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Robust Design and Assessment of Product and Production by Means of Probabilistic Multi-objective Optimization (eBook)

Autor: Maosheng Zheng
CHF 186.40
ISBN: 978-981-9726-61-5
Einband: PDF
Verfügbarkeit: Download, sofort verfügbar (Link per E-Mail)
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This book develops robust design and assessment of product and production from viewpoint of system theory, which is quantized with the introduction of brand new concept of preferable probability and its assessment. It aims to provide a new idea and novel way to robust design and assessment of product and production and relevant problems.

Robust design and assessment of product and production is attractive to both customer and producer since the stability and insensitivity of a product's quality to uncontrollable factors reflect its value. Taguchi method has been used to conduct robust design and assessment of product and production for half a century, but its rationality is criticized by statisticians due to its casting of both mean value of a response and its dispersion into one index, which doesn't characterize the issue of simultaneous robust design of above two independent responses sufficiently, so an appropriate approach is needed. 

The preference or role of a response in the evaluation is indicated by using preferable probability as the unique index. Thus, the rational approach for robust design and assessment of product and production is formulated by means of probabilistic multi-objective optimization, which reveals the simultaneous robust designs of both mean value of a response and its dispersion in manner of joint probability.

Besides, defuzzification and fuzzification measurements are involved as preliminary approaches for robust assessment, the latter provides miraculous treatment for the 'target the best' case flexibly.


Autor Maosheng Zheng
Verlag Springer Nature Singapore
Einband PDF
Erscheinungsjahr 2024
Seitenangabe 120 S.
Lieferstatus
Ausgabekennzeichen Englisch
Abbildungen X, 120 p. 19 illus., 8 illus. in color.
Masse 3'585 KB
Auflage 24001 A. 2024
Plattform PDF

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