By Gyung-Jin Park
In the aggressive international of contemporary engineering, rigorous and sure layout methodologies are wanted. even if, many components of engineering layout are played in both an ad-hoc demeanour or in keeping with the instinct of the engineer.
Analytic tools for layout Practice is the 1st publication to examine either levels of the layout technique – conceptual layout and distinctive layout – and element layout methodologies for each step of the full layout method. The e-book introduces the next analytic layout methodologies and explores their usefulness with many mathematical and functional examples:
- Axiomatic design
- Design of experiments;
- Robust design;
- Structural optimization;
- Dynamic reaction optimization; and
- Multidisciplinary optimization.
A bankruptcy of the e-book is dedicated to case stories exhibiting how functional layout difficulties will be solved with analytic layout equipment in keeping with Professor Park’s stories of training layout engineering over the last ten years.
Students who want an creation to fashionable layout theories and wish to appreciate how they are often utilized to a variety of actual engineering difficulties will locate Analytic equipment for layout perform an very good creation to the topic. the required arithmetic is saved to a minimal and the book’s functional concentration is going to make the e-book invaluable to practicing engineers as a pragmatic instruction manual of design.
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Extra info for Analytic Methods for Design Practice
11 presents a schematic view of this. 11. Calculation of the information content using the probability density function the design range and the system range. The design range is defined by lower and upper bounds and the system range is defined by a distribution function of the system performance. 12. The design should be directed to increase the common range. 32b) where Asr is the system range and Acr is the common range. 9 [Calculation of the Information Content Using a Probability Density Function] A problem is made to demonstrate an example.
29) If p1 is the probability that DP1 satisfies FR1 , then the probability that DP2 satisfies FR2 under satisfaction of FR1 by DP1 is a conditional probability. Suppose it is p 21 . 31) The conditional probability is useful for investigating the characteristics of the Information Axiom. However, it is rarely applied to real problems because p 21 is not easy to evaluate. Instead, the probability density function is more practical for application. 2 Probability Density Function and Information Content Information content can be calculated by using the probability density function.
17. 17. 1¼ ª FR1* º ª3º « * » « » and 'FRi ¬« FR2 ¼» ¬7 ¼ i 1,2. 10. requirement is FRi* 'FRi d FRi d FRi* 'FRi . 5, 48 Analytic Methods for Design Practice (1) Calculate the information content in the DP range by using the graphical method. (2) Calculate the information content in the FR range by using the graphical method. (3) Calculate the information content in the DP domain by using the integration method. 18. 18. 19. 19. 18. 59. 68. tolerances for the design parameters are the same. Calculate probability of success by the integration method.
Analytic Methods for Design Practice by Gyung-Jin Park