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Arizona State University


NSF funded projects:

Collaborative Research: Quantitative Reliability Prediction in Early Design Stages


The objective of this project is to predict product reliability in early design stages, including design conceptualization and embodiment. The prediction is based on information extracted from heterogeneous, multilevel sources such as previous components and products, expert opinions, early prototype testing, and initial simulations. The major approach is the development of a Bayesian framework that aggregates and processes the information and then quantitatively predicts product reliability in early design stages. Quantifying product reliability in early design stages helps reduce risk and avoid costly and unnecessary design changes. With a novel probabilistic graphical modeling approach, this project not only specifies the complex structure of the system reliability prediction but also integrates both subjective and objective information, thereby accommodating reliability-related data that are scattered, in different formats, at different levels of details, and from various sources. The accommodation of all the heterogeneous data allows for more accurate reliability prediction, leading to more effective actions identified early to prevent potential failures or reduce their likelihood.
If successful, this project will impact design practices for all kinds of products because reliability is a core element of product performance and directly determines customer satisfaction, product market share, and product safety. Specifically, this project will advance engineering design theory and methodology and expand the scope of reliability engineering. By quantitatively predicting product reliability early, the project provides engineers with a better way to achieve high reliability with reduced cost. This project is conducted through collaborative efforts between Arizona State University and Missouri University of Science and Technology, involving expertise in both engineering design and reliability engineering.


Collaborative Research: Efficient Experimentation for Product and Process Reliability Improvement


This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

This grant provides funding for developing experimental design theories and techniques for product and process reliability responses that will fill the gap that currently exists in design for reliability. Direct application of traditional design of experiment (DOE) tools on reliability experiments is inappropriate. This project will attack the fundamental problems of reliability experiments, including non-normal response distribution, nonlinear model and parameter dependency problems, which have prevented the efficient use of DOE for product and process reliability improvement. Preliminary studies indicate that the proposed methodology will result in designs superior to those encountered in practice. This project is innovative and transformative in terms of creating and testing a group of new DOE tools that will greatly expand the capability and applicability of statistical experimental design.

If successful, the theoretical results foreseen in the proposal will impact a number of scientific communities, including engineering design, material design, biomedicine and clinical trails. By “designing-in” reliability, companies will be able to reduce the number of experimental runs needed throughout the product design, development, and delivery. A comprehensive website devoted to experimental design and reliability will be developed to disseminate the results of this research. The proposed research activities are a natural venue for training graduate and undergraduate students to develop theoretical models and applications of experimental design and reliability analysis. This project will boost the interdisciplinary research (Industrial Engineering and Mathematical Statistics) at two universities (Arizona State University and Southern Illinois University Edwardsville), and improve both theoretical and application types of education for engineering and mathematical science student.