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Research Paper#Additive Manufacturing, Machine Learning, Composite Materials🔬 ResearchAnalyzed: Jan 3, 2026 20:06

SE-WDNN for CFRC-AM Property Prediction

Published:Dec 26, 2025 22:27
1 min read
ArXiv

Analysis

This paper addresses the challenge of predicting multiple properties of additively manufactured fiber-reinforced composites (CFRC-AM) using a data-efficient approach. The authors combine Latin Hypercube Sampling (LHS) for experimental design with a Squeeze-and-Excitation Wide and Deep Neural Network (SE-WDNN). This is significant because CFRC-AM performance is highly sensitive to manufacturing parameters, making exhaustive experimentation costly. The SE-WDNN model outperforms other machine learning models, demonstrating improved accuracy and interpretability. The use of SHAP analysis to identify the influence of reinforcement strategy is also a key contribution.
Reference

“The SE-WDNN model achieved the lowest overall test error (MAPE = 12.33%) and showed statistically significant improvements over the baseline wide and deep neural network.”

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Research#Composites🔬 ResearchAnalyzed: Jan 10, 2026 07:24

Novel Kinematic Framework for Composite Damage Characterization

Published:Dec 25, 2025 07:11
1 min read
ArXiv

Analysis

This research presents a new kinematic framework, which has the potential to advance the understanding of composite material behavior under stress. The application of this framework to damage characterization is a significant contribution to the field.
Reference

“A novel large-strain kinematic framework for fiber-reinforced laminated composites and its application in the characterization of damage.”

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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:32

Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-state Battery Cathodes

Published:Dec 18, 2025 02:00
1 min read
ArXiv

Analysis

This article describes a research paper applying machine learning, specifically graph analysis, to study particulate composites, with a focus on solid-state battery cathodes. The use of machine learning suggests an attempt to model and understand complex material structures and their properties. The application to battery technology indicates a focus on improving energy storage.
Reference

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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:06

Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network

Published:Dec 15, 2025 03:04
1 min read
ArXiv

Analysis

This article describes a research paper focusing on using a Deep Operator Network to predict deformation in carbon/epoxy composites. The probabilistic nature of the predictions suggests an attempt to account for uncertainties in the manufacturing process. The use of a Deep Operator Network is a key aspect, indicating the application of advanced machine learning techniques to solve a complex engineering problem.
Reference

“The article likely details the methodology, results, and implications of using a Deep Operator Network for this specific application.”

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