This paper presents the first comprehensive literature review of deep learning (DL)
applications in additive manufacturing (AM). It addresses the need for a thorough analysis in …

In its comprehensive exploration of recent developments in laser powder bed fusion (LPBF)
characteristics modeling, this study stands out by offering a unique emphasis on both …

Although laser powder bed fusion (LPBF) technology is considered one of the most
promising additive manufacturing techniques, the fabricated parts still suffer from porosity …

Numerous efforts in the additive manufacturing literature have been made toward in-situ
defect prediction for process control and optimization. However, the current work in the …

Deep learning-based methods for object recognition under closed situation have achieved
remarkable progress. However, the data distribution of cross-scenario images of strip steel …

Shielding gas, metal vapors, and gases trapped inside powders during atomization can
result in gas porosity, which is known to degrade the fatigue strength and tensile properties …

The study investigated the anti-correlation between thermal diffusivity and Ultimate Tensile
Strength (UTS) in Usibor® 1500 steel. The non-destructive pulsed laser spot thermography …

Abstract Machine learning techniques are extensively used to understand and predict
complex non-linear phenomena across various applications. Moreover, these techniques …

The laser welding process is an important manufacturing technology for metallic materials.
However, its application is often hindered by the occurrence of porosity defects. By far, an …

The formation of flaws such as internal porosity in parts produced by Metal-based Powder
Bed Fusion with Laser Beam (PBF-LB/M) significantly hinders its broader industrial …