For over 25 years, Allise has applied mathematical and statistical methods to optimize product designs and manufacturing processes, assess product reliability and liability risk, and analyze data. Her research also includes the development of mathematical models to reveal optimal decision sequences. She employs designed experimentation, reliability analysis, stochastic optimization, and general statistical methods.
Allise is currently the president of Integral Concepts, specializing in failure prevention. She has worked as a managing scientist for Exponent in risk assessment and product litigation and as a management information consultant for Accenture. She has been an adjunct professor in the College of Engineering at the University of Michigan in Ann Arbor. She has also worked with the University of Michigan in providing a certification program in Reliability Engineering and developed research for Intelligent Transportation Systems.
She holds a B.S. in Mathematics, M.A. in Statistics, M.S. in Industrial Engineering, and a Ph.D. in Applied Mathematics/Operations Research from the University of Michigan, Ann Arbor. She also completed graduate coursework at the University of Chicago.
Optimization of Reliability & Other Product Performance Requirements (material content beneficial for all levels of competence)
For over 25 years, Allise has applied mathematical and statistical methods to optimize product designs and manufacturing processes, assess product reliability and liability risk, and analyze data. Her research also includes the development of mathematical models to reveal optimal decision sequences. She employs designed experimentation, reliability analysis, stochastic optimization, and general ...read more
Design of Experiments (DOE) is an indispensable tool for solving complex problems and pro-actively developing optimal and reliable product designs. Multiple product performance requirements (including reliability and sustainability requirements) must be jointly satisfied, and it is rarely obvious how to simultaneously meet all product or process performance requirements.
DOE, when properly applied, provides an extremely efficient approach to developing product/process understanding. The development of predictive models allows the identification of optimal solutions (product design and process specifications).
This presentation includes 3 case studies. One case study will be presented to illustrate successful application of DOE to achieve a challenging reliability requirement. Another case study will focus on a leak issue due to excessive porosity. An automotive component example illustrates a situation with several performance requirements to be met.
The presentation illustrates the use of Design of Experiments (DOE) for product and process optimization. The need for effective experimentation is motivated with several common questions that arise in product design, manufacturing, and decision-making. The use of powerful mathematical (predictive) models to describe and optimize a system is illustrated.
The case studies illustrate the power of DOE in developing optimal solutions that simultaneously satisfy reliability and other critical product performance requirements. The first case study involves the reliability of a valve system. The product was unable to achieve the reliability requirement, because a diaphragm would fail prematurely due to fatigue and weld inadequacy. The porosity issue was a chronic problem causing excessive internal costs to the supplier and customer.
In the automotive case study, there were several product performance requirements. Multi-response optimization attempts to find feasible solutions that simultaneously satisfy all product requirements. An optimizer can also perform sensitivity analyses, so decision-makers can focus on which design and manufacturing variables need to be controlled.
Professionals attending this presentation will gain several key insights, which will help them understand and apply DOE more effectively. They will learn how to apply DOE to meet reliability and other performance requirements.
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