Prediction of in-plane mechanical properties of auxetic honeycombs based onmachine learning

MA Pei; ZHANG Junhua; QUAN Tiehan

Volume 41 Issue 7

Jul. 2024

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MA Pei, ZHANG Junhua, QUAN Tiehan. Prediction of in-plane mechanical properties of auxetic honeycombs based onmachine learning[J]. Acta Materiae Compositae Sinica, 2024, 41(7): 3806-3815.

Citation:

MA Pei, ZHANG Junhua, QUAN Tiehan. Prediction of in-plane mechanical properties of auxetic honeycombs based onmachine learning[J]. Acta Materiae Compositae Sinica, 2024, 41(7): 3806-3815.

MA Pei, ZHANG Junhua, QUAN Tiehan. Prediction of in-plane mechanical properties of auxetic honeycombs based onmachine learning[J]. Acta Materiae Compositae Sinica, 2024, 41(7): 3806-3815.

Citation:

MA Pei, ZHANG Junhua, QUAN Tiehan. Prediction of in-plane mechanical properties of auxetic honeycombs based onmachine learning[J]. Acta Materiae Compositae Sinica, 2024, 41(7): 3806-3815.

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Prediction of in-plane mechanical properties of auxetic honeycombs based onmachine learning

School of Mechanical Engineering, Beijing Information Science and Technology University, Beijing 100192, China

Funds: National Natural Science Foundation of China (12272057; 12372004)

Received Date: 2023-09-11
Accepted Date: 2023-10-27
Rev Recd Date: 2023-10-15

Available Online: 2023-11-11

Publish Date: 2024-07-15

Abstract

Abstract

It is well known that honeycombs with negative Poisson’s ratio have excellent mechanical properties. In this paper, two kinds of multi-input and multi-output artificial neural network models (ANN) were developed and compared to predict the energy absorption characteristics of honeycombs with negative Poisson's ratio under different geometric parameters. The cell angle θ, the ratio of straight wall length to cell height L/H and the thickness t of honeycomb cells are used as the inputs of ANN, and the outputs are the initial peak force, platform force and total energy absorption of honeycombs. The error in the verification set is all within 8%, and the average correlation coefficient R² of the verification set and the test set is greater than 98.2%, which shows that the neural network can obtain good prediction effect and it has the ability to learn and capture the potential physical mechanism that relates the topology structure and mechanical properties of the honeycombs. Compared with ANN1, ANN2 has more network parameters, more complex network structure and better prediction accuracy and training speed. By quickly predicting the mechanical properties of the honeycomb with given geometric parameters, an optimized honeycomb was obtained. A reversed design network was established to reverse design the honeycomb, and it is found that the network has a good prediction effect on the cell angle θ and wall thickness t of the honeycomb, but the prediction effect of L/H is relatively weak, because the parameter L/H has little effect on the initial peak force, platform force and total energy absorption. In addition, the sensitivity analysis of input parameters was carried out. The results show that the sensitivity trend of geometric parameters of the honeycombs to initial peak force, platform force and total energy absorption is the same, the sensitivity of honeycomb cell thickness t is the highest, and the ratio of straight wall length to cell height L/H is the lowest. The reverse design network has good prediction performance for parameters with high sensitivity, while the prediction performance for parameters with low sensitivity is relatively poor. In a word, ANN provides a fast and accurate method for the study of energy absorption performance of honeycombs, which is expected to accelerate the optimization and design process of honeycomb structures.
- Negative Poisson’s ratio honeycomb,
- energy absorption,
- neural network,
- parameter sensitivity,
- machine learning
Long Abstrsct
Innovation Points

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References(34)

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