Deep learning based nanoindentation method for evaluating mechanical properties of polymers

Park, Soowan; Marimuthu, Karuppasamy Pandian; Han, Giyeol; Lee, Hyungyil

doi:10.1016/j.ijmecsci.2023.108162

Cited by 24 publications

(4 citation statements)

References 60 publications

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“…Randomized grid search overcomes these disadvantages by random selection of value at each iteration and provides best performing values after several iterations [51]. This random movement through hyperparameter values is avoided in Bayesian Optimization technique that uses prior knowledge and applies probabilistic function for selection of next hyperparameter setting [22]. The hyperparameters of ANN model are optimized using Grid Search five-fold cross-validation algorithm in the present study as the number of hyperparameter values are less.…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Ghosh,

Sahu,

Dey

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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Nowadays, anodized coating on additively manufactured (AM) or 3D printed Al-10Si-Mg alloy are used for various components in spacecraft such as antenna feeds, wave guides, structural brackets, collimators, thermal radiators etc. In this study, Artificial Neural Network (ANN) and Power law-based models are developed from experimental nanoindentation data for predicting elastic modulus and hardness of anodized AM Al-10Si-Mg at any desired loads. Data from nanoindentation experiments conducted on plan- and cross-sections of anodized coating on AM Al-10Si-Mg alloy was considered for modelling. Apart from nanomechanical properties, load and displacement curves were predicted using Python software from ANN and the Power law model of nanoindentation. It is observed that the ANN model of 50 mN nanoindentation experimental data can accurately predict the loading pattern at any desired load below 50 mN. Elastic modulus and hardness of anodized AM Al-10Si-Mg computed from ANN and the power law model of the unloading curve are also comparable with the values obtained from Weibull distribution analysis reported elsewhere. The derived models were also used to predict nanomechanical properties at 25 and 35 mN, for which no experimental data was available. The computed hardness of plan section of the anodic coating is 3.99 and 4.02 GPa for 25 and 35 mN, respectively. The computed hardness of cross-section of the anodic coating of is 7.16 and 6.61 GPa for 25 and 35 mN, respectively. Thus, the ANN and Power law model of nanoindentation can predict elastic modulus and hardness at different loads by conducting the minimum number of experiments. The novel approach to predict nanomechanical properties using ANN resulted in determining realistic and design specific data on hardness and modulus of the anodized coating on AM Al-10Si-Mg alloy.

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Section: Hyperparameter Tuningmentioning

confidence: 99%

Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Ghosh,

Sahu,

Dey

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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“…The structure parameters of polymeric materials can be used as inputs, with material performance as outputs, to construct a neural network model. This model can then be optimized to improve the molecular structure of the polymeric material [39]. Additionally, neural networks can utilize existing functional material data for learning and predicting intriguing new materials to guide the functional design of polymer composites.…”

Section: Artificial Neural Network In Polymer Compositesmentioning

confidence: 99%

Application of Artificial Neural Networks in Polymer Composites: A Review

Niu,

Zhang

2023

AJRCoS

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Artificial neural networks (ANN), which have been a hot topic in the field of artificial intelligence (AI) since the 1980s, are widely applied these years for their strong ability in the field of nonlinear mapping, pattern recognition, robots, automatic control, biology, economy and so on. This review presents and summarizes the history of artificial neural networks, briefly introducing the application of artificial neural networks. After that, the paper focuses on an overview of research advances in neural networks for polymer composites and introduces several classical categories of applications. Finally, we look ahead to the development of neural network applications in polymer composites and provide a future outlook for the application of artificial neural network in polymer composites.

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“…By using an efficient model, industrial demands can be met more effectively by avoiding or reducing the time-consuming and labor-intensive trialand-error process that involves costly experimental investigations, benefiting inspection, testing, and manufacturing processes [18]. Machine learning has been applied to predict the mechanical properties of various polymers, such as polycarbonate (PC), polymethylmethacrylate (PMMA), aluminum alloys, polypropylene (PP), and cotton fiber [18][19][20][21]. However, few studies that specifically address the prediction of the mechanical properties for PLA biopolymer films/membranes have been conducted.…”

Section: Introductionmentioning

confidence: 99%

“…Park et al [19] developed a deep learning neural network (DLNN) model with a 4-(133-200)-4 architecture to predict material properties of PC and PMM using 200 data points that were generated by finite element (FE) simulation and the Latin hypercube sampling (LHS) algorithm. Whether the NN model is equally effective in predicting the simulated and experimental data points cannot be concluded based this study, as only two experimental data points for PC and PMM were used.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Mechanical Properties of a Polymer-Based Mixed-Matrix Membrane Using Deep Learning Neural Networks

Alhulaybi,

Martuza,

Rushd

2023

ChemEngineering

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Polylactic acid (PLA), the second most produced biopolymer, was selected for the fabrication of mixed-matrix membranes (MMMs) via the incorporation of HKUST-1 metal–organic framework (MOF) particles into a PLA matrix with the aim of improving mechanical characteristics. A deep learning neural network (DLNN) model was developed on the TensorFlow 2 backend to predict the mechanical properties, stress, strain, elastic modulus, and toughness of the PLA/HKUST-1 MMMs with different input parameters, such as PLA wt%, HKUST-1 wt%, casting thickness, and immersion time. The model was trained and validated with 1214 interpolated datasets in stratified fivefold cross validation. Dropout and early stopping regularizations were applied to prevent model overfitting in the training phase. The model performed consistently for the unknown interpolated datasets and 26 original experimental datasets, with coefficients of determination (R2) of 0.93–0.97 and 0.78–0.88, respectively. The results suggest that the proposed method can build effective DLNNmodels using a small dataset to predict material properties.

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Deep learning based nanoindentation method for evaluating mechanical properties of polymers

Cited by 24 publications

References 60 publications

Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Application of Artificial Neural Networks in Polymer Composites: A Review

Modeling the Mechanical Properties of a Polymer-Based Mixed-Matrix Membrane Using Deep Learning Neural Networks

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