Optimized laser and electron beam based additive manufacturing of advanced intermetallic components

基于优化激光和电子束的先进金属间化合物增材制造

• 批准号: 530066-2018
• 负责人: Sinclair, Chadwick
• 金额: $ 3.53万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=697565
• 关键词: Optimized; laser; electron; beam; based

Additive manufacturing (AM), or 3D printing, is rapidly being developed as a disruptive manufacturing technology capable of unprecedented mass customization. The ability to start from a digital rendering of an object and to 'print' an arbitrarily complex geometric part from a wide variety of materials is driving rapid innovation in a number of industries. In the case of structural components, additive manufacturing is allowing for fewer processing steps, more complex geometry and lighter weight for crucial applications ranging from the medical to the aerospace industries. The capabilities of this technique are, however, limited at present by the complexity of the processes involved. This proposal is being made within the framework of the 'Joint Canada-Germany 2 +2 call on Advanced Manufacturing-Industry 4.0' in collaboration with a Canadian company with speciality in process simulation (Convergent Manufacturing Technologies), a German company with specialization in the manufacture of advanced turbochargers (G+L Innotech) and the German aerospace centre, DLR. The aims of the project are to develop technologies for process optimization via in-situ monitoring of advanced intermetallic materials with coupled physics-based process simulation and to use this knowledge for the construction of additively manufactured turbochargers by means of both laser and electron beam-based AM technologies.

增材制造（AM）或3D打印正在迅速发展成为一种颠覆性的制造技术，能够实现前所未有的大规模定制。从物体的数字渲染开始，用各种各样的材料“打印”出任意复杂的几何部件，这种能力正在推动许多行业的快速创新。就结构部件而言，增材制造允许更少的加工步骤，更复杂的几何形状和更轻的重量，适用于从医疗到航空航天工业的关键应用。然而，由于所涉及的过程的复杂性，这种技术的能力目前受到限制。