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.
Highlights
Selective laser sintering (SLS) is one of the most important 3D printing technique widely used in industry [1]
Main principle of the Decision Trees MethodsDecision trees (DT) is to use a series of rules to identify the regions with the most homogeneous output variables to input variables on which a constant is fitted to each region
artificial intelligence (AI) models are illustrated in Figure for theintraining
Summary
Selective laser sintering (SLS) is one of the most important 3D printing technique widely used in industry [1]. The main idea of this method is that powders are sintered by laser in order to produce solid objects predefined by 3D sketch models [2]. Using 3D sketches combined with laser sources, SLS technology could handle complex geometries of objects [3,4,5,6,7]. In industry of polymer processing, the SLS method exposure has great advantages, in terms of saving time in fabrication [12]. Combining particles and laser technique requires broadly information involving many multiphysical phenomena. (ii) it is powerful for mining nonlinear and interactions effects between dependent and independent variables, (iii) it requires no mathematical assumptions between output and input variables, and (iv) it is capable to handle missing values and outliers [42].
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