Contact Davide D'Amico
Areas of expertise
- Computing, Simulation & Modelling
- Through-life Engineering Services
Background
Davide graduated from the University of Padova (Italy) with a BSc degree in Electrical Energy Engineering. He moved on to complete a joint MSc in Energy Engineering at the Free University of Bolzano and the University of Trento (Italy). While studying at Bolzano and Trento, Davide spent one semester at the Vienna University of Technology (Austria), and one semester at the Chiang Mai University (Thailand) to complete his MSc thesis. After the MSc Davide started working at Fraunhofer Institute in the team of Automation and Mechatronics Engineering.
Current activities
Davide is currently pursuing a PhD in developing top-level ontology-based digital twins to monitor through-life degradation of complex engineering assets.
The digital twin to be developed in this PhD aims to deliver the availability and reliability of major asset against a customer requirement (Demand). This will contribute to matching the supply of the different contributing service resources that will assist in making an asset available for the customer to undertake their operations. Within the PhD will be developed degradation optimisation methods embedded in the digital twin to enable the planning process.
At Cranfield, Davide will be based at the Centre for Digital Engineering and Manufacturing (CDEM), which hosts cutting-edge simulation and visualisation facilities.
Clients
- Babcock International
Publications
Articles In Journals
- D’Amico RD, Addepalli S & Erkoyuncu JA. (2023). Industrial Insights on Digital Twins in Manufacturing: Application Landscape, Current Practices, and Future Needs. Big Data and Cognitive Computing, 7(3)
- D’Amico RD, Erkoyuncu JA, Addepalli S & Penver S. (2022). Cognitive digital twin: An approach to improve the maintenance management. CIRP Journal of Manufacturing Science and Technology, 38(August)
Conference Papers
- D’Amico RD, Addepalli P & Erkoyuncu JA. (2022). Is a Top Level Ontology Based Digital Twin the Solution to Human-Machine Interoperability?
- D'Amico RD, Sarkar A, Karray H, Addepalli S & Erkoyuncu JA. (2022). Detecting failure of a material handling system through a cognitive twin
- Brozzi R, D’Amico RD, Pasetti Monizza G, Marcher C, Riedl M, .... (2018). Design of Self-assessment Tools to Measure Industry 4.0 Readiness. A Methodological Approach for Craftsmanship SMEs