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The causes of complex manufacturing systems' failures are preliminarily analyzed. The techniques of creating highly reliable manufacturing systems are then discussed. It has been shown that modern safety and quality analysis conducted from early design phase to operation and maintenance will enhance the reliability of complex manufacturing systems. In addition, optimization between human operators and machines reduces confusion and increases the reliability of such systems. Employing teamwork in all aspects of the organizational systems will enhance the productivity of complex systems. Moreover, there should also exist a unique and systemic mechanism which integrates the performance of the organizational systems with those of technical (engineering) and nontechnical (people) subsystems.

This paper uses the Taguchi method on the tolerance design of a hinge mechanism with the aim of obtaining a design that is insensitive to variations in manufacturing tolerance and joint clearance. The contribution of each control factor in the mechanism to the variations was also quantified. From the analysis of the response table and diagram, it was concluded that the dimension r₁, r₂, r₄ and β had a significant effect on the quality of the mechanism, contributing a total of 82.36% to product variation and were consequently named the key dimensions. The tolerance of these factors must therefore be tightened to improve the quality of the mechanism. Through a series of confirmation experiments, it was revealed that tightening the tolerance resulted in an increase in the S/N (signal to noise) ratio by 1.851 db and a reduction in product variation by 19.25% from the original 80.75%. The proposed method does not require complex mathematical derivatives, but simply the input and output relationship of the system. The method proposed in this study can be applied to all types of mechanism.

Systems can be classified as dynamic or static depending on the nature of the performance. A static system has a fixed target value for the performance characteristic while a dynamic system has a variable performance characteristic. This variable characteristic can be adjusted by a signal factor based on the requirement of the user. Due to this nature, modeling and optimization methods of dynamic systems may be different from those for static systems. This paper presents a two-step approach to the optimization modeling and design of linear dynamic systems. The approach is based on the concepts of quality engineering, experimental design, loss function, signal-to-noise (SN) ratio and optimization methodology. The motivation, justification and computation of SN ratios for linear dynamic systems will be discussed. As an illustrative example, a circuit design problem is given to demonstrate the method.

The common version of Genichi Taguchi's parameter design entails a marginal analysis procedure for determining parameter settings that will maximize or minimize a response. Unless all significant parameter interactions are known a priori and provided for in the orthogonal array of the parameter design experiment, conclusions drawn from marginal analysis will not necessarily be correct. In this paper, the probability that the routine parameter design procedure will actually succeed in realizing the optimization objective is discussed, followed by illustrations based on data from the commonly cited literature.

Polymerase Chain Reaction (PCR) is the most important experimental technique in DNA computing. When a concentration of DNA sequence is too small to investigate, PCR amplifies the DNA sequence by the addition of a polymerase. PCR is frequently used in DNA computing, because the calculation result is usually represented by a small concentration of DNA sequence. Therefore, PCR has a crucial influence on the calculation result. The reliability of PCR needs to be improved for DNA computing. In this paper, the reliability of PCR is defined by the reproducibility of the amplified concentration of DNA sequence. The PCR protocol is adjusted to improve the reliability. Quality Engineering efficiently supports this adjustment.