Machine Learning-Based Modeling of Metal Additive Manufacturing Processes Using Data Augmentation and Ensemble Learning

Document Type : Original Article

Authors

Nima Ayobi

Reza Mahboobi Esfanjani

Ahmad Akbari Alvanagh

Habib Badri Ghavifekr

, Department of Electrical and Computer Engineering – Control, Sahand University of Technology, Tabriz, Iran. Corresponding Author: Professor, Department of Electrical and Computer Engineering – Control, Sahand University of Technology, Tabriz, Iran.

10.22067/jmme.2025.91745.1177

Abstract

Although metal additive manufacturing offers many advantages, it is restricted by the repeatability and quality assurance of the produced parts. Accurate mathematical modeling of the mentioned process is essential to tackle these limitations. Traditional mathematical modeling approaches are inefficient in describing this complicated system. Machine learning is a novel data-based modeling approach to discover hidden patterns in nonlinear systems. However, applying this scheme in the metal additive manufacturing process is challenging due to the limited data samples. This paper combines machine learning and data augmentation to tackle the mentioned issues and model two well-known metal additive manufacturing processes, Selective Laser Melting and Laser Direct Metal Deposition. Ensemble learning is utilized here to improve the model's accuracy. Simulation results demonstrate the effectiveness of the proposed method in modeling the metal additive manufacturing processes. The performance of the proposed models has been improved by 53% and 48%, respectively, compared to the case without data augmentation.

Keywords

Metal additive manufacturing

Machine learning

Data augmentation

Ensemble learning

Modeling

Subjects

• Metal Forming

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