Grant: Alliance Grants

The consortium of two research clusters at the University of British Columbia--the Composite Research Network and Data Science Institute -- and their industrial partner, Convergent Manufacturing Technologies Inc., are interweaving two complementary, yet to-date, poorly connected digital threads: data science with engineering science to advance Canada's position in advanced materials manufacturing. Over the past two decades, we have witnessed the emergence of the so-called "Big Data" era. This is driven by breakthroughs in learning from massive quantities of rich, complex data, to building sophisticated models and extracting insightful patterns from them. Numerous application domains, such as in finance, medicine, and so on, have benefited from these new methods in data science, machine learning, artificial intelligence, and computational statistics. However, the application domain of advanced manufacturing, particularly in composite materials, has yet to harness and leverage the advances in the aforementioned technologies. In particular, we are motivated by the problem of composite (e.g., carbon fibre) part autoclave curing in aerospace engineering, and applying advances in data science, machine learning, and probabilistic programming to better understand and predict the physical processes during composites manufacturing. The ultimate outcome is to reduce costly decisions (i.e., time, financial) in the design and manufacturing of composites--ultimately leading to increased efficiency and better products. The key challenges in applying statistical machine learning tools to composites manufacturing are: small, expensive data; slow, complex forward generation; need to specify control inputs; expensive features; accounting for simulator errors; and uncertainty quantification. In this proposal we will tackle all these challenges to address the problems of inference, prediction, and optimization in data-driven simulators of physical processes, with a particular focus on composite curing processes.