I-Corps: Translation potential of accelerating process development for additive manufacturing of metals

I-Corps项目的更广泛影响是开发基于机器学习的原位方法来监控金属增材制造过程。目前，由于材料的复杂加工性质和相关的物理现象，在金属合金中打印是困难的。打印结果和打印部件质量的可变性是降低打印部件质量及其全面生产潜力的主要障碍。该技术采用现场测量，可进行缺陷监测和质量保证报告、自动反馈控制、工艺参数映射以及对缺陷形成机制的了解。此外，该解决方案还可研究工艺-结构-性能关系，研究新合金的可印刷性，减少非原位表征的需求，并通过早期识别零件故障来减少材料浪费和生产时间。I-Corps项目利用经验学习结合对行业生态系统的第一手调查来评估该技术的转化潜力。该解决方案基于阿尔蒂官方智能辅助方法的开发，以帮助工艺开发和控制通过激光粉末床熔融增材制造的零件的质量。该方法使用成像传感器和机器学习来真实的实时监控过程，并通知最佳处理参数以及可能导致打印缺陷的过程变量。该方法不需要特殊、复杂的温度测量设置，这些设置可能无法可靠地用于不同的金属合金。该方法被认为是可推广到不同类型的金属合金上的激光粉末床机测试。控制打印部件质量的能力可能有助于在更广泛的应用范围内采用金属3D打印。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this I-Corps project is the development of machine learning-based in-situ methods to monitor the metal additive manufacturing process. Currently, printing in metal alloys is difficult due to the complex processing nature of the materials and the associated physical phenomena. The variability in printing outcomes and the quality of printed parts is a major obstacle that reduces the quality of printed parts and the potential for their full production. This technology uses in-situ measurement, which allows for defect monitoring and quality assurance reporting, automatic feedback control, process parameter mapping, and an understanding of defect formation mechanisms. In addition, the solution studies processing–structure-property relationships, investigates the printability of new alloys, reduces the need for ex-situ characterization, and reduces material waste and production time by early identification of part failure.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of an artificial intelligence-assisted approach to aid process development and the control of the quality of parts additively manufactured by laser powder bed fusion. The method uses imagining sensors and machine learning to monitor the process in real time and to inform the optimal parameters for processing as well as the process variables that can lead to printing defects. The method does not require special, complicated setups for temperature measurements that may not work reliably for different metal alloys. The method was found to be generalizable to different types of metal alloys tested on a laser powder bed machine. The ability to control the quality of printed parts may help in adopting 3D printing of metals in a wider scope of applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.