Abstract
Metal Additive manufacturing (AM) is a complex operation, which requires the fine-tuning of hundreds of processes parameters to obtain repeatability and a good quality design at dimensional, geometric, structural levels. Therefore, to be used as final product, metal AM parts must go through advanced quality control processes. This implies large capital equipment investment in measurement systems (i.e., tomography and lengthy inspection operations that adversely impact costs and lead times). A large amount of data can be collected in metal AM processes, as most industrial AM systems are equipped with sensors providing log signals, images and videos. This paper develops and proposes an innovative quality-oriented decision support framework, composed by a Model-based Design tool providing Design for Additive Manufacturing features, and a Cyber-Physical System created by integrating an AM asset with a real-time smart monitoring software application. Such framework caters to process engineers and quality managers needs to improve a set of quality and economic KPIs.
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Acknowledgements
This study was carried out within the MICS (Made in Italy – Circular and Sustainable) Extended Partnership and received funding from the European Union Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1551.11-10-2022, PE00000004).
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Sassanelli, C., Borzi, G.P., Quadrini, W., De Marco, G., Mossa, G., Terzi, S. (2024). A Quality-Oriented Decision Support Framework: Cyber-Physical Systems and Model-Based Design to Develop Design for Additive Manufacturing Features. In: Danjou, C., Harik, R., Nyffenegger, F., Rivest, L., Bouras, A. (eds) Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation. PLM 2023. IFIP Advances in Information and Communication Technology, vol 701. Springer, Cham. https://doi.org/10.1007/978-3-031-62578-7_4
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DOI: https://doi.org/10.1007/978-3-031-62578-7_4
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