TL;DR
The Reliability-Aware Physics-Guided Learning (RAPIL) framework provides an interpretable, reliability-aware framework for relative density prediction across heterogeneous LPBF datasets while highlighting the remaining challenges associated with machine-dependent variability and model transferability.
Powder bed fusion–laser beam (PBF-LB) of nickel–titanium (NiTi) has attracted increasing interest in aerospace, biomedical, and energy applications owing to its shape memory and superelastic properties, combined with the capability to fabricate complex geometries. However, the strong sensitivity of NiTi to thermal hist...
With the increasing power density in advanced packaging, the thermo-mechanical reliability of Ball Grid Array (BGA) solder joints has become a critical concern. While data-driven machine learning models are applied for fatigue prediction, they typically suffer from severe overfitting and violate physical laws under dat...
This review provides a comprehensive overview of ML-based approaches applied to LPBF, emphasizing the importance of understanding the underlying physical phenomena that significantly influence data collection, preprocessing, and feature engineering strategies essential for effective model training and validation.
High-entropy alloys (HEAs) provide a broad compositional space for developing structural materials with balanced phase stability and mechanical performance. However, reliable mechanical-property prediction remains challenging because alloy chemistry, phase constitution, processing state, and model uncertainty are s...
Additive Manufacturing is an upcoming technology to produce metal structures in industry as complex near net-shaped structures can be built. One commonly used method is Laser Powder Bed Fusion (PBF-LB), that uses lasers to melt metal powder layer by layer. Alongside advantages that come with this technology, the va...
Reliable prediction of crystallographic texture in laser powder bed fusion is critical for linking process conditions with anisotropic response and for qualification. However, black-box models may fail under shift and cannot distinguish weak data support from loss of physical validity. This study develops a two-stage p...
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