Predicting Flexural Strength of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites Using Machine Learning

Carbon fiber-reinforced polymer (CFRP) composites have been used extensively in the aerospace and automotive industries due to their high strength-to-weight and stiffness-to-weight ratios. Compared with conventional manufacturing processes for CFRP, additive manufacturing (AM) can facilitate the fabrication of CFRP components with complex structures. While AM offers significant advantages over conventional processes, establishing the structure–property relationships in additively manufactured CFRP remains a challenge because the mechanical properties of additively manufactured CFRP depend on many design parameters. To address this issue, we introduce a data-driven modeling approach that predicts the flexural strength of continuous carbon fiber-reinforced polymers (CCFRP) fabricated by fused deposition modeling (FDM). The predictive model of flexural strength is trained using machine learning and validated on experimental data. The relationship between three structural design factors, including the number of fiber layers, the number of fiber rings as well as polymer infill patterns, and the flexural strength of the CCFRP specimens is quantified.

[1]  Dazhong Wu,et al.  Tensile and flexural behaviors of additively manufactured continuous carbon fiber-reinforced polymer composites , 2019, Composite Structures.

[2]  Viacheslav Balobanov,et al.  Modelling size-dependent bending, buckling and vibrations of 2D triangular lattices by strain gradient elasticity models: Applications to sandwich beams and auxetics , 2018, International Journal of Engineering Science.

[3]  Zhi-Hua Zhou,et al.  Structural diversity for decision tree ensemble learning , 2018, Frontiers of Computer Science.

[4]  D. Lin,et al.  A review on additive manufacturing of polymer-fiber composites , 2017 .

[5]  Lifeng Wang,et al.  Bending behavior of sandwich composite structures with tunable 3D-printed core materials , 2017 .

[6]  K. McDonnell,et al.  Fabrication of Continuous Carbon, Glass and Kevlar fibre reinforced polymer composites using Additive Manufacturing , 2017 .

[7]  A. E. Ikpe,et al.  Design optimization of a B-pillar for crashworthiness of vehicle side impact , 2017 .

[8]  L. F. Silva,et al.  Mode II fracture toughness of CFRP as a function of temperature and strain rate , 2017 .

[9]  Xin Wang,et al.  3D printing of polymer matrix composites: A review and prospective , 2017 .

[10]  Yingguang Li,et al.  Rapid prototyping of continuous carbon fiber reinforced polylactic acid composites by 3D printing , 2016 .

[11]  Dichen Li,et al.  Interface and performance of 3D printed continuous carbon fiber reinforced PLA composites , 2016 .

[12]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[13]  Klaus-Dieter Thoben,et al.  Machine learning in manufacturing: advantages, challenges, and applications , 2016 .

[14]  W. Cong,et al.  Additive manufacturing of carbon fiber reinforced thermoplastic composites using fused deposition modeling , 2015 .

[15]  W. Cong,et al.  Additive Manufacturing of CFRP Composites Using Fused Deposition Modeling: Effects of Carbon Fiber Content and Length , 2015 .

[16]  Nam Soo Kim,et al.  Thermo-mechanical Characterization of Metal/Polymer Composite Filaments and Printing Parameter Study for Fused Deposition Modeling in the 3D Printing Process , 2015, Journal of Electronic Materials.

[17]  Mark Holmes,et al.  Global carbon fibre market remains on upward trend , 2014 .

[18]  L. Love,et al.  The importance of carbon fiber to polymer additive manufacturing , 2014 .

[19]  T. Hastie,et al.  Generalized Additive Models , 2014 .

[20]  R. Wicker,et al.  Fracture Surface Analysis of 3D-Printed Tensile Specimens of Novel ABS-Based Materials , 2014, Journal of Failure Analysis and Prevention.

[21]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[22]  Zhiwei Zhou.  Ensemble Methods: Foundations and Algorithms , 2012 .

[23]  H Wong,et al.  An experimental study of the mechanical properties of fibre reinforced polymer (FRP) and steel reinforcing bars at elevated temperatures , 2007 .

[24]  Alan Y. Chiang,et al.  Generalized Additive Models: An Introduction With R , 2007, Technometrics.

[25]  Kurt Hornik,et al.  Support Vector Machines in R , 2006 .

[26]  D. Ernst,et al.  Extremely randomized trees , 2006, Machine Learning.

[27]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[28]  T. Keller,et al.  Plate bending behavior of a pultruded GFRP bridge deck system , 2004 .

[29]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[30]  A. Machida,et al.  Fiber-Reinforced Polymer Composites for Construction—State-of-the-Art Review , 2002 .

[31]  S. Ramakrishna,et al.  Biomedical applications of polymer-composite materials: a review , 2001 .

[32]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[33]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[34]  Xiaobo Liu,et al.  Ensemble Transfer Learning Algorithm , 2018, IEEE Access.

[35]  Zheng Wang,et al.  Manufacturing and investigation of steel-CFRP hybrid pillar structures for automotive applications by intrinsic resin transfer moulding technology , 2016 .

[36]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2001, Springer Series in Statistics.

[37]  G. Kretsis,et al.  A review of the tensile, compressive, flexural and shear properties of hybrid fibre-reinforced plastics , 1987 .