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

By Hai-Bang Ly, Eric Monteiro, Tien-Thinh Le, Vuong Minh Le, M. Dal, G. Régnier, Binh Thai Pham. 2019

79 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Mechanical Engineering, Water Science and Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Binh Thai Pham
2. Hai-Bang Ly

In Mechanical Engineering

1. Indra Prakash
2. Binh Thai Pham
3. Octavio Loyola-González
4. Khabat Khosravi
5. D. Tien Bui

Citations in the corpus

• Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “drug codes for analyzing chronic patients [116], medical diagnosis [117], diagnosis of sport injuries [118], prediction and sensitivity analysis of bubble dissolution time in 3D selective laser sintering [119], and part-of-speech tagging [120]; among others.”
• Additive Manufacturing: A Comprehensive Review (2024, background): “Artificial intelligence (AI) models are currently being developed to control the SLS device and decrease the amount of imperfections in the final product by performing different analyses and performing corrective responses to a condition in which a defect would result from [63].”
• Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil (2021, methodology): “In this paper, standard statistical measures, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were used to compare and validate the performance of ML models [47, 95].”