Emerging processes, machine learning and applications in composite additive manufacturing

Md Zillur Rahman; H K Mahedi Azad; Morad Hossain Diganto

doi:10.1039/d6ma00219f

January 1, 2026Materials AdvancesOpen Access

Emerging processes, machine learning and applications in composite additive manufacturing

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Key Points

This research aims to explore how machine learning (ML) processes can improve performance in composite additive manufacturing (CAM).
Analyzed processes affecting CAM performance like reinforcement dispersion and bonding
Utilized ML-enabled technologies for sensing and benchmarking
Developed digital twins and uncertainty-aware threads for qualification readiness.
Identified key factors like porosity and anisotropy affecting CAM quality
Demonstrated the effectiveness of ML in enhancing performance outcomes
Created frameworks that enable qualification-ready CAM solutions.

Implication

Emerging processes utilize machine learning to enhance composite additive manufacturing performance, indicating significant advancements.

Abstract

Composite AM performance is governed by reinforcement dispersion and orientation, interfacial bonding, porosity, and anisotropy; ML-enabled sensing, benchmarking, digital twins, and uncertainty-aware digital threads enable qualification-ready CAM.

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Cite This Study

Rahman et al. (2026) studied this question.

synapsesocial.com/papers/6a3632fcdb0793dc1a539788 https://doi.org/10.1039/d6ma00219f

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Emerging processes, machine learning and applications in composite additive manufacturing

Key Points

Implication

Abstract

Discussion

Cite This Study

Emerging processes, machine learning and applications in composite additive manufacturing

Key Points

Implication

Abstract

Discussion

Cite This Study

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