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.
Key Takeaways
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.”