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2025 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 13, p. 30845-30860Article, review/survey (Refereed) Published
Abstract [en]
Laser-based additive manufacturing (LAM) offers the ability to produce near-net-shape metal parts with unparalleled energy efficiency and flexibility in both geometry and material selection. Despite advantages, these processes are inherently, as they are characterized by multiphysics interactions, multiscale phenomena, and highly dynamic behaviors, making their modeling and optimization particularly challenging. Artificial intelligence (AI) has emerged as a promising tool for enhancing the monitoring and control of additive manufacturing. This paper presents a systematic review of AI applications for real-time control of laser-based manufacturing processes, analyzing 16 relevant articles sourced from Scopus, IEEE Xplore, and Web of Science databases. The primary objective of this work is to contribute to the advancement of autonomous manufacturing systems capable of self-monitoring and self-correction, ensuring optimal part quality, enhanced efficiency, and reduced human intervention. Our findings indicate that 62.5 % of the 16 analyzed studies have deployed AI-driven controllers in real-world scenarios, with over 56 % using AI for control strategies, such as Reinforcement Learning. Furthermore, 62.5 % of the studies employed AI for process modeling or monitoring, which was integral to the development or data pipelines of the controllers. By defining a groundwork for future developments, this review not only highlights current advancements but also hints future innovations that will likely include AI-based controllers.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2025
Keywords
Additive manufacturing, artificial intelligence, close-loop control, machine learning, reinforcement learning
National Category
Manufacturing, Surface and Joining Technology
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-111720 (URN)10.1109/ACCESS.2025.3537859 (DOI)001425531400034 ()2-s2.0-85217544216 (Scopus ID)
Note
Validerad;2025;Nivå 2;2025-02-24 (u2);
Full text: CC BY license;
Funder: Project Hi-rEV—Recuperação do Setor de Componentes Automóveis co-financed by the Plano de Recuperação e Resiliência (PRR), Portuguese, through NextGeneration European Union (EU) under Grant C644864375-00000002;
2025-02-242025-02-242025-06-24Bibliographically approved