![]() ![]() ![]() Most prolifically, the density functional theory (DFT) community has used many of them since the early 2000s for evaluating numerous combinations of elements and crystal structures to discover new materials. New machine learning techniques are not needed for most metals AM development those used in other sects of materials science will also work for AM. The use of modern machine learning approaches to model these degrees of freedom can reduce the time and cost to elucidate the science of metals AM and to optimize the engineering of these complex, multidisciplinary processes. Consequently, tens to hundreds of materials and part design degrees of freedom must be simultaneously controlled and understood hence, metals AM is a highly interdisciplinary technology that requires synchronized consideration of physics, chemistry, materials science, physical metallurgy, computer science, electrical engineering, and mechanical engineering. In metals additive manufacturing (AM), materials and components are concurrently made in a single process as layers of metal are fabricated on top of each other in the near-final topology required for the end-use product. ![]()
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