Machine Learning Applied to Composite Materials

Contents
Applications of Machine Learning in the Field of Polymer Composites
	1 Introduction
	2 An Overview of the Applications of Machine Learning in Polymer Composites
	3 Generally Used Machine Learning Algorithms in Polymer Composites
		3.1 Neural Networks
		3.2 Logistic Regression
		3.3 Gaussian Process
		3.4 Support Vector Machine
	4 Conclusion and the Future Scope
	References
Image Processing and Machine Learning Methods Applied to Additive Manufactured Composites for Defect Detection and Toolpath Reconstruction
	1 Introduction
	2 Methods
		2.1 Sample Preparation
		2.2 μCT Scan Image
		2.3 Circular Image Dataset Preparation for ML Model Training
		2.4 Dataset for Orientation Prediction in Each Layer
		2.5 Machine Learning
		2.6 The Architectures of the Machine Learning Algorithms
	3 2D CNN Result
		3.1 1-Dimensional Convolutional Neural Network (1D CNN)
		3.2 1D CNN Result
	4 Summary
	References
AI/ML for Quantification  and Calibration of Property Uncertainty  in Composites
	1 Introduction and Prior Work in Modeling and Uncertainty Quantification
		1.1 Representation of Property Variations in Materials
		1.2 Sampling with Monte Carlo Methods
		1.3 Sampling with Reliability Methods
		1.4 Sampling with Spectral Stochastic Finite Element Methods
		1.5 Approaches for Micro- and Macro-UQ of Composite Performance
		1.6 Machine Learning Methods for UQ
	2 Enhancing Sample Efficiency Through Quasi Monte Carlo Simulations
		2.1 Application of QMC for the Modeling of Random Fields with KLE
	3 UQ Parameter Calibration with Neural Networks
		3.1 Karhunen-Loève Expansion
		3.2 Computational Model
		3.3 Parameter Estimation Using a Neural Network
		3.4 Network Architecture
		3.5 Experimental
		3.6 Neural Network Training
		3.7 Optimization of Neural Network Architecture
		3.8 Estimation of Experimental Correlation Lengths
	4 Concluding Remarks
	References
Radial Basis Function-Based Uncertain Low-Velocity Impact Behavior Analysis of Functionally Graded Plates
	1 Introduction
	2 Mathematical Formulation
	3 Radial Basis Function (RBF)
	4 RBF-Based Probabilistic Analysis of FGM Plates
	5 Results and Discussion
	6 Conclusions and Future Perspective
	References
Application of Machine Learning in Determining the Mechanical Properties of Materials
	1 Introduction
	2 Big-Data
	3 Machine Learning
	4 Tensile Strength
	5 Fatigue and Creep
	6 Conclusion
	References
Machine Learning Prediction for the Mechanical Properties of Lightweight Composite Materials
	1 Introduction
	2 Background of FRPs
	3 Non-destructive Testing for FRPs
	4 Overview of Machine Learning
	5 Machine-Learning Prediction of Mechanical Properties of FRPs
	6 Conclusion
	References
Ballistic Performance of Bi-layer Graphene: Artificial Neural Network Based Molecular Dynamics Simulations
	1 Introduction
	2 Modelling and Simulation
		2.1 MD Setup
		2.2 Artificial Neural Network
	3 Results and Discussion
	4 Conclusion and Future Perspective
	References
Quantifying the Sensitivity of Input Parameters in an ANN-Based Committee Networks Model for Estimation of Steel Girder Bridge Load-Ratings
	1 Introduction
	2 Methodology
		2.1 Bridge Datasets
		2.2 Load Rating
		2.3 FE Modeling and Calibration
		2.4 Committee Neural Networks (CN)
		2.5 Sensitivity Analysis of Network Inputs
	3 Discussion of Results
	4 Conclusions
	Appendix
	References
Estimating Axial Load Capacity of Concrete-Filled Double-Skin Steel Tubular Columns of Multiple Shapes Using Genetic Algorithm-Optimized Artificial Neural Networks
	1 Introduction
	2 FE Modeling and Simulation
		2.1 Element Selections
		2.2 Constitutive Material Models
		2.3 Loading and Boundary Conditions
		2.4 FE Modeling Validation
	3 Discussion of Results
		3.1 Datasets for Neural Network
		3.2 ANN Model Predictions
		3.3 Hybrid GA-ANN Model Predictions
	4 Conclusions
	Appendix 1
	Appendix 2
	References