Herbert Wertheim College of Engineering
Jia (Peter) Liu Lab
Led by Jia Liu · Industrial and Systems Engineering
What we study
The Liu Lab studies advanced manufacturing using machine learning and physics-based models, focusing on optimizing laser powder bed fusion and improving fatigue performance in aerospace and automotive industries.
What working here is like
- Computational
- Coding required
- Papers
- 5
Recent papers, explained
- Physics-inspired statistical modeling of the impact of surface roughness on fatigue performance of additively manufactured Ti–6Al–4V
2026 · International Journal of Fatigue
- Cross-Alloy Transfer Learning With Transferability Assessment for Mechanical Property Prediction of Lead-Free Solders
2026 · IEEE Transactions on Components Packaging and Manufacturing Technology
Uses data from one solder alloy to predict properties of another, reducing the need for expensive tests.
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- Investigation of Inconel 718 powder flowability for laser beam powder bed fusion using physics-informed machine learning framework
2025 · Powder Metallurgy
Tests how Inconel 718 powder features affect its flowability in 3D printing using machine learning.
- A Machine Learning Framework with Shapley’s Additive Explanations to Assess Solder Joint Reliability for Electronic Packaging
2025 · Journal of Electronic Materials
Uses machine learning to predict solder joint reliability, explaining results with Shapley values.
- Benchmarking Machine Learning Methods Against Finite Element and Empirical Models for Predicting Solder Joint Characteristic Life
2025 · IEEE Transactions on Components Packaging and Manufacturing Technology
Compares machine learning methods with traditional models for predicting solder joint lifespan.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
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