I-Corps: Metal hybrid multi-metal parts made possible by additive manufacturing technology

I-Corps项目更广泛的影响/商业潜力是开发一种新的金属增材制造技术，该技术旨在在包含多种金属合金的单一工艺中打印零件。这些多金属部件被称为“金属混合”，该系统可以设计和制造具有复杂几何形状的金属部件，并在不同的沉积位置使用不同的金属。这是当今任何单一制造工艺都无法实现的。金属混合部件可以设计成与现有的单金属部件一样坚固，但重量更轻，同时具有优越的耐热性，耐磨性和耐腐蚀性。预计该技术的发展将颠覆广泛的高温应用和系统，通过减少技术足迹，降低制造成本，减少对复杂装配和连接操作的需求，并减少零件数量，提高增材制造工艺的效率。这在航空航天、汽车和能源行业都有应用。这个I-Corps项目是基于增材制造技术的发展，该技术允许在一种称为粉末床融合的增材制造系统中同时沉积和混合不同的金属粉末，形成薄层。然后用激光束熔化沉积的多金属粉末层，该过程一层一层地继续，直到零件建成。此外，还开发了一种机器学习算法来确定合适的激光参数，以正确熔化不同成分的多金属粉末。本文提出的沉积技术和工艺参数预测模型不仅将新一代金属零件引入工业，而且将扩大对增材制造多金属部件的制造工艺参数与材料性能之间关系的基本认识。该奖项反映了美国国家科学基金会的法定使命，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/commercial potential of this I-Corps project is the development of a new metal additive manufacturing technique that is designed to print parts in a single process containing multiple metal alloys. These multi-metal parts are called “metal hybrids,” and this system enables the design and manufacturing of metal parts with complex geometries and different metals used in various deposition locations. This is something that no single manufacturing process can achieve today. The metal hybrid parts may be designed to be just as strong but lighter in weight than existing single-metal parts while offering superior thermal, wear, and corrosion resistance. The development of this technology is expected to be disruptive to a wide range of high-temperature applications and systems, increase the efficiency of additive manufacturing processes by reducing technological footprints, reduce manufacturing costs, reduce the need for complex assemblies and joining operations, and reduce part count. This has applications for aerospace, automotive, and energy industries.This I-Corps project is based on the development of a additive manufacturing technique that allows for the simultaneous deposition and mixing of different metal powders, in thin layers, within a type of additive manufacturing system referred to as powder bed fusion. The deposited multi-metal powder layer is then melted using a laser beam and the process continues layer by layer until the part has been built. In addition, a machine learning algorithm has been developed to determine suitable laser parameters for correctly melting the multi-metal powders with varying composition. This proposed deposition technique and process parameter prediction model will not only introduce a new generation of metal parts to industry, but also will expand the fundamental understanding of the relationship between the manufacturing process parameters and material properties of additively manufactured multi-metal components.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.