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Optimal tolerance allocation and process-sequence selection incorporating manufacturing capacities and quality issues
Journal of Manufacturing Systems Vol. 23/No. 4 2004
2003-2004 abstract and ke word index Journal of Manufacturing Systems Volume 23, Number 2, 2004
ysis, Ful-Chiang Wu, Chiuh-Cheng Chyu, v23, n2, 2004, pp134-143 The use of the Taguchi method for improving the design and quality of products and processes has become widespread among different industries. The traditional Taguchi method focused on one characteristic to optimize a combination of parameter conditions. In practice, most products have more than one quality characteristic. The methods of multiple quality characteristics design have become very important for industries. Several studies have presented approaches addressing multiple quality characteristics. Few published articles have focused primarily on optimizing correlated multiple quality characteristics. This research presents an approach to optimizing correlated multiple quality characteristics by using proportion of quality loss reduction and principal component analysis. The results reveal the advantages of this approach in that the optimal parameter design using proportion of quality loss reduction is the same as that using the Taguchi traditional method for one quality characteristic; the chosen optimal design is robust for optimizing correlated multiple quality characteristics. Keywords: Taguchi Method, Multiple Quality Characteristics, Loss Function, Proportion of Quality Loss Reduction, Principal Component Analysis
Strengths and Limitations of Taguchi's Contributions to Quality, Manufacturing, and Process Engineering, Saeed Maghsoodloo, Gultekin Ozdemir, Victoria Jordan, Chen-Hsiu Huang, v23, n2, 2004, pp73-126 This paper reviews Genichi Taguchi's contributions to the field of quality and manufacturing engineering from both a statistical and an engineering viewpoint. His major contributions are first listed and then described in a systematic and analytical manner. The concepts underlying Taguchi's univariate quality loss functions (QLFs), his orthogonal arrays (OAs), robust designs, signal-tonoise (S/N) ratios, and their corresponding applications to quality and process engineering are examined and described in great detail. Some of Taguchi's OAs are related to the classical (fractional) factorial designs (a field that was started by Sir Ronald A. Fisher in the early 1920s). The applications of Taguchi's robust (parameter and tolerance) designs to manufacturing engineering are illustrated through designed experiments. Kevwords: Taguchi Methods, Quality Loss Functions, Orthogonal Arrays, Parameter Design, Tolerance Design, Signal-to-Noise Ratios
Quadratic Quality Loss Functions and Signal-to-Noise Ratiosfor a TrivariateResponse, Gultekin Ozdemir, Saeed
Optimal Tolerance Allocation and Process-Sequence Selection Incorporating Manufacturing Capacities and Quality Issues, Natalia Robles, Utpal Roy, v23, n2, 2004,
Maghsoodloo, v23, n2, 2004, pp144-171 This paper extends Taguchi's quadratic quality loss function (QQLF) and signal-to-noise (S/N) ratios to a trivariate case by considering correlations between all pairs of quality characteristics. Single static quality characteristics (QCHs) are classified as: (1) smaller the better (STB), (2) larger the better (LTB), and (3) nominal the best (NTB). Initially, Taguchi studied single-response models. Later Chang (1993), Maghsoodloo and Chang (2001), and Maghsoodloo and Huang (2001) obtained relationships for the bivariate response cases. Chang (1993) and Maghsoodloo and Chang (2001) developed relationships for cost coefficients of the bivariate QQLF and S/N ratios for the cases of (NTB, NTB), (STB, STB), and (LTB, LTB). Later Maghsoodloo and Huang (2001) developed relationships for the remaining cases, which are (STB, LTB), (STB, NTB), and (LTB, NTB). This paper extends the number of variates from two to three, which are correlated, and obtains relationships between cost coefficients to ensure that the cost matrix is positive definite (pd). Also, S/N ratios are developed for all combinations of trivariate responses.
pp127-133 This research focuses on the development of a model for tolerance and process sequence selection optimization. To ascertain the optimal tolerances for all component dimensions of an assembly and the best processing sequences, the model incorporates the manufacturing tolerance cost and the quality loss resulting from not achieving the committed target dimensions. In addition, the model includes decisive manufacturing constraints such as machining capacities, measurement errors, and process capabilities. The process selection and tolerance optimization constraints require the model to be formulated as a 0-1 nonlinear optimization problem. This paper is an original work and has not been previously published except in the Transactions of NAMRI/ SME, Vol. 31, 2003. Keywords: Optimal Tolerance Allocation, Processing Sequence Selection, Process Sequence Optimization
Optimization of Correlated Multiple Quality Characteristics Robust Design Using Principal Component Anal-
337
2003-2004 abstract and ke word index Journal of Manufacturing Systems Volume 23, Number 2, 2004
ysis, Ful-Chiang Wu, Chiuh-Cheng Chyu, v23, n2, 2004, pp134-143 The use of the Taguchi method for improving the design and quality of products and processes has become widespread among different industries. The traditional Taguchi method focused on one characteristic to optimize a combination of parameter conditions. In practice, most products have more than one quality characteristic. The methods of multiple quality characteristics design have become very important for industries. Several studies have presented approaches addressing multiple quality characteristics. Few published articles have focused primarily on optimizing correlated multiple quality characteristics. This research presents an approach to optimizing correlated multiple quality characteristics by using proportion of quality loss reduction and principal component analysis. The results reveal the advantages of this approach in that the optimal parameter design using proportion of quality loss reduction is the same as that using the Taguchi traditional method for one quality characteristic; the chosen optimal design is robust for optimizing correlated multiple quality characteristics. Keywords: Taguchi Method, Multiple Quality Characteristics, Loss Function, Proportion of Quality Loss Reduction, Principal Component Analysis
Strengths and Limitations of Taguchi's Contributions to Quality, Manufacturing, and Process Engineering, Saeed Maghsoodloo, Gultekin Ozdemir, Victoria Jordan, Chen-Hsiu Huang, v23, n2, 2004, pp73-126 This paper reviews Genichi Taguchi's contributions to the field of quality and manufacturing engineering from both a statistical and an engineering viewpoint. His major contributions are first listed and then described in a systematic and analytical manner. The concepts underlying Taguchi's univariate quality loss functions (QLFs), his orthogonal arrays (OAs), robust designs, signal-tonoise (S/N) ratios, and their corresponding applications to quality and process engineering are examined and described in great detail. Some of Taguchi's OAs are related to the classical (fractional) factorial designs (a field that was started by Sir Ronald A. Fisher in the early 1920s). The applications of Taguchi's robust (parameter and tolerance) designs to manufacturing engineering are illustrated through designed experiments. Kevwords: Taguchi Methods, Quality Loss Functions, Orthogonal Arrays, Parameter Design, Tolerance Design, Signal-to-Noise Ratios
Quadratic Quality Loss Functions and Signal-to-Noise Ratiosfor a TrivariateResponse, Gultekin Ozdemir, Saeed
Optimal Tolerance Allocation and Process-Sequence Selection Incorporating Manufacturing Capacities and Quality Issues, Natalia Robles, Utpal Roy, v23, n2, 2004,
Maghsoodloo, v23, n2, 2004, pp144-171 This paper extends Taguchi's quadratic quality loss function (QQLF) and signal-to-noise (S/N) ratios to a trivariate case by considering correlations between all pairs of quality characteristics. Single static quality characteristics (QCHs) are classified as: (1) smaller the better (STB), (2) larger the better (LTB), and (3) nominal the best (NTB). Initially, Taguchi studied single-response models. Later Chang (1993), Maghsoodloo and Chang (2001), and Maghsoodloo and Huang (2001) obtained relationships for the bivariate response cases. Chang (1993) and Maghsoodloo and Chang (2001) developed relationships for cost coefficients of the bivariate QQLF and S/N ratios for the cases of (NTB, NTB), (STB, STB), and (LTB, LTB). Later Maghsoodloo and Huang (2001) developed relationships for the remaining cases, which are (STB, LTB), (STB, NTB), and (LTB, NTB). This paper extends the number of variates from two to three, which are correlated, and obtains relationships between cost coefficients to ensure that the cost matrix is positive definite (pd). Also, S/N ratios are developed for all combinations of trivariate responses.
pp127-133 This research focuses on the development of a model for tolerance and process sequence selection optimization. To ascertain the optimal tolerances for all component dimensions of an assembly and the best processing sequences, the model incorporates the manufacturing tolerance cost and the quality loss resulting from not achieving the committed target dimensions. In addition, the model includes decisive manufacturing constraints such as machining capacities, measurement errors, and process capabilities. The process selection and tolerance optimization constraints require the model to be formulated as a 0-1 nonlinear optimization problem. This paper is an original work and has not been previously published except in the Transactions of NAMRI/ SME, Vol. 31, 2003. Keywords: Optimal Tolerance Allocation, Processing Sequence Selection, Process Sequence Optimization
Optimization of Correlated Multiple Quality Characteristics Robust Design Using Principal Component Anal-
337