Expectation maximization for 3D complex geometry modeling
Expectation maximization and GMM for statistical estimation and modeling of the multimodal probability distribution function of melt pools
This toolbox includes codes and the example to simulate the emission of photons for statistical estimation and modeling of the multimodal probability distribution function (PDF) of melt pools that are sensed and measured in the Laser powder bed fusion (LPBF) process. LPBF is a key technology of additive manufacturing that enables the fabrication of metal parts with complex geometry through a multi-layer process. Multimodal PDFs provide new characterization of meltpool morphology and geometry that are eminent indicators for manufacturing process stability and part quality.
Author: Runsang Liu and Hui Yang
Affiliation:
The Pennsylvania State University
310 Leohard Building, University Park, PA
Email: yanghui@gmail.com
If you find this toolbox useful, please cite the following paper:
[1] R. Liu and H. Yang. (2023). Multimodal probabilistic modeling of melt pool geometry variations in additive manufacturing. Additive Manufacturing, Vol. 61, p.103375. DOI: 10.1016/j.addma.2022.103375
引用格式
Hui Yang (2026). Expectation maximization for 3D complex geometry modeling (https://ww2.mathworks.cn/matlabcentral/fileexchange/178769-expectation-maximization-for-3d-complex-geometry-modeling), MATLAB Central File Exchange. 检索时间: .
Liu, Runsang, and Hui Yang. “Multimodal Probabilistic Modeling of Melt Pool Geometry Variations in Additive Manufacturing.” Additive Manufacturing, vol. 61, Elsevier BV, Jan. 2023, p. 103375, doi:10.1016/j.addma.2022.103375.
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