Search
CSV
RISIntegrated Physics-Informed Learning and Resonance Process Signature for the Prediction of Fatigue Crack Growth for Laser-Fused Alloys
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. Fatigue behaviors of metal components by laser fusion suffer from scattering due to random geometrical defects (e.g., porosity and lack of fusion). Monitoring fatigue crack initiation and growth is critical, ...
Bayesian Hierarchical Fatigue Scattering Model for Laser-Fused Metal Components
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. Laser powder bed fusion (LPBF) enables the fabrication of complex metallic components with high precision and flexibility. However, LPBF-manufactured materials often exhibit substantial scatter in fatigue behavior ...
Deep Learning-Based Anomaly Detection for Laser-Fused Metal Components Using Pyrometric Data
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. Laser powder-bed fusion (LPBF) enables the fabrication of complex metal components but suffers from subsurface porosity and geometrical defects that compromise performance. Existing postprocess inspection and ...
Fatigue Scattering Analytics and Prediction of SS 316L Fabricated by Laser Powder Bed Fusion
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Laser powder bed fusion (LPBF) is an enabling process manufacture of complex metal components. However, LPBF is prone to generate geometrical defects (e.g., porosity, lack of fusion), which causes a significant fatigue ...