Abstract
Additive manufacturing (AM) offers the advantage of producing complex parts more efficiently and in a lesser production cycle time as compared to conventional subtractive manufacturing processes. It also provides higher flexibility for diverse applications by facilitating the use of a variety of materials and different processing technologies. With the exceptional growth of computing capability, researchers are extensively using machine learning (ML) techniques to control the performance of every phase of AM processes, such as design, process parameters modeling, process monitoring and control, quality inspection, and validation. Also, ML methods have made it possible to develop cybermanufacturing for AM systems and thus revolutionized Industry 4.0. This paper presents the state-of-the-art applications of ML in solving numerous problems related to AM processes. We give an overview of the research trends in this domain through a systematic literature review of relevant journal articles and conference papers. We summarize recent development and existing challenges to point out the direction of future research scope. This paper can provide AM researchers and practitioners with the latest information consequential for further development.
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Xames, M.D., Torsha, F.K. & Sarwar, F. A systematic literature review on recent trends of machine learning applications in additive manufacturing. J Intell Manuf 34, 2529–2555 (2023). https://doi.org/10.1007/s10845-022-01957-6
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DOI: https://doi.org/10.1007/s10845-022-01957-6