Quantification and compensation of thermal distortion in additive manufacturing: a computational statistics approach

DOI10.1016/J.CMA.2020.113611zbMath1506.74244OpenAlexW3112775015MaRDI QIDQ2022058

Danielle Zeng, Chao Wang, Xinhai Zhu, Shaofan Li

Publication date: 27 April 2021

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2020.113611

zbMATH Keywords

artificial intelligenceadditive manufacturing3D printingdata-driven modelingcoherent point drift (CPD) methodintelligent manufacture

Mathematics Subject Classification ID

Dynamics of phase boundaries in solids (74N20) Stochastic and other probabilistic methods applied to problems in solid mechanics (74S60)

Related Items (5)

A generalized Bayesian regularization network approach on characterization of geometric defects in lattice structures for topology optimization in preliminary design of 3D printing ⋮ Data-driven prognostic model for temperature field in additive manufacturing based on the high-fidelity thermal-fluid flow simulation ⋮ A Bayesian regularization network approach to thermal distortion control in 3D printing ⋮ Modeling the temperature, crystallization, and residual stress for selective laser sintering of polymeric powder ⋮ Towards out of distribution generalization for problems in mechanics

Uses Software

• DistMesh

Cites Work

• Tutorial on maximum likelihood estimation
• Data-driven multi-scale multi-physics models to derive process-structure-property relationships for additive manufacturing
• Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
• A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
• Smoothed Particle Hydrodynamics
• A Simple Mesh Generator in MATLAB
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