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Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees

1
University of Transport Technology, Hanoi 100000, Vietnam
2
PIMM, ENSAM, CNRS, CNAM, HESAM Université, 151 Boulevard de l’Hôpital, 75013 Paris, France
3
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
4
NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam
*
Authors to whom correspondence should be addressed.
Materials 2019, 12(9), 1544; https://doi.org/10.3390/ma12091544
Received: 31 March 2019 / Revised: 22 April 2019 / Accepted: 6 May 2019 / Published: 10 May 2019
(This article belongs to the Special Issue Selective Laser Sintering (SLS) of Materials)
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Abstract

The presence of defects like gas bubble in fabricated parts is inherent in the selective laser sintering process and the prediction of bubble shrinkage dynamics is crucial. In this paper, two artificial intelligence (AI) models based on Decision Trees algorithm were constructed in order to predict bubble dissolution time, namely the Ensemble Bagged Trees (EDT Bagged) and Ensemble Boosted Trees (EDT Boosted). A metadata including 68644 data were generated with the help of our previously developed numerical tool. The AI models used the initial bubble size, external domain size, diffusion coefficient, surface tension, viscosity, initial concentration, and chamber pressure as input parameters, whereas bubble dissolution time was considered as output variable. Evaluation of the models’ performance was achieved by criteria such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and coefficient of determination (R2). The results showed that EDT Bagged outperformed EDT Boosted. Sensitivity analysis was then conducted thanks to the Monte Carlo approach and it was found that three most important inputs for the problem were the diffusion coefficient, initial concentration, and bubble initial size. This study might help in quick prediction of bubble dissolution time to improve the production quality from industry. View Full-Text
Keywords: 3D selective laser sintering; artificial intelligence; decision trees; bubble dissolution time; sensitivity analysis 3D selective laser sintering; artificial intelligence; decision trees; bubble dissolution time; sensitivity analysis
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Ly, H.-B.; Monteiro, E.; Le, T.-T.; Le, V.M.; Dal, M.; Regnier, G.; Pham, B.T. Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees. Materials 2019, 12, 1544.

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