The Composites Research Network (CRN) and the Data Science Institute are collaborating to merge engineering, AI, and data science to streamline manufacturing processes for composite materials. The team will develop digital factories in miniature that include digital twinning and then transfer the results to real-world scale for industry partners.
The past two decades have witnessed the emergence of the so-called “Big Data” era. There have been numerous breakthroughs—from data science, machine learning, artificial intelligence, and computational statistics—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 those methods. 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, this research consortium is motivated to apply the latest methods--in statistics, machine learning, probabilistic programming, artificial intelligence--to various processes involved in composite (e.g., carbon fibre reinforced polymers; CFRP) manufacturing for the aerospace sector. Examples of projects include:
- Developing improved simulators for curing process of CFRP (i.e., autoclave digital twin)
- Improving object and defect recognition by sensors
- Developing new sensors and algorithms to improve composite layup (e.g., automated displacement measurements, leak detection)
- Developing methods to predict surface damage in milling and tool wear in machining process of CFRP
List of Research Faculty:
- Trevor Campbell
- Xiaoliang Jin
- Abbas Milani
- Homayoun Najjaran
- Raymond Ng
- Anoush Poursartip
- Reza Vaziri
- Frank Wood
This research collaboration is supported with funding from Convergent Technologies, Canada's Digital Technology Supercluster, UBC VPRI, and NSERC Alliance.