Probabilistic design
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Contents |
[edit] Introduction
Probabilistic design is a discipline within Engineering Design. It deals primarily with the consideration of the effects of random variability upon the performance of an engineering system during the design phase. Typically, these effects are related to quality and reliability. Thus, probabilistic design is a tool that is mostly used in areas that are concerned with quality and reliability. For example, product design, quality control, systems engineering, machine design, civil engineering (particularly useful in Limit state design) and manufacturing. It differs from the classical approach to design by assuming a small probability of failure instead of using the safety factor [1]
[edit] Designer’s perspective
When using a probabilistic approach to design, the designer no longer thinks of each variable as a single value or number. Instead, each variable is viewed as a probability distribution. From this perspective, probabilistic design predicts the flow of variability (or distributions) through a system. By considering this flow, a designer can make adjustments to reduce the flow of random variability, and improve quality. Proponents of the approach contend that many quality problems can be predicted and rectified during the early design stages and at a much reduced cost.
[edit] The objective of probabilistic design
Typically, the goal of probabilistic design is to identify the design that will exhibit the smallest effects of random variability. This could be the one design option out of several that is found to be most robust. Alternatively, it could be the only design option available, but with the optimum combination of input variables and parameters. This second approach is sometimes referred to as robustification, parameter design or design for six sigma
[edit] Methods used
Essentially, probabilistic design focuses upon the prediction of the effects of random variability. Some methods that are used to predict the random variability of an output include:
- the Monte Carlo method (including Latin hypercubes);
- propagation of error;
- design of experiments (DOE)
- the method of moments
- Statistical interference
[edit] References
- Ang and Tang (2006) Probability Concepts in Engineering: Emphasis on Applications to Civil and Environmental Engineering. John Wiley & Sons. ISBN 047172064X
- Ash (1993) The Probability Tutoring Book: An Intuitive Course for Engineers and Scientists (and Everyone Else). Wiley-IEEE Press. ISBN 0780310519
- Clausing (1994) Total Quality Development: A Step-By-Step Guide to World-Class Concurrent Engineering. American Society of Mechanical Engineers. ISBN 0791800350
- Haugen (1980) Probabilistic mechanical design. Wiley. ISBN 0471058475
- Papoulis (2002) Probability, Random Variables and Stochastic Process. McGraw-Hill Publishing Co. ISBN 0071199810
- Siddall (1982) Optimal Engineering Design. CRC. ISBN 0824716337
[edit] External links
[edit] Footnotes
- ^ See Probabilistic mechanical design by Haugen in the references
