Data Science and Composite Materials Manufacturing

英属哥伦比亚大学（University of British Columbia）的两个研究集群——复合研究网络（Composite research Network）和数据科学研究所（Data Science Institute）——及其工业合作伙伴Convergent Manufacturing Technologies Inc.正在将数据科学与工程科学这两个互补但迄今为止联系不紧密的数字线索交织在一起，以提升加拿大在先进材料制造领域的地位。在过去的二十年里，我们见证了所谓的“大数据”时代的出现。这是由从大量丰富、复杂的数据中学习到构建复杂模型并从中提取有洞察力的模式的突破所推动的。许多应用领域，如金融、医学等，都受益于这些数据科学、机器学习、人工智能和计算统计学的新方法。然而，先进制造的应用领域，特别是在复合材料领域，尚未利用和利用上述技术的进步。特别是，我们受到航空航天工程中复合材料（例如碳纤维）零件高压釜固化问题的激励，并应用数据科学、机器学习和概率编程方面的进步，以更好地理解和预测复合材料制造过程中的物理过程。最终的结果是在复合材料的设计和制造中减少昂贵的决策（即时间，财务）-最终导致提高效率和更好的产品。

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.