Abstract Scope |
Unlike welding and casting technologies that matured largely through many decades of trial and error testing, metal printing is uniquely positioned to benefit from the powerful emerging digital tools such as mechanistic modeling and machine learning. The common issues of quality consistency of 3D printed parts and cost competitiveness, particularly defects such as cracking, lack of fusion, delamination, distortion, residual stress, surface roughness, compositional change, and balling are difficult to mitigate easily using empirical testing. The expensive machines and feedstocks and the wide range of values of the additive manufacturing process variables make a large volume of physical testing expensive and time-consuming. In contrast, virtual testing using validated numerical simulation tools can provide optimized solutions to effectively mitigate defects based on scientific principles before parts are physically printed. When the underlying physical processes of metal printing can be quantified based on the laws of physics, the process variables can be connected with the formation of defects, and well-tested mechanistic numerical models can mitigate defects. When the mechanisms of defect formation are not known, machine learning provides a framework to connect the process variables with the formation of defects, especially when an adequate volume of data is available. Unlike additive manufacturing hardware and material testing and characterization facilities, mechanistic modeling and machine learning do not require expensive equipment. By significantly eliminating the influence of financial resources, the world can benefit from the scholarship, imagination, and creativity of all researchers, thus expediting the development of additive manufacturing and making the world a more inclusive and welcoming place for all. |