Adaptive Machining of Carbon Fiber Reinforced Composites Informed by Prior Manufacturing Processes

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The objective of this project is to develop collaborative research on smart machining of carbon fiber reinforced polymer (CFRP) components with adaptive process parameters. Machining operations such as edge trimming and hole drilling are used to achieve the final geometry and surface of the CFRP components. However, the process uncertainties in the manufacturing chain of CFRP prior to machining cause substantial variations in its mechanical property. The proposed adaptive machining strategy will consider the CFRP property uncertainties due to prior manufacturing processes, therefore achieving the optimum machining performance. Manufacturing represents more than 10% ($174B) of Canada's total GDP, more than 68% of all merchandise exports, and sustains 1.7M full-time jobs. The aerospace industry is a pillar of Canada's economy in applying advanced manufacturing technology. Manufacturing carbon fiber-based composites is one of the key enabling technologies to boost Canada's aerospace and defense industries. This project is proposed to address the critical challenges faced in composites manufacturing, with the research outcome expected to benefit the aerospace manufacturing industry in Canada, and further enhance its economic growth and global leadership in advanced manufacturing, aerospace, and defense. This collaboration is between the research groups at UBC and University of Washington (UW) with the expertise complementing each other. The UW Composites Group is a global leader in composite manufacturing research with strong industrial support from Boeing etc. Specifically, the proposed research will generate new knowledge to understand how the mechanical property variation in CFRP quantitatively influences the material removal mechanism and surface generation in machining. An integrated physics-based modeling and machine learning approach will be implemented. Practically, an optimum machining process planning strategy including the machining path, process parameters, and tooling will be developed to achieve an efficient process and enhanced part quality.