Lavoisier S.A.S.
14 rue de Provigny
94236 Cachan cedex
FRANCE

Heures d'ouverture 08h30-12h30/13h30-17h30
Tél.: +33 (0)1 47 40 67 00
Fax: +33 (0)1 47 40 67 02


Url canonique : www.lavoisier.fr/livre/autre/quality-engineering/descriptif_4675216
Url courte ou permalien : www.lavoisier.fr/livre/notice.asp?ouvrage=4675216

Quality Engineering Off-Line Methods and Applications

Langue : Anglais

Auteur :

Couverture de l’ouvrage Quality Engineering

As quality becomes an increasingly essential factor for achieving business success, building quality improvement into all stages?product planning, product design, and process design?instead of just manufacturing has also become essential. Quality Engineering: Off-Line Methods and Applications explores how to use quality engineering methods and other modern techniques to ensure design optimization at every stage. The book takes a broad approach, focusing on the user?s perspective and building a well-structured framework for the study and implementation of quality engineering.

Starting with the basics, this book presents an overall picture of quality engineering. The author delineates quality engineering methods such as DOE, Taguchi, and RSM as well as computational intelligence approaches. He discusses how to use a general computational intelligence approach to improve product quality and process performance. He also provides extensive examples and case studies, numerous exercises, and a glossary of basic terms.

By adopting quality engineering, the defect rate during manufacturing shows noticeable improvement, the production cost is significantly lower, and the quality and reliability of products can be enhanced. Taking an integrated approach that makes the methods of upstream quality improvement accessible, without extensive mathematical treatments, this book is both a practical reference and an excellent textbook.

Introduction. Fundamentals of Experimental Design. Principles of Quality Engineering. Utilization of Orthogonal Arrays. Quality Loss Function and Static Signal-to-Noise Ratios. Parameter Design for Static Characteristics. Parameter Design for Dynamic Characteristics. Implementing Parameter Design. Tolerance Design. Mahalanobis-Taguchi System. Response Surface Methodology. Parameter Design Using Computational Intelligence. Appendix. References. Glossary. Index.

Chao-Ton Su is a chair professor with the Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan.