Guo, L;
Liu, H;
Wang, H;
Wei, Q;
Zhang, J;
Chen, Y;
Leung, CLA;
... Wang, H; + view all
(2023)
A high-fidelity comprehensive framework for the additive manufacturing printability assessment.
Journal of Manufacturing Processes
, 105
pp. 219-231.
10.1016/j.jmapro.2023.09.041.
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Abstract
Additive manufacturing is capable of fabricating complex and customized components that cannot be easily and economically produced by other techniques. It is of great significance to determine the printability map for the extensive application of the fabricated parts, which has been hindered by the common defects such as interior pores and surface roughness. Here, a comprehensive framework including multiphysics model, physics-informed machine learning, and experimental data is proposed to predict the printability. The characteristics for different phenomena (lack of fusion, balling and keyhole) are analyzed by the mechanistic model considering high-fidelity powder-scale model, fluid flow, recoil pressure and Marangoni effect, which provides a more accurate thermal history, molten pool dynamics and surface morphology compared to the finite element model. Classification criterion is established by three mechanistic variables based on the molten pool morphology, which divides the process map into four regions. For the first time, the relationship between the solidified-track surface morphology and the interior quality is established, and the optimal surface morphology corresponding to defect-free printing is determined. The printability is predicted by mathematical machine learning classification models via 10-fold cross-validation method, which validates the classification criterion and the comprehensive framework to assess the printability. This high-fidelity comprehensive framework to assess the printability could potentially guide the alloy design and printing parameters selection towards mass additive manufacturing production.
| Type: | Article |
|---|---|
| Title: | A high-fidelity comprehensive framework for the additive manufacturing printability assessment |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.jmapro.2023.09.041 |
| Publisher version: | https://doi.org/10.1016/j.jmapro.2023.09.041 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Printability, Simulation, Laser powder bed fusion, Machine learning, Surface topography |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10178986 |
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