The research objective of this award is to develop integrated quality and reliability models and analysis tools that provide fundamental insights for the successful development and commercialization of evolving technologies, such as Micro-Electro-Mechanical Systems (MEMS) and biomedical implant devices. For many new and evolving technologies, their continued successful development depends on the concurrent modeling and optimization of process variability and product life, which are inherently linked for many of these technologies. The first research effort is to develop an integrated quantitative methodology to jointly optimize system quality and reliability. Based on this integrated framework, probabilistic models will be investigated for predicting reliability of devices that experience multiple failure processes due to simultaneous exposure to degradation and shock loads. Competing risk models with dependent failure processes will be developed and extended. In addition, reliability models will be developed for complex systems with multiple independent or dependent components. Multiple user objectives (e.g., quality, reliability, yield, cost, etc.) will be simultaneously considered by the formulation and solution of multi-objective optimization problems. Finally, the developed models will be validated for MEMS and biomedical implant devices through collaborations with industrial and government partners.
The results of this research will advance the state-of-the-art by contributing new concepts, models and algorithms. A solid multidisciplinary scientific foundation will be built to combine methodologies in quality, reliability, statistics and operations research. In addition, the success of this research will offer fundamental insights that can be transformed to many newer design and manufacturing problems, such as nano-technology that also has unique manufacturing challenges. The integrated methodology can provide timely and effective tools for decision-makers in manufacturing to economically optimize operational decisions for improving reliability, quality and productivity. Undergraduate and graduate students from underrepresented groups will be continuously recruited to work on this research.
