Jian Cao

Cardiss Collins Professor

Northwestern University

United States

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Jian Cao is the Cardiss Collins Professor at Northwestern University, USA. His research focuses on advanced manufacturing processes, additive manufacturing, and machine learning applications in materials science, as demonstrated by his recent articles. His work includes the exploration of martensite transformation in maraging steel, the design of polymer composites, and the integration of AI techniques for real-time decision-making in manufacturing systems.

Articles (28)

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Technical University of Denmark

DENMARK

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Jian Cao

Research Interests

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About

Articles (28)

Restricted variant selection in martensite transformation of maraging steel during additive manufacturing

Unifying machine learning and interpolation theory via interpolating neural networks

Inverse design of triaxial braid polymer composites using differentiable analytical approach verified by multiscale finite element method

Tensor-decomposition-based A Priori Surrogate (TAPS) modeling for ultra large-scale simulations

Transfer learning enabled geometry, process, and material agnostic RGNN for temperature prediction in directed energy deposition

Real-time decision-making for Digital Twin in additive manufacturing with Model Predictive Control using time-series deep neural networks

Efficient GPU-computing simulation platform JAX-CPFEM for differentiable crystal plasticity finite element method

AI-enabled manufacturing process discovery

Mold liners produced by incremental sheet forming

Understanding coaxial photodiode-based multispectral pyrometer measurements at the overhang regions in laser powder bed fusion for part qualification

Data-driven prediction of inter-layer process condition variations in laser powder bed fusion

Statistical parameterized physics-based machine learning digital shadow models for laser powder bed fusion process

Accelerating phase-field simulation of three-dimensional microstructure evolution in laser powder bed fusion with composable machine learning predictions

Artificial intelligence in metal forming

In-situ blended 316L-Si and PH48S via laser directed energy deposition for functionally graded applications

Machine learning guided adaptive laser power control in selective laser melting for pore reduction

JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science

Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification

Tensile strength prediction in directed energy deposition through physics-informed machine learning and Shapley additive explanations

Pore formation driven by particle impact in laser powder-blown directed energy deposition

Efficient GPU-accelerated thermomechanical solver for residual stress prediction in additive manufacturing

Differentiable simulation for material thermal response design in additive manufacturing processes

Melt pool instability detection using coaxial photodiode system validated by in-situ X-ray imaging

Modeling and simulation of surface generation in manufacturing

Simulation-guided feedforward-feedback control of melt pool temperature in directed energy deposition

Additive friction stir deposition of metallic materials: Process, structure and properties

Multiphysics Analysis and Verification of Jet Flight in Electrohydrodynamic Printing for Near-Field Electrospinning Applications

Cooling rate measurement in directed energy deposition using photodiode-based planck thermometry (PDPT)

Collaborators (8)

Shengmin Guo

Satyajit Mojumder

Ye Lu

Miguel Bessa

Jihoon Jeong

Samantha Webster

Clayton Cooper

GUIDO Tosello

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