Generative Design with Pre-Qualified Additive Manufacturing

In the past decade, there has been notable advancement in metal additive manufacturing (AM) for producing load-bearing, structural parts. This is due to the design flexibility and significant weight reduction benefits inherent to the process.  Metal additive manufacturing (AM) offers the ability to create fully functional parts with complex geometries directly from raw materials such as metal powders, which complements the capabilities of traditional manufacturing processes, including subtractive and formative methods. Powder Bed Fusion (PBF) is one of the most popular and important metal AM processes that employ a moving heat source (laser or electron beam) to melt and join layers of material powder laid out sequentially on the bed.  The heterogeneity of thermally induced properties in additively manufactured metal parts leads to two major challenges to the broader adoption of metal AM technologies:  (1) qualification of Metal AM components is a lengthy and costly process dominated by physical testing, and (2) lack of computational tools for design and optimization results in inefficient structural designs with significantly exaggerated safety factors. Intact Solutions proposes to develop the Generative.AM: a software technology for the generative design of pre-qualified additively manufactured metal components that explicitly takes advantage of the additive manufacturing process capabilities, including material heterogeneity and anisotropy, internal strains, and other process-induced properties.  The proposed approach builds on technological breakthroughs in simulation technology for additive manufacturing and generative design applications previously demonstrated with funding from NIST, and seeks to demonstrate the following trasnformative innovations:  (1) Generative.AM will generate geometry of highly efficient components with spatially heterogeneous material properties, designed explicitly in terms of experimentally validated process plans;  (2) The generated designs will be pre-qualified virtually with respect to process, material properties, and function using scalable and calibrated path-scale simulation;  (3) Generative.AM approach is based on rigorous scientific principles and relies on the thermal history of the manufacturing process to fuse, correlate, and unify simulation, experimental, and process data.  While proposed approach is applicable to many additive manufacturing processes, for the purpose of this Phase II project, we will focus on PBF as one of the most challenging and important metal additive manufacturing processes.   The developed technology will be demonstrated in the design of several non-trivial industrial components.