Innovation in Additive Manufacturing Intelligent Adaptive Processes

增材制造智能自适应流程的创新

• 批准号: RGPIN-2017-04490
• 负责人: Vlasea, Mihaela
• 金额: $ 1.53万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=710474
• 关键词: Innovation; Additive; Manufacturing; Intelligent; Adaptive

Additive manufacturing (AM) has been gaining tremendous popularity in the public and industrial domains. The industrial landscape is undergoing a paradigm shift towards AM, as layer-by-layer manufacturing offers freedom of design by enabling production of shapes with complex internal and external features that enhance product functionality without the need for specialized tooling. For high-value-low-volume parts, or when a high degree of customization is needed, AM has been shown to decrease costs by reducing the design to fabrication cycle, by consolidating of multipart assemblies, and by reducing material waste. Thus AM has dramatically reshaped the industrial landscape, having a projected global revenue growth from $3.07B in 2013 to $21B by 2020 (Wohlers Report 2014), with key investment opportunities in related transformative research. The Canadian manufacturing sector has a unique opportunity to establish and maintain leadership through innovation in AM technologies. This proposal supports the above vision of excellence by targeting the ongoing technology gaps that need to be bridged before economically sustainable AM adoption can occur, with a direct focus on powder bed binder jetting (PBBJ) and powder bed laser fusion (PBLF) of metals. Some of the current limitations in AM of metals are related to part quality in terms of poor dimensional tolerances, high surface roughness, occurrence of internal defects, and inconsistencies in composition and mechanical properties. To address these limitations, this research focuses on developing advanced intelligent monitoring and adaptive control strategies for PBBJ and PBLF. To achieve this goal, two synergistic complementary long-term objectives are proposed: (1) development of advanced monitoring strategies for detection of relevant process characteristics, (2) advancement of robust adaptive control and calibration strategies to enable reliable part fabrication. The short-term focus will be to develop two Dynamic Sensing Clusters (DSC). The first, for PBLF composed of monitoring systems for thermometry, spectrometry, and vision-based measurements. The second, for PBBJ will be using vision-based sensing. The DSC systems will enable a comprehensive correlation between process input parameters, process characteristics, and part qualities and will be deployed to meet the two longer-term objectives for PBBJ and PBLF. The proposed research vision will facilitate the development of the next generation of intelligent powder bed AM systems and the training of highly qualified personnel. The research outcomes will lead to an increase in the Canadian manufacturing competitive advantage, with a meaningful economic impact through the development of technologies by which to offer high value services for metal part manufacturing and presenting commercialization opportunities for intelligent digital manufacturing strategies.

增材制造（AM）在公共和工业领域获得了极大的普及。工业格局正在向增材制造转变，因为逐层制造提供了设计自由度，可以生产具有复杂内部和外部特征的形状，从而增强产品功能，而无需专门的工具。对于高价值低批量零件，或需要高度定制时，AM已被证明可以通过减少设计到制造周期，通过合并多部件装配和减少材料浪费来降低成本。因此，增材制造极大地重塑了行业格局，预计全球收入将从2013年的30.7亿美元增长到2020年的210亿美元（Wohlers Report 2014），相关变革性研究具有关键的投资机会。加拿大制造业有一个独特的机会，通过AM技术的创新来建立和保持领导地位。 该提案通过瞄准在经济上可持续的AM采用之前需要弥合的持续技术差距来支持上述卓越愿景，并直接关注金属的粉末床粘合剂喷射（PBBJ）和粉末床激光聚变（PBLF）。目前金属AM的一些限制与零件质量有关，包括尺寸公差差、表面粗糙度高、内部缺陷的发生以及成分和机械性能的不一致。为了解决这些局限性，本研究的重点是开发先进的智能监测和自适应控制策略PBBJ和PBLF。 为了实现这一目标，提出了两个协同互补的长期目标：（1）开发先进的监测策略，用于检测相关的过程特性，（2）先进的鲁棒自适应控制和校准策略，使可靠的零件制造。短期重点将是开发两个动态传感集群。第一种是PBLF，由温度测量、光谱测量和基于视觉的测量的监测系统组成。第二，PBBJ将使用基于视觉的传感。DSC系统将实现过程输入参数、过程特性和零件质量之间的全面关联，并将用于满足PBBJ和PBLF的两个长期目标。 提出的研究愿景将促进下一代智能粉末床AM系统的开发和高素质人才的培养。研究成果将提高加拿大制造业的竞争优势，通过开发为金属零件制造提供高价值服务的技术，并为智能数字制造战略提供商业化机会，从而产生有意义的经济影响。