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September 23, 2025PolymersOpen Access

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

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This review highlights the use of machine learning for defect detection and adaptive control in fiber composites, suggesting future pathways.

Machine learning improves defect detection and adaptive control in fiber composite manufacturing, enhancing overall quality.
The review examines predictive modeling and sensor fusion in six case studies, demonstrating practical applications in real-time processing.
Data-driven methods, including hybrid AI models, optimize processes by integrating physics-informed networks and segmentation techniques.
Challenges in small-data scenarios and industrial scalability highlight the need for further advancements in machine learning for composites.

Malashin et al. (2025) studied this question.

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This review highlights the use of machine learning for defect detection and adaptive control in fiber composites, suggesting future pathways.