A Surrogate Model's Decision Tree Method Evaluation for Uncertainty Quantification on a Finite Element Structure via a Fuzzy-Random Approach

Mohamad Syazwan Zafwan bin Mohamad Suffian; Syahiir Kamil; Ahmad Kamal Ariffin; Abdul Hadi Azman; Israr M. Ibrahim; Kazuhiro Suga

doi:10.59796/jcst.V14N3.2024.50

Authors

Mohamad Syazwan Zafwan bin Mohamad Suffian Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak 94300 Malaysia
Syahiir Kamil Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor 43600 Malaysia
Ahmad Kamal Ariffin Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor 43600 Malaysia
Abdul Hadi Azman Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor 43600 Malaysia
Israr M. Ibrahim Department of Mechanical and Industrial Engineering, Universitas Syiah Kuala (USK), Banda Aceh 23111 Indonesia
Kazuhiro Suga Department of Mechanical Engineering, Faculty of Engineering, Kogakuin University, Shinjuku, Tokyo 163-8677 Japan

DOI:

https://doi.org/10.59796/jcst.V14N3.2024.50

Keywords:

decision tree, finite element method, gaussian process regression, machine learning, surrogate model, uncertainty analysis

Abstract

A novel additive manufacturing method (AM)) constructs a three-dimensional model from a computer-aided design by adding material layer by layer. This technique produces a lightweight end product with complex geometries and has gained recognition among industrial players. Nonetheless, the mechanical properties and geometry components are the uncertainties that prevail in its structures. An alternative approach using the Finite Element Method (FEM) to analyse these uncertainties demands extensive computational effort and time consumption. Therefore, a machine learning (ML) tool using the surrogate modelling technique offers an alternative way to provide and predict simulation outcomes. This study applies two surrogate modeling approaches, the decision tree (DT) and the Gaussian process regression (GPR) methods. Output data from a FEM simulation with uncertainty elements are obtained for the training purposes of the surrogate models. Both ML methods can predict simulation results with high precision. Both approaches obtained an excellent coefficient of determination value, R² of 0.998, and Root Mean Square Error, RMSE of 0.012, successfully reducing time consumption and computational effort. The DT method shows better robustness when compared to the GPR method. A value change in the input parameter significantly impacts the surrogate model's prediction performance. An adequate quantity of data input for the training phase of both surrogate models exhibits the FEM results with the presence of uncertainty and robustness.

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