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Author Spotlight: Enhancing Fiber Composite Laminate Quality with the Wet Hand Lay-Up/Vacuum Bag Process

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Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review.

Ivan Malashin¹, Dmitry Martysyuk¹, Vadim Tynchenko¹

¹Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia.

|September 27, 2025

Summary

This summary is machine-generated.

Machine learning enhances fiber composite manufacturing through adaptive process control and defect detection. This survey explores data-driven methods and proposes a hybrid AI model for improved real-time quality assurance.

Keywords:

fiber-reinforced composites graph neural networks machine learning pultrusion reinforcement learning

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Last Updated: Jan 16, 2026

Author Spotlight: Enhancing Fiber Composite Laminate Quality with the Wet Hand Lay-Up/Vacuum Bag Process

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Published on: June 30, 2023

Operation of the Collaborative Composite Manufacturing CCM System

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Published on: October 1, 2019

Fabrication and Design of Wood-Based High-Performance Composites

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Published on: November 9, 2019

Area of Science:

Materials Science
Manufacturing Engineering
Artificial Intelligence

Background:

Composite manufacturing faces challenges with material heterogeneity and process variability.
Traditional methods struggle with real-time quality assurance and defect detection.
Machine learning (ML) offers data-driven solutions for adaptive process control.

Purpose of the Study:

To survey the applications of ML in fiber composite manufacturing.
To review data-driven approaches for process control and quality assurance.
To propose a novel hybrid AI model architecture for natural-fiber composites.

Main Methods:

Review of predictive modeling, sensor fusion, and adaptive control techniques.
Analysis of six case studies, including robotic draping, adhesion prediction, and forming optimization.
Development of a hybrid AI model integrating physics-informed GNN, 3D Spectral-UNet, and a cross-attention controller.

Main Results:

Demonstrated end-to-end operability of the proposed hybrid AI model on synthetic data.
Validated the effectiveness of ML in defect detection and real-time quality assurance.
Highlighted the potential of physics-informed neural networks and digital twins.

Conclusions:

ML provides a comprehensive roadmap for advancing composite manufacturing.
Future work should address challenges in small-data regimes and industrial scalability.
The proposed hybrid AI model shows promise for closed-loop parameter adjustment and improved manufacturing outcomes.