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Ordinary Differential Equations with Machine Learning for Prediction of Smart Composite Fracture Toughness

RELEBOHILE GEORGE QHOBOSHEANE, MUTHU RAM PRABHU ELENCHEZHIAN, VAMSEE VADLAMUDI, KENNETH REIFSNIDER, RASSEL RAIHAN

Abstract


This work in on the development of an ordinary differential equation (ODE) model coupled with statistical methods for the prediction of fracture toughness of a magnetostrictive, piezoelectric smart self-sensing Fiber Reinforced Polymer (FRP) composite. The smart composite with sensing properties encompasses Terfenol-D alloy nanoparticles and Single Walled Carbon NanoTubes (SWCNT). To explore various configurations the of nanoparticle constituents’ effect on fracture toughness within the FRP composite, the ODE model developed within a finite element analysis (FEA) environment is considered to attain fracture observations across the solution space. The acquired FEA data is then used to feed the machine-learning (ML) algorithms to obtain composite fracture toughness predictions. A comparison and development of artificial neural networks (ANN), decision trees and support vector machines (SVM) models for FRP smart self-sensing composite fracture toughness prediction is done. Qualitative results stating if the sample has fractured or not and quantitative data giving the fracture toughness and strain energy release rate for the smart self-sensing FRP composites is attained. A comparison of all predictions from the developed models for both fracture toughness is corroborated with literature data.


DOI
10.12783/asc36/35820

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References


Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev & Aron Walsh. "Machine learning

for molecular and materials science." Nature volume 559 (2018): 547-555.

Jing Wei, Xuan Chu, Xiang-Yu Sun, Kun Xu, Hui-Xiong Deng, Jigen Chen, Zhongming Wei, Ming Lei.

"Machine learning in materials science." InfoMat 1.3 (2019).

Albert P. Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R. Kermode, Gábor Csányi and

Michele Ceriotti. "Machine learning unifies the modeling of materials and molecules." Science Advances

12 (2017).

Ghanshyam Pilania, Chenchen Wang, Xun Jiang, Sanguthevar Rajasekaran & Ramamurthy Ramprasad.

"Accelerating materials property predictions using machine learning." Scientific Reports 3 (2013).

Aykut, Şeref. "Surface Roughness Prediction in Machining Castamide Material Using ANN ." Acta

Polytechnica Hungarica 8.2 (2011).

A. Tugrul Seyhan, Gokmen Tayfur, Murat Karakurta and Meting Tanog'lu. "Artificial neural network

(ANN) prediction of compressive strength of VARTM processed polymer composites." Computational

Materials Science 34.1 (2005): 99-105.

Deepayan Gope, Prakash Chandra Gope, Aruna Thakur, Abhishek Yadav. "Application of artificial neural

network for predicting crack growth direction in multiple cracks geometry." Applied Soft Computing 30

(2015): 514-528.

K. Zarrabi, W.W. Lu, A.K. Hellier. "An Artificial Neural Network Approach to Fatigue Crack Growth."

Advanced Materials Research 275 (2011): 3-6.

Konstantin N. Nechval, Nicholas A. Nechval, Irina Bausova , Daina Šķiltere,Vladimir F. Strelchonok.

"PREDICTION OF FATIGUE CRACK GROWTH PROCESS VIA ARTIFICIAL NEURAL NETWORK

TECHNIQUE." International Scientific Journal of Computing 5.3 (2006): 1-12.

Seyedali Sadeghi, Mehdi Ahmadi Najafabadi, Yashar Javadi, Mohammadjavad Mohammadisefat. "Using

ultrasonic waves and finite element method to evaluate through-thickness residual stresses distribution in

the friction stir welding of aluminum plates." Materials & Design 52 (2013): 870-880.

Compass. "Standard Test Method for Mode I Interlaminar Fracture Toughness of Unidirectional Fiber-

Reinforced Polymer Matrix Composites." Active Standard ASTM D5528 (n.d.).

G. Colab. [Online]. Available: https://colab.research.google.com/notebooks/intro.ipynb?utm_source=scsindex.

[Accessed March 2021].

Python. [Online]. Available: https://www.python.org/. [Accessed February 2021].

Djalal Eddine KHODJA, Aissa KHELDOUN and Larbi REFOUFI. "Sigmoid Function Approximation for

ANN Implementation in FPGA Devices." Recent Researches in Circuits, Systems, Electronics, Control and

Signal Processing 9 (2010).

L. Breiman, "Random Forests," Machine Learning, vol. 45, pp. 5-32, 2001.

Pal, M. "Random forest classifier for remote sensing classification." International Journal of Remote

Sensing 26.1 (2005).

Ahmad Taher Azar, Hanaa Ismail Elshazly, Aboul Ella Hassanien, Abeer Mohamed Elkorany. "A random

forest classifier for lymph diseases." Computer Methods and Programs in Biomedicine 113.2 (2014): 465-

Achmad Widodo, Bo-Suk Yang. "Support vector machine in machine condition monitoring and fault

diagnosis." Mechanical Systems and Signal Processing 21.6 (2007): 2560-2574.

Rory Mitchell, Eibe Frank. "Accelerating the XGBoost algorithm using GPU computing." PeerJ Computer

Science (2017).


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