Titolo della tesi

A management framework for smart manufacturing applications in Industry 4.0

Tutori

• Massimo Poncino

Corso di laurea magistrale

• Machine learning in applications. A.A. 2021/22, INGEGNERIA INFORMATICA (COMPUTER ENGINEERING). Collaboratore del corso

Corso di laurea di 1° livello

• Tecniche di programmazione. A.A. 2020/21, INGEGNERIA INFORMATICA. Collaboratore del corso
• Tecniche di programmazione. A.A. 2021/22, INGEGNERIA INFORMATICA. Collaboratore del corso
• Informatica. A.A. 2019/20, INGEGNERIA AEROSPAZIALE. Collaboratore del corso

• Cannizzaro, Davide; Simone, Filomena; Illgner-Fehns, Klaus; Mata, Sara; Mondino, Ivan; ... (2022)
  Quality inspection of critical aircraft engine components: towards full automation. In: 2022 27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Stuttgart (DE), 6th-9th September 2022
  Contributo in Atti di Convegno (Proceeding)
• Cannizzaro, Davide; Varrella, Antonio Giuseppe; Paradiso, Stefano; Sampieri, Roberta; ... (2022)
  In-situ defect detection of metal Additive Manufacturing: an integrated framework. In: IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, vol. 10, pp. 74-86. ISSN 2168-6750
  Contributo su Rivista
• Cannizzaro, Davide; Varrella, Antonio Giuseppe; Paradiso, Stefano; Sampieri, Roberta; ... (2021)
  In-Situ Monitoring of Additive Manufacturing. In: Predictive Maintenance in Smart Factories / S.N., S.L., Springer, Singapore, pp. 207-228. ISBN: 978-981-16-2939-6
  Contributo in Volume
• Cannizzaro, Davide; Giuseppe Varrella, Antonio; Paradiso, Stefano; Sampieri, Roberta; ... (2021)
  Image analytics and machine learning for in-situ defects detection in Additive Manufacturing. In: 2021 Design, Automation and Test in Europe Conference and Exhibition (DATE 2021), Virtual Conference (due to Covid-19), 2021, pp. 603-608. ISSN 1530-1591. ISBN: 978-3-9819263-5-4
  Contributo in Atti di Convegno (Proceeding)
• Cannizzaro, Davide; Aliberti, Alessandro; Bottaccioli, Lorenzo; Macii, Enrico; ... (2021)
  Solar radiation forecasting based on convolutional neural network and ensemble learning. In: EXPERT SYSTEMS WITH APPLICATIONS, vol. 181. ISSN 0957-4174
  Contributo su Rivista