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
-
AM process simulation (physical and surrogate models; experimental validation/ characterization). Includes post-processing (machining).
- Development of intelligent automation frameworks. Includes implementation activities.
Outputs
-
Framework of intelligent automation for cyberphysical systems, made available and implemented in simulation systems.
-
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
Castanheira, L. et al. Parametrization and characterization of DED printings using recycled AISI 303 powder particles (2024) Powder Technology, 435.
DOI: 10.1016/j.powtec.2024.119453
DOI: 10.1016/j.powtec.2024.119453
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Romio, P. et al. Spur gear teeth reconstruction via direct laser deposition (2024) Forsch Ingenieurwes, 88.
DOI: 10.1007/s10010-023-00721-3
DOI: 10.1007/s10010-023-00721-3
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Firme, B. et al. Augmented reality in intelligent automation: an application to support industrial scheduling (2024) Production & Manufacturing Research, 12 (1).
DOI: 10.1080/21693277.2024.2383654
DOI: 10.1080/21693277.2024.2383654
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Melzer, D., et al. Ambient and high temperature tensile behaviour of DLD-manufactured inconel 625/42C steel joint (2023) Materials Science and Engineering: A, 885: 145603.
DOI: 10.1016/j.msea.2023.145603
DOI: 10.1016/j.msea.2023.145603
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Martins, M. S., et al. Minimizing total completion time in large-sized pharmaceutical quality control scheduling (2023) Journal of Heuristics, 29.1: 177-206.
DOI: 10.1007/s10732-023-09509-8
DOI: 10.1007/s10732-023-09509-8
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Firme, B., et al. Agent-based hybrid tabu-search heuristic for dynamic scheduling (2023) Engineering Applications of Artificial Intelligence, 126: 107146.
DOI: 10.1016/j.engappai.2023.107146
DOI: 10.1016/j.engappai.2023.107146
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Gil, J. et al. Automation of property acquisition of single track depositions manufactured through direct energy deposition (2022) Applied Sciences, 12, 2755, 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, 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 (2022) Automation 3(1), 153-175.
DOI: 10.3390/automation3010008
DOI: 10.3390/automation3010008
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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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