Optimization of product and process design for quality and cost

Robust product and process design is an important technique for achieving high quality at low cost. It involves making the product's function much less sensitive to various sources of noise such as manufacturing variation, environmental variation and deterioration. This is a problem in optimization involving minimization of the mean square loss resulting from the deviation of the product's function from its target. Here we show that the optimization can be carried out in two steps: first maximize a quantity called signal-to-noise ratio (S/N) and then bring the performance on target by special adjustment parameters. The two-step procedure works for a wide variety of product functions and makes the optimization process more efficient and practical compared to the direct minimization of the quadratic loss function.     

Optimization strategies in robust engineering design and computer-aided design

This paper reviews the fundamental ideas involved in robust engineering design (RED), and how they relate to computer-aided design. There are several areas of RED that may be successfully resolved by the use of statistical methods or ideas. This paper gives a general overview of several popular statistical strategies in RED and discusses how these strategies approach the statistical problems involved.     

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.     

Selection of Welding Process Parameters for the Optimum Butt-Joint Strength of an Aluminum Alloy

In this paper, the selection of welding process parameters for obtaining the optimum weld butt-joint ultimate tensile strength (UTS) of aluminum alloy (6061-T6) is presented. Considering weld-joint UTS as the quality characteristic in the selection of process parameters, the Taguchi method is used to analyze the effect of each individual process parameter and of their interaction and then to determine process parameters for the optimum weld-joint UTS. Analysis of variance (ANOVA) technique is applied to investigate which welding process parameter has significant effect on the weld-joint UTS. Experimental results are provided to illustrate the proposed approach.     

Selection of Welding Process Parameters for the Optimum Butt-Joint Strength of an Aluminum Alloy

In this paper, the selection of welding process parameters for obtaining the optimum weld butt-joint ultimate tensile strength (UTS) of aluminum alloy (6061-T6) is presented. Considering weld-joint UTS as the quality characteristic in the selection of process parameters, the Taguchi method is used to analyze the effect of each individual process parameter and of their interaction and then to determine process parameters for the optimum weld-joint UTS. Analysis of variance (ANOVA) technique is applied to investigate which welding process parameter has significant effect on the weld-joint UTS. Experimental results are provided to illustrate the proposed approach.     

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.     

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 Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images

Nondestructive evaluation (NDE) techniques are widely used to detect flaws in critical components of systems like aircraft engines, nuclear power plants, and oil pipelines to prevent catastrophic events. Many modern NDE systems generate image data. In some applications, an experienced inspector performs the tedious task of visually examining every image to provide accurate conclusions about the existence of flaws. This approach is labor-intensive and can cause misses due to operator ennui. Automated evaluation methods seek to eliminate human-factors variability and improve throughput. Simple methods based on peak amplitude in an image are sometimes employed and a trained-operator-controlled refinement that uses a dynamic threshold based on signal-to-noise ratio (SNR) has also been implemented. We develop an automated and optimized detection procedure that mimics these operations. The primary goal of our methodology is to reduce the number of images requiring expert visual evaluation by filtering out images that are overwhelmingly definitive on the existence or absence of a flaw. We use an appropriate model for the observed values of the SNR-detection criterion to estimate the probability of detection. Our methodology outperforms current methods in terms of its ability to detect flaws. Supplementary materials for this article are available online.     

制造质量记录信息管理系统的总体设计与开发

本文首先论述了质量记录在计算机辅助质量管理（ＣＡＱＭＳ）中的作用及地位，随后阐述制造质量记录信息管理系统的总体设计方案，以上工作在计算机集成制造系统（ＣＩＭＳ）应用中将得到体现。     

Determination of dynamic crack resistance of ductile cast iron using the compliance ratio key curve method

The compliance ratio method is an analytical approach for instantaneous crack length determination in dynamic single-specimen J-R curve testing of ferritic ductile cast iron (DCI). Comparison testing at room temperature and −40 °C was applied to PCVN and SE(B)15 specimens to examine their performance and suitability for the dynamic key curve method for DCI. An experimental reference database of dynamic crack resistance curves was set up by low-blow multiple-specimen tests and used to validate the results of the CR method. The influence of test temperature, microstructure, loading rate and specimen geometry on fracture behavior of the tested DCI was investigated in great detail and these parameters were linked to fracture mechanical properties. The results obtained show that the CR method is suited to establish valid dynamic crack resistance curves for both types of specimen. Nevertheless, SE(B)15 specimens are preferred for dynamic J-R curve determination of DCI based on their advantages such as higher accuracy.     

Likelihood ratio gradient estimation for dynamic reliability applications

This paper investigates the issue of performing a first-order sensitivity analysis in the setting of dynamic reliability. The likelihood ratio (LR) derivative/gradient estimation method is chosen to fulfill the mission. Its formulation and implementation in the system-based Monte Carlo approach that is commonly used in dynamic reliability applications is first given. To speed up the simulation, we then apply the LR method within the framework of Z-VISA, a biasing (or importance sampling) method we have developed recently. A widely discussed dynamic reliability example (a holdup tank) is studied to test the effectiveness and behaviors of the LR method when applied to dynamic reliability problems and also the effectiveness of the Z-VISA biasing technique for reducing the variance of LR derivative estimators.