Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates

Hu, Haichao1,2; Wei, Qiang1,3; Wang, Tianao2; Ma, Quanjin4; Jin, Peng2; Pan, Shupeng2; Li, Fengqi2; Wang, Shuxin2; Yang, Yuxuan2; Li, Yan5

2024-06-01

10.3390/polym16111589

POLYMERS   影响因子和分区

2073-4360

This study unveils a machine learning (ML)-assisted framework designed to optimize the stacking sequence and orientation of carbon fiber-reinforced polymer (CFRP)/metal composite laminates, aiming to enhance their mechanical properties under quasi-static loading conditions. This work pioneers the expansion of initial datasets for ML analysis in the field by uniquely integrating the experimental results with finite element simulations. Nine ML models, including XGBoost and gradient boosting, were assessed for their precision in predicting tensile and bending strengths. The findings reveal that the XGBoost and gradient boosting models excel in tensile strength prediction due to their low error rates and high interpretability. In contrast, the decision trees, K-nearest neighbors (KNN), and random forest models show the highest accuracy in bending strength predictions. Tree-based models demonstrated exceptional performance across various metrics, notably for CFRP/DP590 laminates. Additionally, this study investigates the impact of layup sequences on mechanical properties, employing an innovative combination of ML, numerical, and experimental approaches. The novelty of this study lies in the first-time application of these ML models to the performance optimization of CFRP/metal composites and in providing a novel perspective through the comprehensive integration of experimental, numerical, and ML methods for composite material design and performance prediction.

machine learning stacking sequence orientation selections mechanical properties finite element modeling prediction strategy

SCI ; EI

英语

其他

Polymer Science

Polymer Science

WOS:001245561800001

MDPI

Web of Science

1.Hebei Univ Technol, Sch Mat Sci & Engn, Tianjin 300401, Peoples R China
2.Tianjin Sino German Univ Appl Sci, Sch Mech & Engn, Tianjin 300350, Peoples R China
3.Hebei Univ Technol, Sch Mech & Engn, Tianjin 300401, Peoples R China
4.Southern Univ Sci & Technol, Sch Syst Design & Intelligent Mfg, Shenzhen 518055, Peoples R China
5.Tianjin Sino German Univ Appl Sci, Tianjin Sino Spanish Machining Tool Vocat Training, Tianjin 300350, Peoples R China

Hu, Haichao,Wei, Qiang,Wang, Tianao,et al. Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates[J]. POLYMERS,2024,16(11).

Hu, Haichao.,Wei, Qiang.,Wang, Tianao.,Ma, Quanjin.,Jin, Peng.,...&Li, Yan.(2024).Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates.POLYMERS,16(11).

Hu, Haichao,et al."Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates".POLYMERS 16.11(2024).