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Machine Learning Trends in Design for Additive Manufacturing

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Design Tools and Methods in Industrial Engineering III (ADM 2023)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Additive Manufacturing is becoming a widespread manufacturing system in several industrial fields such as automotive, aerospace, biomedical, etc. Design for Additive Manufacturing represents the branch of research that considers the technological constraints from the early stages of design, arriving at a geometrical model to be exported in G-code. The limitations of additive manufacturing are related to the complexity of the process, the high costs, the processing time, and the difficulties of ensuring adequate geometric and dimensional tolerances. A data-driven approach can be a solution to improve the Design for Additive Manufacturing. Artificial Intelligence and Machine Learning methods are employed in the literature to shorten the time for assessing the optimal combination of parameters and supporting decision-making. The current state of the art shows three macro-areas to apply Machine Learning methods in Design for Additive Manufacturing. These applications concern Geometrical Design Level, Process Configuration Level, and Process Monitoring Level. This paper aims to identify and classify the Machine Learning methods and algorithms most used in Design for Additive Manufacturing practices, analyzing parameters, results, processes, and materials involved.

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Acknowledgments

Project ECS 0000024 Rome Technopole, Concession Decree No. 1051 of 23 June 2022 adopted by the Italian Ministry of University and Research, CUP B83C22002820006, Rome Technopole.

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Authors and Affiliations

  1. Dipartimento di Ingegneria Industriale, Elettronica e Meccanica, Università degli Studi ROMA TRE, 00146, Rome, Italy

    Michele Trovato & Paolo Cicconi

  2. Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza Università di Roma, 00184, Rome, Italy

    Luca Belluomo, Michele Bici & Francesca Campana

Authors
  1. Michele Trovato
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  2. Luca Belluomo
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  3. Michele Bici
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  4. Francesca Campana
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  5. Paolo Cicconi
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Corresponding author

Correspondence to Michele Trovato .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, University of Florence, Florence, Italy

    Monica Carfagni

  2. Department of Industrial Engineering, University of Florence, Florence, Italy

    Rocco Furferi

  3. Department of Industrial and Information Engineering and Economics, University of L'Aquila, L’Aquila, Italy

    Paolo Di Stefano

  4. Department of Industrial Engineering, University of Florence, Florence, Italy

    Lapo Governi

  5. Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy

    Francesco Gherardini

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Trovato, M., Belluomo, L., Bici, M., Campana, F., Cicconi, P. (2024). Machine Learning Trends in Design for Additive Manufacturing. In: Carfagni, M., Furferi, R., Di Stefano, P., Governi, L., Gherardini, F. (eds) Design Tools and Methods in Industrial Engineering III. ADM 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-52075-4_14

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  • DOI: https://doi.org/10.1007/978-3-031-52075-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-52074-7

  • Online ISBN: 978-3-031-52075-4

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