Research on Dynamic Mechanical Properties of Two-Phase Composites Based on Convolutional Neural Network

BU Lehu; WANG Pengfei; WU Yangfan; WANG Deya; XU Songlin

doi:10.11858/gywlxb.20230601

Volume 37 Issue 3

Jun 2023

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Chinese Journal of High Pressure Physics > 2023 > 37(3): 034201.

BU Lehu, WANG Pengfei, WU Yangfan, WANG Deya, XU Songlin. Research on Dynamic Mechanical Properties of Two-Phase Composites Based on Convolutional Neural Network[J]. Chinese Journal of High Pressure Physics, 2023, 37(3): 034201. doi: 10.11858/gywlxb.20230601

Citation:

BU Lehu, WANG Pengfei, WU Yangfan, WANG Deya, XU Songlin. Research on Dynamic Mechanical Properties of Two-Phase Composites Based on Convolutional Neural Network[J]. Chinese Journal of High Pressure Physics, 2023, 37(3): 034201. doi: 10.11858/gywlxb.20230601

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Research on Dynamic Mechanical Properties of Two-Phase Composites Based on Convolutional Neural Network

doi: 10.11858/gywlxb.20230601

CAS Key Laboratory for Mechanical Behavior and Design of Materials, University of Science and Technology of China, Hefei 230027, Anhui, China

Received Date: 08 Jan 2023
Rev Recd Date: 08 Feb 2023

Available Online: 19 Jun 2023

Issue Publish Date: 05 Jun 2023

Abstract

Abstract

Additive manufacturing technology has promoted the development of composite materials and broadened the design space of composite structures. However, the dynamic mechanical properties of composite materials based on additive manufacturing still face problems such as lack of research methods and complex design processes. The split Hopkinson pressure bar (SHPB) experimental technique and ABAQUS finite element simulation were used to study the dynamic mechanical behavior of two-phase composites printed by light-cured 3D, combined with the principal component analysis (PCA) to establish composite structure datasets, and the relationship between the composite structures and the stress-strain curves were learned through a high-performance convolutional neural network (CNN). The research results showed that the finite element model containing interface elements was more suitable for simulating the dynamic mechanical response of composites, and the predictive performance of CNN could be improved by setting hyperparameters. Based on the structure, the trained CNN could quickly predict the dynamic stress-strain curve of the composites. This study provides a reference for the design and application of machine learning in the dynamic performance of composites.
- composite materials,
- 3D printing,
- principal component analysis,
- convolutional neural network,
- dynamic stress-strain curve

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References

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Research on Dynamic Mechanical Properties of Two-Phase Composites Based on Convolutional Neural Network

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