1118 High-fidelity Computational Modeling of Process-Structure-Property Relationships in Additive Manufacturing

Wentao Yan, Northwestern University & NIST

Jinhui Yan, Northwestern University

Additive Manufacturing (AM) technologies (typically Selective Laser/Electron Beam Melting/Sintering and Direct Energy Deposition) are revolutionizing industries. However, the wide industrial adoption is hindered by the lack of fundamental understanding of the complex manufacturing process, structure and properties. One goal of modeling for AM is to predict the resultant mechanical properties from given manufacturing parameters and intrinsic material properties, thereby reducing uncertainty in the material built. This can dramatically reduce the time and cost for the R&D of new products using AM. Thus, this mini-symposium is aimed at providing a platform to discuss high-fidelity computational models for AM process, structure and property to advance the fundamental understanding, and to further facilitate the linking of process-structure-property relationships. Specific topics of the mini-symposium include (but are not limited to)

• Multi-physics modeling of AM processes, including powder melting/sintering, powder spreading/feed, physically-informed heat source modeling, and residual stress and distortion
• Modeling of micro-structure evolution, such as grain growth/coarsening, solid-state phase transformation Mechanical modeling incorporating micro-structures (such as crystal, pores and voids) to predict mechanical properties
• High-fidelity and reduced-order computational methods for process, structure and property predictions, such as multi-phase flow, free surface capturing, granular flow, phase field, Cellular Automata, Monte Carlo, and Meshfree methods
• Data-driven modeling techniques, such as for model integration and material design
• Topology optimization
• Uncertainty quantification

• Experimental validation of models