A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures
Part quality manufactured by the laser powder bed fusion process is significantly affected by
porosity. Existing works of process–property relationships for porosity prediction require …
porosity. Existing works of process–property relationships for porosity prediction require …
A deep learning framework for defect prediction based on thermographic in-situ monitoring in laser powder bed fusion
The prediction of porosity is a crucial task for metal based additive manufacturing techniques
such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring …
such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring …
Exploring chemistry and additive manufacturing design spaces: a perspective on computationally-guided design of printable alloys
Additive manufacturing (AM), especially Laser Powder-Bed Fusion (L-PBF), provides alloys
with unique properties, but faces printability challenges like porosity and cracks. To address …
with unique properties, but faces printability challenges like porosity and cracks. To address …
Multi-sensor monitoring for in-situ defect detection and quality assurance in laser-directed energy deposition
L Chen - 2024 - dr.ntu.edu.sg
Additive manufacturing (AM), specifically laser-directed energy deposition (LDED), has
evolved rapidly as a pivotal technology in the realm of Industry 4.0, gaining significant …
evolved rapidly as a pivotal technology in the realm of Industry 4.0, gaining significant …
Data-Augmented Modeling for Melt Pool Dimensions in Laser Powder Bed Fusion: A Bayesian Approach
The laser powder bed fusion (LPBF) technique has become increasingly prominent in metal
additive manufacturing. However, tuning parameters for printing defect-free parts requires …
additive manufacturing. However, tuning parameters for printing defect-free parts requires …