Strain rate dependent mechanical properties of a high‐strength poly(methyl methacrylate)

Fan, Jihui

doi:10.1002/app.46189

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…Eyring’s equation can be used to illustrate the strength of polymer materials related to the applied strain rate and inherent material parameters [54,55], which is expressed as seen below:σyT=ΔUvT+Rvln[2trueε˙e0], where σy is the yield stress; T is the ambient temperature; ΔU is the activation energy of plastic deformation/flow, i.e., the height of the potential energy barrier of two adjacent equilibrium positions for element units to jump; v is the activation volume of element motion unit; ε˙ is the strain rate; e0 is the pre-exponential factor; and R is the gas constant. Under dynamic loading at a given strain rate, yield stress is correlated with the material parameters of ΔU and v.…”

Section: Dynamic Experimental Results and Discussionmentioning

confidence: 99%

“…Along the loading direction, numerous deformation bands are formed and elongated, which construct a uniform parallel pattern (see Figure 7b). These deformation bands are expected to be induced by lateral tensile stress upon an applied dynamic compressive loading [16,55], which carry the considerable strain. By measurement, they have an average width of about 70 µm.…”

Section: Dynamic Experimental Results and Discussionmentioning

confidence: 99%

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Studying a Flexible Polyurethane Elastomer with Improved Impact-Resistant Performance

Fan

Chen

2019

Polymers

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A flexible polyurethane elastomer (PUE) is studied, and the improved impact-resistant performance is revealed. Compressive stress–strain curves over a wide loading rate range were derived. Under static loading, the rubbery-like characteristics are demonstrated, which are flexible and hyperelastic, to process a large strain of about 60% followed by full recovery upon unloading. Under high-rate loadingcompared with the mechanical data of polyurethane elastomer (PUE) and polyurea (PUA) materials in the literature. Orderly parallel deformation bands were formed from carrying a large strain. The fibrils were found between deformation bands for enhancing the yield/plateau stress. A considerable plastic zone ahead of propagating crack with numerous crazes and microcracks was produced for realizing the dynamic strain energy absorption. This work presents a scientific innovation for developing outstanding impact-resistant polyurethane elastomers for transparent protection engineering.

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Section: Dynamic Experimental Results and Discussionmentioning

confidence: 99%

Section: Dynamic Experimental Results and Discussionmentioning

confidence: 99%

Studying a Flexible Polyurethane Elastomer with Improved Impact-Resistant Performance

Fan

Chen

2019

Polymers

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“…When the glass transition temperature is lower than room temperature, the polymers are rigid, like polymethyl methacrylate. They also can be applied for energy absorption after microstructural modifications [ 67 , 68 , 69 , 70 , 71 ]. Therefore, this work offers an intellectual method for determining the glass transition temperature of polymers for applications as absorbent materials of mechanical energy.…”

Section: Discussionmentioning

confidence: 99%

Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers

Uddin,

Fan

2024

Polymers

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The glass transition temperature of polymers is a key parameter in meeting the application requirements for energy absorption. Previous studies have provided some data from slow, expensive trial-and-error procedures. By recognizing these data, machine learning algorithms are able to extract valuable knowledge and disclose essential insights. In this study, a dataset of 7174 samples was utilized. The polymers were numerically represented using two methods: Morgan fingerprint and molecular descriptor. During preprocessing, the dataset was scaled using a standard scaler technique. We removed the features with small variance from the dataset and used the Pearson correlation technique to exclude the features that were highly connected. Then, the most significant features were selected using the recursive feature elimination method. Nine machine learning techniques were employed to predict the glass transition temperature and tune their hyperparameters. The models were compared using the performance metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). We observed that the extra tree regressor provided the best results. Significant features were also identified using statistical machine learning methods. The SHAP method was also employed to demonstrate the influence of each feature on the model’s output. This framework can be adaptable to other properties at a low computational expense.

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A Macro-Mechanical Study for Capturing the Dynamic Behaviors of a Rate-Dependent Elastomer and Clarifying the Energy Dissipation Mechanisms at Various Strain Rates

Ali

Fan

Feng³

et al. 2021

Acta Mech. Solida Sin.

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Strain rate dependent mechanical properties of a high‐strength poly(methyl methacrylate)

Cited by 7 publications

References 34 publications

Studying a Flexible Polyurethane Elastomer with Improved Impact-Resistant Performance

Studying a Flexible Polyurethane Elastomer with Improved Impact-Resistant Performance

Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers

A Macro-Mechanical Study for Capturing the Dynamic Behaviors of a Rate-Dependent Elastomer and Clarifying the Energy Dissipation Mechanisms at Various Strain Rates

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