Investigating Deep Learning-Based Stress Prediction in Particulate Polymer Composites Using Multiple Quality Measures DOI
Sristi Gupta, T. Mukhopadhyay, Divyesh Varade

et al.

Springer proceedings in physics, Journal Year: 2024, Volume and Issue: unknown, P. 101 - 107

Published: Jan. 1, 2024

Language: Английский

Design and Kinematic Analysis of Origami Honeycomb Metamaterials with One-DOF Radial Motion DOI
Haojie Huang, Jinlong Jiang,

Yongquan Li

et al.

Thin-Walled Structures, Journal Year: 2025, Volume and Issue: unknown, P. 112978 - 112978

Published: Jan. 1, 2025

Language: Английский

Citations

2
Deep learning-based semantic segmentation for morphological fractography DOI
Keke Tang, Peng Zhang,

Yindun Zhao

et al.

Engineering Fracture Mechanics, Journal Year: 2024, Volume and Issue: 303, P. 110149 - 110149

Published: May 8, 2024

Language: Английский

Citations

10
Machine learning-based fatigue life prediction of lamellar titanium alloys: A microstructural perspective DOI
Y. Zhao, Yujie Xiang, Keke Tang

et al.

Engineering Fracture Mechanics, Journal Year: 2024, Volume and Issue: 303, P. 110106 - 110106

Published: April 24, 2024

Language: Английский

Citations

9
A novel framework to predict transversal and shear parameters of unidirectional composites by combining experimental, numerical and machine learning methods DOI Creative Commons

Siqi Cheng,

Xiaoyu Wang, Yuxuan Gao

et al.

Polymer Composites, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 25, 2025

Abstract This work presents a new method to predict the transversal and shear properties of unidirectional composites (UD) through combining experimental, numerical machine learning methods. The experimental results proved complexity difficulty explaining primary factors affecting mechanical UD. representative unit cell model was then created generate 500 virtual samples for learning. show that back propagation neural network (BP) is most suitable predicting UD, with an accuracy 98% within 2% error. minimum mean square absolute errors are 1.09E‐3 1.15E‐5, respectively. It interface has significant influences on all UD modulus composite in 12 directions (G c ) affected by input parameters optimized BP model. Due wide coverage data, proposed universal can be adopted made from different kinds fibers. Highlights Interface composites. Shear along intricated. Machine Specific beneficial improve predicted accuracy.

Language: Английский

Citations

1
A machine learning strategy for enhancing the strength and toughness in metal matrix composites DOI
Zhiyan Zhong, Jun An, Dian Wu

et al.

International Journal of Mechanical Sciences, Journal Year: 2024, Volume and Issue: 281, P. 109550 - 109550

Published: July 8, 2024

Language: Английский

Citations

7
Multimodal Data Fusion Enhanced Deep Learning Prediction of Crack Path Segmentation in CFRP Composites DOI
Peng Zhang, Keke Tang, Guangxu Chen

et al.

Composites Science and Technology, Journal Year: 2024, Volume and Issue: 257, P. 110812 - 110812

Published: Aug. 13, 2024

Language: Английский

Citations

6
Effect of hierarchical structure on the shape recovery properties and load-carrying capacity of 4D-printed kirigami-inspired honeycomb DOI
Yong Yang, Yang Han, Yukai Wang

et al.

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Kirigami-inspired honeycomb structures demonstrate outstanding self-expanding capabilities and exceptional mechanical properties. To further enhance the shape recovery properties load-carrying capacity of kirigami-inspired honeycomb, hierarchical structure is introduced by importing porous into cell walls 4D-printed (Structure II), then an innovative I) designed for achieving effect. Additionally, three with different configurations III–V) are also comparing traditional Structure II. The finite element analysis experiments conducted to compare compression deformation behavior energy absorption I–V. It found that I exhibits significantly improved compared stiffness increased 50.43%, performance 65.00%. time has been shortened 27.27% it a better rate. III–V can performances IV best capacity, increase 76.25%. Thus, developed honeycombs have broad application prospects multifunctional applications in future.

Language: Английский

Citations

0
Unsupervised transfer learning for monitoring CFRP responses using discrete strains DOI

Yan Huai,

Songhe Meng, Bo Gao

et al.

International Journal of Mechanical Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 110142 - 110142

Published: March 1, 2025

Language: Английский

Citations

0
From fabrication to prediction: unraveling the tensile strength of Al/SiN composites through machine learning DOI
Guttikonda Manohar,

Venkateswara Reddy,

Vaishali Vaishali

et al.

International Journal on Interactive Design and Manufacturing (IJIDeM), Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

Language: Английский

Citations

0
Extraction of kaolin and tribo informative analysis of the Al-kaolin composite through machine learning approaches DOI Creative Commons
V. S. S. Venkatesh, Guttikonda Manohar, Pandu R. Vundavilli

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 18, 2025

Language: Английский

Citations

0