A review of multimodal surrogate machine learning models for real-time control and defect mitigation in automated composite manufacturing

Abstract This paper addresses the application of machine learning (ML) to automated composite manufacturing with a particular focus on thermal regulation, in-situ defect detection, and process control in AFP/ATL systems. First, a structured review of recent studies is presented, covering reinforceme...

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Bibliografische gegevens

Hoofdauteurs:	Ivan P. Malashin, Dmitry Martysyuk, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Vadim Tynchenko
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	Springer 2026-02-01
Reeks:	Discover Applied Sciences
Onderwerpen:	Physics-informed machine learning Thermal process control Defect detection Digital twin Automated composite manufacturing
Online toegang:	https://doi.org/10.1007/s42452-026-08323-8
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