Additive Manufacturing of Automotive Tooling Components: Defect Reduction, Process Optimization and Powder Development

制造业（尤其是汽车行业）因疫情相关限制而遭受供应链中断。金属增材制造（AM），也称为3D打印，是工业生产的一种变革性方法，可以直接从计算机辅助设计中创建组件，避免传统的成型和加工。AM提供了新的机会，以减少与采购，制造，维修，并最终交付过程相关的工具在汽车行业的交货时间。然而，目前有三个关键障碍阻碍了AM的大规模采用，特别是对于汽车行业的定向能量沉积（DED）工艺：（1）孔隙、裂纹、残余应力等缺陷和缺陷的形成，导致产品质量不一致并缩短产品周期时间;（2）最佳工艺参数（例如，激光功率、光斑尺寸、扫描速度以及预处理和后处理条件）来识别每种模具材料以确保产品质量的一致性;（3）粉末原料的高成本阻碍了DED工艺的大规模生产。该提案中的伙伴关系涉及多伦多大学的四个研究小组和三个伙伴组织--麦格纳国际、Exco工程和NRC-Mississauga。该合作团队将开发一种闭环控制的基于激光的DED系统，该系统可以检测和预测缺陷，并在几乎没有人工输入的情况下自适应地校正各种材料和零件几何形状的加工参数，从而显著提高AM零件的生产质量。该团队还将研究和优化DED工具钢的预处理和后处理条件，并为DED工艺开发低成本的原料粉末。为期两年的研究计划将培养两名博士后研究员，四名研究生和六名本科生，导致多伦多大学和合作组织之间的几个后续重大研究项目的建立。新的成果和相关技术将转让给伙伴组织，使加拿大的制造业受益。

The manufacturing sector, particularly the automotive industries, suffers from supply chain disruptions due to pandemic related restrictions. Metal additive manufacturing (AM), also known as 3D printing, is a transformative approach to industrial production that enables the creation of components directly from computer aided design and avoids conventional moulding and machining. AM offers new opportunities to reduce lead times associated with procurement, fabrication, repair, and ultimately delivery of process related tooling in the automotive sector. However, three key hurdles currently prevent the mass adoption of AM, particularly for the directed energy deposition (DED) process in the automotive industries: (1) The formation of flaws and defects, such as pores, cracks, residual stresses, lead to inconsistent product quality and reduce product cycle times; (2) Optimal process parameters (e.g., laser power, spot size, scanning speed, and pre-and post-processing conditions) are needed to identify each tooling material to ensure the consistency of product quality; (3) The high cost of powder feedstock impedes the DED process towards mass production. The partnership in this proposal involves four research groups at the University of Toronto and three partner organizations - Magna International, Exco Engineering and NRC-Mississauga. This collaborative team will develop a closed-loop controlled laser-based DED system that can sense and predict defects and correct processing parameters adaptively for various materials and part geometries with little human input, thereby significantly improving the quality of AM parts for production. The team will also investigate and optimize the pre-and post-processing conditions for the DED tool steels and develop low-cost feedstock powders for the DED process. The two-year research program will train two postdoctoral fellows, four graduate students, and six undergraduate students, leading to the establishment of several subsequent major research projects between the University of Toronto and the partner organizations. The new results and related techniques will be transferred to the partner organizations, benefiting the manufacturing industries in Canada.