
Advanced Production (AP)
Advanced Production (AP)
Towards the Digital Twin for Metal Additive Manufacturing (DigitalTwin4AM) & Intelligent Automation for Cyberphysical Systems.
Goal
- Build-up of an advanced metal AM cell to be used as a platform for all types of activities in the context of Industry 4.0.
Key Tasks
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AM process simulation (physical and surrogate models; experimental validation/ characterization). Includes post-processing (machining).
- Development of intelligent automation frameworks. Includes implementation activities.
Outputs
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Framework of intelligent automation for cyberphysical systems, made available and implemented in simulation systems.
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Predictive tools to be used in the AM process planning and control, combining physical analytical/numerical modelling with in-situ sensor data (data-driven models).
Featured Publications
Gil, J. et al. Numerical Modeling and Prediction of Residual Stresses in AISI 316L and 18Ni300 Steels Produced by Selective Laser Melting (2021) Procedia Structural Integrity, 34, pp. 6-12.
DOI: 10.1016/j.prostr.2021.12.002.
DOI: 10.1016/j.prostr.2021.12.002.
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Coito, T. et al. Intelligent Sensors for Real-Time Decision-Making (2021) Automation, 2(2), pp. 62-82.
DOI: 10.3390/automation2020004
DOI: 10.3390/automation2020004
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Coito, T. et al. The impact of intelligent automation in internal supply chains (2021) International Journal of Integrated Supply Management, 14 (1), pp. 1-27.
DOI: 10.1504/IJISM.2021.113563
DOI: 10.1504/IJISM.2021.113563
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Gil, J. et al. Automation of property acquisition of single track depositions manufactured through direct energy deposition, Applied Sciences, 12, 2755 (2022) 1-21.
DOI: 10.3390/app12052755
DOI: 10.3390/app12052755
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Gil, J. et al. Finite Element Analysis of Distortions, Residual Stresses and Residual Strains in Laser Powder Bed Fusion-Produced Components, (2022) Structural Integrity, 25, pp. 137-147.
DOI: 10.1007/978-3-030-91847-7_14.
DOI: 10.1007/978-3-030-91847-7_14.
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Coito, T. et al. Digital twin of a flexible manufacturing system for solutions preparation. Automation 3(1), 153-175, 2022.
DOI: 10.3390/automation3010008
DOI: 10.3390/automation3010008
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Global Coordinator
Miguel Afonso Dias Ayala Botto
Other Institutional Coordinators
Abílio Manuel Pinho de Jesus
Team
Bernardo Marreiros Firme
Jorge Wolfs Gil