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

由于与大流行相关的限制，制造业，特别是汽车行业，供应链中断。金属添加剂制造(AM)，也称为3D打印，是一种工业生产的变革性方法，能够直接从计算机辅助设计创建部件，并避免传统的造型和加工。AM提供了新的机会，以减少与汽车行业中与工艺相关的工具的采购、制造、维修以及最终交付相关的交货期。然而，目前有三个关键障碍阻碍了AM的大规模采用，尤其是汽车工业中的定向能沉积(DED)工艺：(1)缺陷和缺陷的形成，如气孔、裂纹、残余应力，导致产品质量不一致，并缩短产品周期；(2)需要最佳的工艺参数(如激光功率、光斑大小、扫描速度和前后处理条件)来确定每种模具材料，以确保产品质量的一致性；(3)粉末原料成本高，阻碍了DED工艺走向大规模生产。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 Engineering和NRC-MissisAuga。这个协作团队将开发一种基于激光的闭环控制的DED系统，该系统可以针对各种材料和零件几何自适应地检测和预测缺陷并校正加工参数，而只需很少的人力投入，从而显著提高用于生产的AM零件的质量。该团队还将调查和优化DED工具钢的前处理和后处理条件，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，导致多伦多大学与合作组织之间建立几个后续的重大研究项目。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.