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Abstract

Additive Manufacturing (AM) is on the forefront of innovative advance manufacturing techniques leveraging Artificial Intelligence (AI) and Machine Learning (ML) to improve processing capabilities. We conducted a literature review to survey the current state of the art for AI/ML applications within Material Extrusion AM (MEX-AM). Furthermore, this study explored the intersection of AI applications and use of Carbon Fiber-Reinforced Polymers (CFRP) as a MEX-AM material. We found that while discontinuous CFRPs are covered in several experimental studies, there was a noticeable lack of research on continuous CFRPs among the collected papers. We found that the most common ML Solution for quality issues in MEX-AM was the artificial neural network feed forward supervised learning back propagation (ANN-FFNN-SL-BPN) Solution.

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Disclosure of Interests

The authors have no competing interests to declare that are relevant to the content of this article.

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Acknowledgments

This ongoing effort is supported by Naval Air Systems Command (NAVAIR) a subsidiary of the U.S. Navy.

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Correspondence to Thorsten Wuest .

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Harper, A., Wuest, T. (2024). An Explorative Study of AI Applications in Composite Material Extrusion Additive Manufacturing. In: Thürer, M., Riedel, R., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments. APMS 2024. IFIP Advances in Information and Communication Technology, vol 731. Springer, Cham. https://doi.org/10.1007/978-3-031-71633-1_17

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  • DOI: https://doi.org/10.1007/978-3-031-71633-1_17

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