Albert To
University of Pittsburgh, PA·Engineering
Metal 3D printing can build parts too complex for traditional machining, for aerospace, energy, biomedical and defense uses. In laser powder bed fusion (LPBF), a laser melts thin layers of metal powder one track at a time.
- Fields
Engineering
Materials Science
Computer Science
- Topics
additive manufacturing
metal 3D printing
laser powder bed fusion
- Grants
- 2 known grants, through Jul 2029
What they work on
- Build a framework that couples finite element heat simulation with data-driven surrogate models through a local thermal environment representation, allowing two-way information exchange to predict melt pool evolution quickly in laser powder bed fusion.
- Calibrate and validate the coupled surrogate-finite element framework across many processing conditions using computational fluid dynamics simulations, experimental melt pool characterization and LPBF builds.
- Determine optimal laser scan sequences that even out temperature in LPBF parts by incorporating advanced thermal effects into data-driven models, and validate the predicted reductions in overheating experimentally.
- Identify when thermal-only scan sequence optimization fails to reduce distortion and residual stress, add thermomechanical models in those cases, and measure effects on microstructure and part quality.
More about the research
The intense, fast-changing heat can warp parts, trap stress and cause cracks. Computer simulations that predict this heat are often too slow or too simple. The lab's projects build faster, more accurate thermal models and use them to plan how the laser moves.
Why it matters
Faster, reliable heat simulations and smarter laser scan plans could help aerospace, biomedical, automotive and energy manufacturers print metal parts with fewer cracks, less warping and fewer failed builds. This would cut costly trial-and-error testing and make metal 3D printing more economical for wider industrial use.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
CDS&E: Concurrently Coupling Data-Driven Surrogate with Finite Element Method for Accurate, Scalable Thermal Transport and Melt Pool Simulation of Laser Powder Bed Fusion
NSF · Aug 2026 – Jul 2029
Collaborative Research: Towards Intelligent Scan Sequence Generation to Reduce Local Overheating, Distortion, and Residual Stress in LPBF Additive Manufacturing
NSF · Jan 2025 – Dec 2027
Closest labs
Yue Zhou
Embry-Riddle Aeronautical University-Daytona Beach, FL·Engineering
Measure how directed energy deposition settings affect both short-range clustering and overall 3D uniformity of micro-scale ceramic particles in printed metal matrix composites, using controlled experiments and advanced characterization.
Yiliang Liao
Iowa State University, IA·Engineering
Determine how the evolving, uneven thermal environment of a hot liquid support bath controls polymer diffusion and interlayer fusion in embedded printing, using deep-neural-network molecular dynamics, phase-field modeling, and experiments.
Prahalada Rao
Virginia Polytechnic Institute and State University, VA·Engineering
Develop physics-informed machine learning models that predict outcomes of laser wire directed energy deposition (laser-and-wire metal 3D printing) and optimize laser power, scan speed and tool paths before printing.
The school
University of Pittsburgh: 83 labs on PI Pages, $1.5B a year on research, 403 research doctorates a year.
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