Application of machine learning methods on dynamic strength analysis for additive manufactured polypropylene-based composites

• Main Authors: Ahzi, S. (Author), Baniassadi, M. (Author), Cai, R. (Author), Peng, Y. (Author), Wang, K. (Author), Wen, W. (Author)
• Format: Article
• Language: English
• Published: Elsevier Ltd 2022
• Subjects: 3D printers; Additive manufacturing; Additives; Computational efficiency; Decision trees; Dynamic materials; Dynamic strength; Efficiency; Forecasting; Machine learning; Machine learning algorithms; Machine learning approaches; Machine learning methods; Neural networks; ON dynamics; Performance; Polypropylene based composites; Polypropylene-based composites; Polypropylenes; Prediction; Split Hopkinson pressure bars; Strength analysis
• Online Access: View Fulltext in Publisher
• ISBN: 01429418 (ISSN)
• DOI: 10.1016/j.polymertesting.2022.107580

This study aimed at applying machine learning (ML) methods to analyze dynamic strength of 3D-printed polypropylene (PP)-based composites. The dynamic strength of additive manufactured PP-based composites with different fillers and printing parameters was investigated by split Hopkinson pressure bars. Based on experimental results, six machine learning approaches were applied to express the relationships between the dynamic strength and materials as well as printing parameters. The performance of the six machine learning algorithms with relatively small training datasets was evaluated. The comparison results showed that artificial neural network could achieve the highest prediction accuracy but with relatively low computational efficiency, whereas the support vector regression could provide satisfactory prediction with both good accuracy and efficiency. The extreme gradient boosting and random forest approaches were recommended if the importance of input was required. © 2022