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

基于先前制造工艺的碳纤维增强复合材料的自适应加工

• 批准号: 580723-2022
• 负责人: Jin, XiaoliangX
• 金额: $ 1.82万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759979
• 关键词: Adaptive; Machining; Carbon; Fiber; Reinforced

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.

本项目的目标是开展具有自适应工艺参数的碳纤维增强聚合物(CFRP)构件智能加工的协同研究。加工操作，如修边和打孔，是用来获得最终的几何形状和表面的碳纤维复合材料组件。然而，加工前碳纤维增强塑料制造链中的工艺不确定性导致其力学性能发生很大变化。所提出的自适应加工策略将考虑由于先前制造工艺而引起的CFRP性能不确定性，从而实现最佳加工性能。制造业占加拿大国内生产总值的10%(1740亿美元)，占所有商品出口的68%以上，并维持着170万个全职工作岗位。在应用先进制造技术方面，航空航天工业是加拿大经济的支柱。制造碳纤维复合材料是推动加拿大航空航天和国防工业发展的关键技术之一。该项目旨在解决复合材料制造业面临的严峻挑战，其研究成果预计将惠及加拿大的航空航天制造业，并进一步增强其经济增长和在先进制造、航空航天和国防领域的全球领导地位。这项合作是在UBC和华盛顿大学(UW)的研究小组之间进行的，专业知识相辅相成。UW复合材料集团是复合材料制造研究的全球领导者，得到了波音等公司的大力工业支持。具体地说，拟议的研究将产生新的知识，以了解CFRP中的机械性能变化如何定量地影响加工中的材料去除机制和表面生成。将实施以物理为基础的综合建模和机器学习方法。在实践中，将开发一种包括加工路径、工艺参数和刀具在内的最佳加工工艺规划策略，以实现高效的工艺和提高零件质量。