ML-Based Materials Evaluation in 3D Printing - Izabela Rojek; Dariusz Mikołajewski; Krzysztof Galas; Jakub Kopowski (Applied Sciences)
Machine learning (ML) is transforming the evaluation of 3D printing materials, enabling more efficient and accurate assessment of material properties, including their sustainable life cycle. ML algorithms can analyze vast amounts of data from previous printing processes to predict the performance of different materials (including those used in multi-material printing) under different conditions. This predictive ability helps in selecting the most suitable materials for specific printing tasks, optimizing the mechanical, chemical, and overall quality of the final product. Furthermore, by integrating real-time data from sensors during the printing process, ML can continuously monitor and adjust parameters, ensuring optimal material utilization and reducing waste. ML models can identify and correct defects in printed materials by recognizing patterns associated with defects, thus improving the reliability of 3D-printed objects. This approach reduces the need for expensive and time-consuming physical tests. This accelerates the pace of 3D printing development but also increases the precision of material selection and processing, contributing to more efficient use of materials and energy for printing.