the dual objective of optimizing the signal (the mean) and minimizing variation. While much research has been published recently with regard to how to solve the dual-response problem (DRP), relatively little attention has been given to the unique characteristics of process robust design, like the existence of systematic variation or intercorrelations among the process “controllable” variables. These properties indeed put process robust design in a category of its own (separate from product robust design). In this paper, we first expound these unique properties and develop a general formulation of the DRP as it applies to process robust design. We then report on an implementation to an industrial process in a high-tech corporation.     

Parameter and tolerance design in the engineering modelling stage

Generalized parameter and tolerance design problems have been formulated as nonlinear optimization problems under a broader set of assumptions. A new approach for parameter design and tolerance design problems is outlined. This approach integrates engineering models and numerical optimization methods so it can work in the early stage of design where a good engineering model is available to simulate the real product or process. The new approach is also able to handle multiple quality characteristics and constraints. Several important theoretical results have been derived by the authors for tolerance design problems that could serve as guidelines for optimal tolerance design and tolerance distribution.     

Optimization of Correlated Multiple Quality Characteristics Using Desirability Function

A real problem in a product or process usually possesses multiple quality characteristics. For the multiple quality characteristics optimization problem, the most popular method for simultaneous quality characteristics optimization is the desirability function approach. However, the variation and correlation between quality characteristics are usually ignored in this approach. The variation reduction through robust design introduced by Taguchi is a major concept. This research presents an approach to optimizing the correlated multiple quality characteristics based on the modified double-exponential desirability function. The implementation and the effectiveness of the proposed approach are illustrated through two examples from previously published articles.     

Quality risk management in pharmaceutical development

The objective of ICH Q8, Q9 and Q10 documents is application of systemic and science based approach to formulation development for building quality into product. There is always some uncertainty in new product development. Good risk management practice is essential for success of new product development in decreasing this uncertainty. In quality by design paradigm, the product performance properties relevant to the patient are predefined in target product profile (TPP). Together with prior knowledge and experience, TPP helps in identification of critical quality attributes (CQA’s). Initial risk assessment which identifies risks to these CQA’s provides impetus for product development. Product and process are designed to gain knowledge about these risks, devise strategies to eliminate or mitigate these risks and meet objectives set in TPP. By laying more emphasis on high risk events the protection level of patient is increased. The process being scientifically driven improves the transparency and reliability of the manufacturer. The focus on risk to the patient together with flexible development approach saves invaluable resources, increases confidence on quality and reduces compliance risk. The knowledge acquired in analysing risks to CQA’s permits construction of meaningful design space. Within the boundaries of the design space, variation in critical material characteristics and process parameters must be managed in order to yield a product having the desired characteristics. Specifications based on product and process understanding are established such that product will meet the specifications if tested. In this way, the product is amenable to real time release, since specifications only confirm quality but they do not serve as a means of effective process control.     

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.     

Combined parameter and tolerance design optimization with quality and cost

The need to remain competitive for survival in the current world market has led manufacturing sectors to consider the low cost and high quality of product and process design. Production of quality products at low cost in today's manufacturing industry requires simultaneous consideration of product design and process planning, particularly in the early stage of design and planning. During product design, parameter design determines the design target (design setting) and tolerance design determines design tolerance. During process design, the parameter design determines the process mean (process setting) and tolerance design determines process tolerance. This study provides a mathematical relationship to link the elements of design target, design tolerance, process mean and process tolerance in one equation. By following this equation, manufacturability for all possible combinations of product and process design can be ensured, which increases the flexibility of both product design and process planning. With this in mind, an analysis model that includes manufacturing cost and quality loss simultaneously has been developed to determine the optimal values of design tolerance, process mean and process tolerance. The proposed model provides a method of combining the optimization of parameter and tolerance design over product/process in the early stage of design.     

Off-line quality control via yield constrained variability minimization in circuit design

During the lifetime of any system, e.g. an electronic circuit, sources of variation of parameters include fabrication, operational and environmental (FOE) variables. Since these sources of variation are not under designer control, one important objective at the product design stage is to reduce rather than control their influence. The aim of this paper is to present a methodology whereby settings of system parameters which make product performance less sensitive to FOE variations are identified. By so doing, reliability (consistency of acceptable performance), and hence quality, is enhanced. The approach taken is to minimize performance variability subject to a constraint on yield. This objective ensures consistency of performance while the constraint ensures acceptability of performance. Multiple performances are handled via a weighted sum objective. The Monte Carlo approach ensures that any parameter probability density function is handled and that computational cost does not increase with system dimensionality. The effectiveness of the technique is illustrated via a practical system example—an electronic circuit having 11 design parameters.     

Statistical modeling for the optimal deposition of sputteredpiezoelectric films

The quality of sputtered-deposited piezoelectric films used for integrating bulk acoustic wave (BAW) and surface acoustic wave (SAW) devices with semiconductor circuitry depends on several deposition parameters, including substrate temperature, background pressure, gas composition, gas flow rate, and deposition rate. It is desirable to establish the fabrication process based on a selection of the controllable parameter values that optimizes the film quality. It is common practice to perform a number of deposition experiments by varying the controllable parameters to determine the optimal film growth conditions. The films are grown under a number of different conditions within this space and a response parameter related to film performance is measured. Then a multiple linear regression model is fit to the data. By optimizing the fitted response, the best growth conditions can be obtained. This approach is illustrated with data from recent work on the development of very high quality magnetron sputtered aluminum nitride (AlN) films whose acoustic characteristics are like those of epitaxial films grown at considerably higher substrate temperatures. Because the resource cost involved can be high, depending upon the number of deposition runs made, it is desirable to minimize the number of experiments and maximize the amount of information gained from them. A discussion is given on how the statistical theory of experimental design can be used to obtain this goal     

Bivariate tolerance design for lock wheels by considering quality loss

In the design of tolerance allocation the cost–tolerance function is usually employed to represent the objective function which is to be minimized. The traditional cost–tolerance functions in the literature are concerned with only one characteristic. In this paper we obtain a bivariate cost–tolerance function to describe the relationship between the cost and tolerances of two characteristics (i.e. the thickness and inner diameter) of a lock wheel. Then the bivariate loss function is combined with the bivariate cost–tolerance function to determine the optimal tolerances for the thickness and inner diameter of a lock wheel such that the user's potential loss/cost may be evaluated. When the quality loss is considered, the tolerances of both characteristics become tighter. By including the effect of product degradation, the present work of expected bivariate quality loss is then introduced as a quality performance measure. By assuming linear drifts on both the thickness and inner diameter of the lock wheels, the model with the present worth of quality loss leads to tighter tolerances of both characteristics. In addition, a longer planning horizon (or a longer useful life of the product) leads to tighter tolerances and a larger user's discount rate results in looser tolerances for both characteristics. Copyright © 2000 John Wiley & Sons, Ltd.     

Combining iterative heuristic optimization and uncertainty analysis methods for robust parameter design

A number of investigators have pointed out that products and processes lack quality because of performance inconsistency, which is often due to uncontrollable parameters in the manufacturing process or product usage. Robust design methods are aimed at finding product/process designs that are less sensitive to parameter variation. Robust design of computer simulations requires a large number of runs, which are very time consuming. A novel methodology for robust design is presented in this article. It integrates an iterative heuristic optimization method with uncertainty analysis to achieve effective variability reductions, exploring a large parameter domain with an accessible number of simulations. To demonstrate the effectiveness of this methodology, the robust design of a 0.15 μm CMOS device is shown.     

A Novel Approach to Simultaneous Robust Design of Product Parameters and Tolerances Using Quality Loss and Multivariate ANOVA Concepts

Design and development of high quality products are of utmost importance to any production plant. Product design consists of parameter design and tolerance design, which affect the product performances and the manufacturing costs, respectively. Most products involve more than one quality feature. Design and development of such products raise multi‐response surface problems in which it is necessary to determine the optimal values of parameters and the tolerances for all responses simultaneously. In this research, an approach for simultaneous robust parameter and tolerance design is proposed to deal with multi‐response problems. The proposed method employs quality loss concept and one‐way multivariate analysis of variance. Two simulation studies are performed to validate the applicability of the proposed method. Research findings show that the proposed method performs better in quality improvement as well as in cost reduction than the existing methods. The variances of the responses are also lower than those of the other methods, that is, the proposed method results in a more robust approach to product design. Copyright © 2016 John Wiley & Sons, Ltd.     

Single- and multiobjective optimization problems in robust parameter design

This paper reviews the evolution of off-line quality engineering methods with respect to one or more quality criteria, and presents some recent results. The fundamental premises that justify the use of robust product/process design are established with an illustrative example. The use of designed experiments to model quality criteria and their optimization is briefly reviewed. The fact that most design-for-quality problems involve multiple quality criteria motivates the development of multiobjective optimization techniques for robust parameter design. Two situations are considered: one in which response surface models for the quality characteristics can be obtained using regression and considered over a continuous factor space, and one in which the problem scenario and the experiment permit only discrete parameter settings for the design factors. In the former scenario, a multiobjective optimization technique based on the reference-point method is presented; this technique also incorporates an inference mechanism to deal with uncertainty in the response surface models caused by finite, noisy data. In the discrete-factors scenario, an efficient method to reduce computational complexity for a class of models is presented.     

QFD optimization using linear physical programming

A new approach to quality function deployment (QFD) optimization is presented. The approach uses the linear physical programming (LPP) technique to maximize overall customer satisfaction in product design. QFD is a customer-focused product design method which translates customer requirements into product engineering characteristics. Because market competition is multidimensional, companies must maximize overall customer satisfaction by optimizing the design of their products. At the same time, all constraints (e.g. product development time, development cost, manufacturing cost, human resource in design and production, etc.) must be taken into consideration. LPP avoids the need to specify an importance weight for each objective in advance. This is an effective way of obtaining optimal results. Following a brief introduction to LPP in QFD, the proposed approach is described. A numerical example is given to illustrate its application and a sensitivity analysis is carried out. Using LPP in QFD optimization provides a new direction for optimizing the product design process.     

Development of a sequential optimization procedure for robust design and tolerance design within a bi-objective paradigm

Many practitioners and researchers have implemented robust design and tolerance design as quality improvement and process optimization tools for more than two decades. Robust design is an enhanced process/product design methodology for determining the best settings of control factors while minimizing process bias and variability. Tolerance design is aimed at determining the best tolerance limits for minimizing the total cost incurred by both the customer and manufacturer by balancing quality loss due to variations in product performance and the cost of controlling these variations. Although robust design and tolerance design have received much attention from researchers and practitioners, there is ample room for improvement. First, most researchers consider robust design and tolerance design as separate research fields. Second, most research work is based on a single quality characteristic. The primary goal of this paper is to integrate a sequential robust design–tolerance design optimization procedure within a bi-objective paradigm, which, the authors believe, is the first attempt in the robust design and tolerance design literature. Models are proposed and numerical examples along with sensitivity analysis are performed for verification purposes.     

论产品拆解与绿色设计方法问题

基于产品设计资料,引入产品结构树型图、产品可拆解性和能源回收指数等概念,评估了产品的可拆解性和能源回收性;构建了合并材料和整合产品功能单元2种绿色优化设计方法。以四川绵阳某企业B型超声仪器为例,通过优化设计前后产品零部件数量、拆解步数、拆解时间和能源回收指数的对比,验证了该方法的有效性。