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.     

A review of the ‘taguchi methods’ for off-line quality control

The Taguchi methods have recently become popular in the U.S.A following a realization of their importance in Japanese quality design. This review is an initial attempt to extract the important ideas while drawing on the ‘Western’ experience with response surface methodology and experimental design.     

Application of Taguchi methods to surface mount processes

This paper describes the implementation of a Taguchi experiment for the placement of surface mount components as part of a process at Mars Electronics. The justification for the experimental work, the practical problems in its implementation and the significance of the results obtained are described. The experiment is seen as part of a programme and the implications of the results obtained for subsequent experimentation are discussed.     

ROBUST DESIGN BY VARIABILITY OPTIMIZATION

A deterministic method of robust design against design parameter uncertainty is described. The method consists of the definition of a variability function for the performance measure under consideration, based on known or assumed uncertainties in the parameters of the measure and its known dependency on these parameters. It is assumed that the design parameters are subject to constraints on their individual values and interactions and that the performance measure (or output variable) is required to have a specified nominal design point value when the design parameters are at their chosen nominal values. An algorithm is then described which determines that set of nominal parameter values which results in a minimum value of the variability function given the specified constraints. The algorithm is applied to two examples, firstly in analytic form to demonstrate the procedure and then numerically to specific design problems. The results are discussed in relation to a probable design strategy and with regard to the advantages and limitations of the method employed. © 1997 by John Wiley & Sons, Ltd.     

机器人仿真与离线编程系统

介绍了我们自行研制开发的机器人仿真与离线编程系统软件。该软件为机器人的研究与设计提供了良好的环境，可用于机器人的本体设计、作业单元布局、路径规划与碰撞检验、任务规划与作业任务的离线编程，可实现多个运动机构间的相互通讯，并在屏幕上动态模拟显示整个作业的工作过程。该系统采用全局监控菜单，为用户提供了良好的界面。     

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.     

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.     

Production and quality control policies for deteriorating manufacturing system

In this paper, a novel model for a deteriorating manufacturing system is analysed, considering repairs and overhauls of random durations. The machine manufactures one product and the model is further complicated because the quality of the parts’ produced deteriorates according to the wear of the machine and human interventions. When a breakdown occurs, either a repair or an overhaul is performed. The machine is restored to as-good-as-new conditions if the overhaul is selected, and conversely, its condition deteriorates following repairs. Multiple operational states are considered to define an ageing process. The decision variables of the model are the production rate and the repair/overhaul switching strategy. This paper provides new insights to this research area by considering the simultaneous production and repair/overhaul control policy under the effect of deteriorations. The optimal decision policy minimises the total incurred cost comprising the inventory, backlog, repair and overhaul costs over an infinite planning horizon. Our paper differs from other research projects in its consideration of the machine’s history, defined by the number of repairs and multiple operational states. A numerical example is given to illustrate the proposed approach and a sensitivity analysis has been conducted to confirm the structure of the obtained control policies.     

A comparison of deterministic and statistical sampling techniques for quality analysis of integrated circuits

There has been a great amount of publicity about Taguchi methods which employ deterministic sampling techniques for robust design. Also given wide exposition in the literature is tolerance design which achieves similar objectives but employs random sampling techniques. The question arises as to which approach—random or deterministic—is more suitable for robust design of integrated circuits. Robust design is a two-step process and quality analysis—the first step—involves the estimation of ‘quality factors’, which measure the effect of noise on the quality of system performance. This paper concentrates on the quality analysis of integrated circuits. A comparison is made between the deterministic sampling technique based on Taguchi's orthogonal arrays and the random sampling technique based on the Monte Carlo method, the objective being to determine which of the two gives more reliable (i.e. more consistent) estimates of quality factors. Results obtained indicated that the Monte Carlo method gave estimates of quality which were at least 40 per cent more consistent than orthogonal arrays. The accuracy of prediction of quality by Taguchi's orthogonal arrays is strongly affected by the choice of parameter quantization levels—a disadvantage—since there is a very large number (theoretically infinite) of choices of quantization levels for each parameter of an integrated circuit. The cost of the Monte Carlo method is independent of the dimensionality (number of designable parameters), being governed only by the confidence levels required for quality factors, whereas the size of orthogonal array required for a given problem is partly dependent on the number of circuit parameters. Two integrated circuits—a 7-parameter CMOS voltage reference and a 20-parameter bipolar operational amplifier—were employed in the investigation. Quality factors of interest included performance variability, acceptability (relative to customer specifications) and deviation from target.     

信息系统的测试与质量控制

根据信息系统质量控制的目标任务,结合实际案例,对其代码级漏洞测试、功能测试、性能测试、问题定位、回归测试和文档评审的测试方法进行描述。使人们了解信息系统的测试方法,并且对信息系统的质量控制有所重视。     

Joint production control and product quality decision making in a failure prone multiple-product manufacturing system

This paper considers joint production control and product quality specifications decision making in unreliable multiple-product manufacturing system. This is with the knowledge that an optimum compromise should guide the decision making process. In fact, tight process specifications will generally lead to products with good quality and higher market values, but at the same time associated with a higher rate of non-conforming parts rejection leading to higher non quality costs and lower plant productivity. Moreover, in unreliable manufacturing context the decision maker should adopt an adequate production policy to hedge against future capacity shortages caused by machine failures in order to meet customer demand. This paper intends to extend previous findings to tackle this problem and study the overall decision making process aiming to guide the production and quality specification decisions in multiple-product context. The overall optimal decision policy is defined here as one that maximises the long term average per unit time profit of a combined measure of quality and quantity dependent sales revenue, minus inventory and backlog costs, in the presence of random plant failures and random repair durations.     

Mathematically fuzzy approaches to quality control

It is proposed to simulate quality control by means of one out of three fuzzy numbers used in accordance with the circumstances: the tolerance number as a variety of fuzzy interval, the S number in the form of a rectangular sigmoid, and the Z number in the form of a rectangular zetoid. Translated from Izmeritel’naya Tekhnika, No. 3, pp. 18–19, March, 2009.     

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.     

Continuous Taguchi—a model-based approach to Taguchi's ‘quality by design’ with arbitrary distributions

Statistical experimental design has been used in ‘off-line’ quality control to determine the optimal settings for a system even when the mathematical model is known. Taguchi demonstrated how signal-to-noise ratios could be used to improve the performance of a system through variance minimization. However, these statistical methods often do not use the full distribution information that may be available. Proposed in this paper is an extension and complement to Taguchi's use of experimental design and signal-to-noise ratios for known system models. The use of a probability transformation method with the mathematical system model will allow designers to perform parameter and tolerance design simultaneously using a method of ‘fast integration’. The result is a new method in the field of ‘quality by design’, which we call continuous Taguchi, that can handle both linear and non-linear systems, with components of any distribution type, with or without correlation of the variables. In addition, an interpretation of Taguchi's classification of factors is given in the context of our full distribution method. © 1998 John Wiley & Sons, Ltd.