About this Abstract |
Meeting |
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
Symposium
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Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
Presentation Title |
DigitalClone for Additive Manufacturing (DC-AM): an Integrated Computational Materials Engineering Platform to Model Metal AM Process and Performance |
Author(s) |
Jingfu Liu, Ziye Liu, Behrooz Jalalahmadi |
On-Site Speaker (Planned) |
Jingfu Liu |
Abstract Scope |
DigitalClone for Additive Manufacturing (DC-AM) is a multi-scale and multi-physics SaaS software to computationally assess the quality and performance of additively manufactured metal parts. DC-AM currently focuses on laser power bed fusion process (LPBF), with potential to extend to direct energy deposition (DED) and other AM processes. It uniquely links process-microstructure-fatigue modules to provide design and computational testing. Process module is to simulate as-build part distortion and residual stress; Microstructure module is to simulate grain morphology, grain size, and porosity; Fatigue module is to simulate component fatigue life using Sentient’s patented microstructure-based modeling approach. In this talk, we will present detailed features of DC-AM software along with the underlying modeling methodology. Case studies and experimental validation of different materials (e.g., Ti64, IN718, 17-4 PH, AlSi10Mg) and machines will be presented to demonstrate the feasibility of DC-AM. Additionally, we will share our vision of industrial needs and future development. |
Proceedings Inclusion? |
Undecided |