An intelligent approach to robust multi-response process design

In order to meet strict customer demands in a global highly-complex industrial sector, it is necessary to design manufacturing processes based on a clear understanding of the customer's requirements and usage of a product, by translating this knowledge into the process parameter design. This paper presents an integrative, general and intelligent approach to the multi-response process design, based on Taguchi's method, multivariate statistical methods and artificial intelligence techniques. The proposed model considers process design in a general case where analytical relations and interdependency in a process are unknown, thus making it applicable to various types of processes, and incorporates customer demands for several (possible correlated) characteristics of a product. The implementation of the suggested approach is presented on a study that discusses the design of a thermosonic copper wire bonding process in the semiconductor industry, for assembly of microelectronic devices used in automotive applications. The results confirm the effectiveness of the approach in the presence of different types of correlated product quality characteristics.     

Optimal design of multi-response experiments using semi-definite programming

Optimal design of multi-response experiments for estimating the parameters of multi-response linear models is a challenging problem. The main drawback of the existing algorithms is that they require the solution of many optimization problems in the process of generating an optimal design that involve cumbersome manual operations. Furthermore, all the existing methods generate approximate design and no method for multi-response n-exact design has been cited in the literature. This paper presents a unified formulation for multi-response optimal design problem using Semi-Definite Programming (SDP) that can generate D-, A- and E-optimal designs. The proposed method alleviates the difficulties associated with the existing methods. It solves a one-shot optimization model whose solution selects the optimal design points among all possible points in the design space. We generate both approximate and n-exact designs for multi-response models by solving SDP models with integer variables. Another advantage of the proposed method lies in the amount of computation time taken to generate an optimal design for multi-response models. Several test problems have been solved using an existing interior-point based SDP solver. Numerical results show the potentials and efficiency of the proposed formulation as compared with those of other existing methods. The robustness of the generated designs with respect to the variance-covariance matrix is also investigated.     

The use of nested desirability functions and quality indices for multi-response robust parameter design problems

A multi-response robust parameter design (RPD) problem-solving technique based on desirability functions is presented in which the means and variances of all responses are placed on a level playing field. The proposal allows a decision maker to integrate their preferences for the individual means and variances. It is shown that the ensuing operating point is a system setting that produces a mutually robust set of responses. In addition, this article offers an approach to assess several RPD strategies via quality indices. The measures presented here allow for a more knowledgeable and comprehensive evaluation of the competing RPD strategies.     

Application of semi-definite programming to the design of multi-response experiments

The need to be able to design experiments with multiple responses is becoming apparent in many real-world applications. The generation of an optimal design to estimate the parameters of a multi-response model is a challenging problem. Currently available algorithms require the solution of many optimization problems in order to generate an optimal design. In this paper, the problem of multi-response D-optimal design is formulated as a semi-definite programming model and a relaxed form of it is solved using interior-point solvers. The main advantage of the proposed method lies in the amount of computation time taken to generate a D-optimal design for multi-response models. The proposed method is tested on several test problems and is shown to be very efficient with optimal designs being found very quickly in all cases. The robustness of the generated designs with respect to the variance-covariance matrix is also assessed for the test problems in order to show how a sensitivity analysis can be performed. The characteristics of the proposed method are also compared with those of other existing methods.     

Optimization of correlated multi-response quality engineering by the upside-down normal loss function

Most of the published literature on robust design is basically concerned with a single response. However, the reality is that common industrial problems usually involve several quality characteristics, which are often correlated. Traditional approaches to multidimensional quality do not offer much information on how much better or worse a process is when finding optimal settings. Köksoy and Fan [Engineering Optimization 44 (8): 935–945] pointed out that the upside-down normal loss function provides a more reasonable risk assessment to the losses of being off-target in product engineering research. However, they only consider the single-response case. This article generalizes their idea to more than one response under possible correlations and co-movement effects of responses on the process loss. The response surface methodology has been adapted, estimating the expected multivariate upside-down normal loss function of a multidimensional system to find the optimal control factor settings of a given problem. The procedure and its merits are illustrated through an example.     

Characterizing and optimizing multi-response processes by the taguchi method

In this paper we demonstrate the ability of the Taguchi technique accurately to characterize and successfully to optimize complicated multi-response processes with the minimum of experiments, provided one uses simple statistical techniques which can ensure valid, and definitive results. We point out the usefulness of suitable data-transformations, and we suggest a systematic procedure for establishing the optimal operating conditions and for carrying out confirmatory experiments. For the particular case detailed in this paper (which is typical of multi-response processes) the Taguchi technique achieved an improvement in uniformity of a factor of 2, together with optimized process control.     

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.     

Optimizing multi-response surface problems: How to use multi-objective optimization techniques

A common problem encountered in product or process design is the selection of optimal parameter levels that involves the simultaneous consideration of multiple response characteristics, called a multi-response surface problem. Notwithstanding the importance of multi-response surface problems in practice, the development of an optimization scheme has received little attention. In this paper, we note that Multi-Response surface Optimization (MRO) can be viewed as a Multi-Objective Optimization (MOO) and that various techniques developed in MOO can be successfully utilized to deal with MRO problems. We also show that some of the existing desirability function approaches can, in fact, be characterized as special forms of MOO. We then demonstrate some MOO principles and methods in order to illustrate how these approaches can be employed to obtain more desirable solutions to MRO problems.     

Production system design for quality robustness

In automotive assembly plants, vehicles with defects are either repaired (e.g., components are exchanged, scratches are polished, etc.) or reworked (e.g., the whole vehicle is repainted) to maintain high product quality. The performance of vehicle quality is typically characterized in terms of the first time quality and also the quality buy rate. First time quality is defined as the good job ratio of all first time processing jobs, while the quality buy rate is the good job ratio of all processed jobs, including the first time jobs and reworked jobs.

In this paper, we study a repair and rework system at an automotive paint shop with random first time quality. Specifically, we show that paint quality, in terms of quality buy rate, can be described by a function of repair capacity and first time quality. Increasing the repair capacity can improve the quality buy rate and reduce unnecessary repaints. Variations in first time quality may lead to a reduction in the quality buy rate and an increase in unnecessary repaints, and consequently, a substantial waste of production capacity and materials. In addition, we observe that the average quality buy rate depends primarily on the mean and coefficient of variation of the first time quality rather than its complete distribution. Based on these results, we introduce the notion of quality robustness and show that the design of a production system should accommodate randomness in first time quality to achieve a robust quality buy rate. Finally, a case study on a repair and rework system redesign to improve paint quality is presented.     

QS 9000质量体系与产品设计质量控制

ＱＳ 90 0 0质量体系标准是美国福特、通用和克莱斯勒三大汽车公司 (以下简称三大公司 )在ＩＳＯ 90 0 0标准的基础上联合推出的质量标准 ,受到国际汽车界的广泛重视 ,因此 ,初步了解ＱＳ 90 0 0有关产品设计方面的标准 ,对提高产品设计素质 ,规范产品设计开发活动大有益处 ,并有益于ＧＢ 190 0 0的宣贯和认证工作的开展。1 ＱＳ 90 0 0质量体系与ＩＳＯ 90 0 0ＩＳＯ 90 0 0的推出 ,受到国际社会各行业的普遍认可 ,美国三大公司为了全球战略的需要 ,在ＩＳＯ 90 0 0的基础上协调一致 ,携手推出了ＱＳ 90 0 0质量体系。其目的是针对三大公…     

Multivariate tolerance design using quality loss

The determination of tolerance allocations among design parameters is an integral phase of product/process design. Such allocations are often necessary to achieve desired levels of product performance. We extend our prior research on tolerance allocation by developing both parametric and nonparametric methods for a multivariate set of performance measures that are functions of a common set of design parameters. The parametric method is novel and assumes full information about the probability distribution of design parameter processes. The proposed nonparametric method assumes that only partial information is available and significantly extends prior research by considering a more contemporary and realistic model for manufacturer costs. For both methods we derive economically based models that represent the costs, both internal (supplier) and external (manufacturer), of tolerance allocation under several different process scenarios. These scenarios are based on the manner of disposition of nonconforming product. For the parametric methods we derive tolerance allocation solutions that jointly minimize expected total cost of the supplier and manufacturer. For the nonparametric methods we derive solutions for tolerance allocation that jointly minimizes the maximum expected total cost. An example in the fabrication of a rubber tread compound is used to: (i) demonstrate the implementation of our proposed methodologies for tolerance allocation; (ii) illustrate and compare the nonparametric and parametric methods; land iii) assess the sensitivity of optimal tolerance allocations to changes in process model types, cost coefficient estimates, and manner of disposition of nonconforming product.     

电能质量分析仪系统设计

为改善现有电能质量分析仪产品在功能完善性、稳定性和测试精度等方面存在的不足,该文采取基于ARM+DSP+FPGA的三核架构研制一款功能全面的电能质量分析仪。在前段电力信号调理电路的设计中采取全硬件控制的测量电路使其具有更高的精度、带宽、响应速度以及更大的动态范围;在同步采样方案设计中将数字PI控制引入锁相环代替传统的环路滤波器,有效提高频率锁定的速度和稳定度。通过对各项性能的测试发现,各种电能参数的测量结果均达到相关的测量标准。     

Linking quality citizenship to process design: a quality management perspective

As organisations emphasise their role in society, there is a need to determine the effect of socially responsible practices on organisational processes and firm performance. Although the effect of corporate social responsibility on firm performance has been addressed from different perspectives, little is known about the influence of corporate social responsibility on organisational processes and internal operations of firms. The paper aims to make a link between corporate social responsibility, quality management practices and quality outcomes through emphasising quality citizenship as one of the practices associated with quality management. This paper provides a new perspective on the link between quality citizenship and product/process design within the quality management framework. The stakeholder theory of the firm is used to explain the formation and development of new management practices within a firm. Data were collected from managers in the petroleum industry to examine the role of quality citizenship on design and development of organisational processes. The results show that quality citizenship has a significant effect on employee involvement and an indirect effect on process design. Quality citizenship enables firms to obtain a much broader perspective on product/process design and facilitates design and development of products, services and processes that meet the needs of a broader set of customers – the stakeholders.     

产品设计文件质量的有效管理

产品开发是一项科学而严谨的工作,只有不断地对产品开发实践经验进行归纳、总结,才能在新的产品开发中系统、有效地进行策划,以达到"以最经济的方式生产出适合买方要求的产品"的境界.     

Warranty and redundancy design with uncertain quality

When products of an uncertain quality are sold with a warranty, the expected warranty cost depends on the reliability of the product and on the quality uncertainty. This cost can be reduced through the use of redundancy and/or quality control techniques. However, these routes increase the manufacturing cost. This paper develops a model to determine the optimal redundancy and quality control strategies that achieve a trade-off between the two costs.     

Managing product design quality under resource constraints

Based on quality function deployment (QFD), we develop a simple, intuitive method, formulated as constrained non-linear programming for managing product development for obtaining the best quality product under dynamic resource constraints. The model can respond to changes in the environment including changes in customers' desires, introduction of new technologies, reduction of budget, or changes in the company workforce. Our model improves existing models by incorporating realistic cost functions and allowing its continuous use throughout the project. While using QFD, we also extend the original technique to remove a shortcoming in the roof of the house of quality (HoQ). We give a criterion for guaranteeing global optimality and briefly describe the use of the method in a real project.     

The use of a grey-based Taguchi method for optimizing multi-response simulation problems

Simulation modelling is a widely accepted tool in system design and analysis, particularly when the system or environment has stochastic and nonlinear behaviour. However, it does not provide a method for optimization. In general, problems contain more than one response, which are often in conflict with each other. This article proposes a grey-based Taguchi method to solve the multi-response simulation problem. The grey-based Taguchi method is based on the optimizing procedure of the Taguchi method, and adopts grey relational analysis (GRA) to transfer multi-response problems into single-response problems. A practical case study from an integrated-circuit packaging company illustrates that differences in performance of the proposed grey-based Taguchi method and other methods found in the literature were not significant. The grey-based Taguchi method thus provides a new option when solving a multi-response simulation-optimization problem.     

The house of quality in a design process

The product development process is subject to randomness of duration of design activities and a path followed through the network of activities. In this paper, the qualitative relationships between various attributes of the design process and the corresponding design process variables are captured using the house of quality and then transformed into quantitative relationships. The impact of the process control variables on the design process attributes is discussed. The problem of determining optimal values of the design process variables to maximize the combined quality index of the critical design activities is modelled as a geometric programming problem.     

Modeling the design quality competition for durable products

In this paper we model the design quality competition in durable product markets. We use a duopolistic non-cooperative game theoretic framework. Each firm controls the design quality level and the price of its product. The resulting control theoretic model is solved under certain functional assumptions to describe the Nash equilibrium design quality and pricing strategies of the two competitors. We compare open and closed loop design quality and pricing strategies. We use an extensive set of numerical experiments to investigate the effects of various parameters of the model on the optimal policies. We conclude with a brief summary of our results and some useful managerial insights derived from the model.