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 …

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 …

This article presents a state-of-the-art review of the emerging field of physics-informed
machine learning (PIML) models in additive manufacturing for process-structure-property …

Physics-informed deep learning (PIDL) is one of the emerging topics in additive
manufacturing (AM). However, the success of previous PIDL approaches is generally …

This research is centered on optimizing the mechanical properties of additively
manufactured (AM) lattice structures via strain optimization by controlling different design …

Bridging in additive manufacturing (AM) parts occur between two points without any support
from below. If the material extruded between these points is dangled (ie, not horizontal), this …

Physics-informed machine learning (PIML) represents a promising area within Laser-
Assisted Machining (LAM) process. However, previous approaches have heavily relied on …

Prediction of the temperature history of printed paths in additive manufacturing is crucial
towards establishing the process-structure-property relationship. Traditional approaches for …

In recent years, the field of materials engineering has undergone a significant shift towards
data-driven discovery, fueled by the increasing availability of experimental, theoretical, and …