Machine Learning for the Development of 3D Printing Process/Materials
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".
Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 1884
Special Issue Editors
2. Arts et Métiers Institute of Technology, CNAM, LIFSE, HESAM University, 75013 Paris, France
Interests: additive manufacturing; 3D printing of polymers; 3D bioprinting; rheology of materials; mechanics of materials
Special Issues, Collections and Topics in MDPI journals
Interests: CFD; computational aeroacoustics; complex fluid flows
Special Issues, Collections and Topics in MDPI journals
Interests: polymers and composites; polymer processing; mechanical properties; solid mechanics; fracture mechanics; material characterization; additive manufacturing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the convergence of machine learning (ML) and 3D printing has revolutionized the manufacturing landscape. Three-dimensional printing is a transformative technology that enables the creation of intricate and complex objects layer by layer. However, the process is not without challenges, including the material properties, structural integrity, and production speed. ML, with its ability to analyze vast datasets and generate insights, has emerged as a powerful tool for enhancing the efficiency, accuracy, and overall optimization of 3D printing processes. ML algorithms are capable of analyzing and predicting how different materials will behave during the 3D printing process and in the final product. This predictive capability enables manufacturers to optimize material selection, ensuring the desired properties of the printed objects. ML models can simulate the interactions between materials and printing conditions, leading to the development of innovative materials with enhanced strength, flexibility, and heat resistance. This, in turn, expands the range of applications for 3D-printed products, from aerospace components to medical implants. Three-dimensional printing involves a myriad of parameters such as layer height, print speed, temperature, and cooling rates, which directly influence the quality of the printed object. ML algorithms can process data from sensors embedded within the printing process, identifying patterns and correlations that are beyond human perception. By analyzing these data, ML models can optimize the printing process in real-time, reducing defects, minimizing material wastage, and enhancing the overall efficiency of production. This Special Issue is aimed at providing selected contributions on advances in the application of ML in 3D printing.
Dr. Hamid Vanaei
Prof. Dr. Sofiane Khelladi
Prof. Dr. Abbas Tcharkhtchi
Guest Editors
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Keywords
- machine learning
- artificial intelligence
- 3D printing
- additive manufacturing
- optimization
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