Machine Learning in 3D Printing

Chapter 14 Machine Learning in 3D Printing Mohammadali Rastak, Mohammadali Rastak Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Quebec, CanadaSearch for more papers by this authorSaeedeh Vanaei, Saeedeh Vanaei Department of Mechanical, Industrial and Manufacturing Engineering, University of Toledo, Toledo, OH, 43606 USASearch for more papers by this authorShohreh Vanaei, Shohreh Vanaei Department of Bioengineering, Northeastern University, Boston, MA, USASearch for more papers by this authorMohammad Moezzibadi, Mohammad Moezzibadi Arts et Metiers Institute of Technology, CNAM, LIFSE, HESAM University, Paris, 75013 FranceSearch for more papers by this author Mohammadali Rastak, Mohammadali Rastak Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Quebec, CanadaSearch for more papers by this authorSaeedeh Vanaei, Saeedeh Vanaei Department of Mechanical, Industrial and Manufacturing Engineering, University of Toledo, Toledo, OH, 43606 USASearch for more papers by this authorShohreh Vanaei, Shohreh Vanaei Department of Bioengineering, Northeastern University, Boston, MA, USASearch for more papers by this authorMohammad Moezzibadi, Mohammad Moezzibadi Arts et Metiers Institute of Technology, CNAM, LIFSE, HESAM University, Paris, 75013 FranceSearch for more papers by this author Book Editor(s):Hamid Reza Vanaei, Hamid Reza Vanaei HESAM Université, 151 Boulevard de l'Hôpital, Paris, 75013 FranceSearch for more papers by this authorSofiane Khelladi, Sofiane Khelladi HESAM Université, 151 Boulevard de l'Hôpital, Paris, 75013 FranceSearch for more papers by this authorAbbas Tcharkhtchi, Abbas Tcharkhtchi HESAM Université, 151 Boulevard de l'Hôpital, Paris, 75013 FranceSearch for more papers by this author First published: 01 March 2024 https://doi.org/10.1002/9781394150335.ch14 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary In just a brief span of time, 3D printing process has made significant strides across various applications. A fundamental advantage of 3D printing lies in its ability to effortlessly produce intricate geometries. In this context, a central issue within 3D printing pertains to precision and achieving minimal tolerances using this technique. Given the mechanism of the 3D printing process, the resulting components can exhibit deviations from the original CAD model, leading to an increased incidence of defects, and reduced production output compared to other methods. Porosity, fractures, and surface unevenness are prevailing challenges inherent to 3D printing that are generally inevitable due to the manufacturing process. Among the available alternatives, compensating for these issues in the automotive industry seems to offer the most advantageous balance, considering aspects like cost and feasibility of implementation. Consequently, identifying discrepancies during the manufacturing process (real-time defect diagnosis and monitoring) and subsequently implementing corrective measures can be executed nearly simultaneously. In the realm of 3D printing, a modern approach involving artificial intelligence (AI), particularly machine learning (ML), has recently emerged to address the challenge of real-time monitoring. Moreover, this approach can extend to optimizing processing parameters, estimating costs, and addressing other pertinent considerations. ML holds the potential to train models for predicting outcomes based on unseen data, especially when abundant data and features are available. This chapter delves into the techniques and recent advancements concerning the integration of AI/ML in 3D printing, as well as the recent progress in monitoring the printing process. References Goh , G.D. , Sing , S.L. , and Yeong , W.Y. ( 2021 ). 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