This text presents a model-based approach to quality improvement through design of experiments. After a description of statistical methods for data analysis and design of experiments it addresses the following topics: Taguchi's approach to quality improvement; reduction of errors transmitted from factors to the response; robustness against errors in product/process parameters and external noise factors; optimization procedures for product/process design; quality improvement through mechanistic models; quality improvement of products with both qualitative and quantitative factors; and quality improvement based on replicated observations. The book provides systematic and detailed practical guidance to a model-based approach to quality engineering problems. All methods are illustrated by real-world examples that make them readily accessible to readers.
