Project Objectives
The aim of this initiative is to bring together excellences in manufacturing research in order to define and deliver the new competences required by future engineers working in the context of the fourth industrial revolution. In particular:
- New contents and models will be developed with particular emphasis given to the aspect related with a sustainable transition to the digital era.
- New educational units will be formulated in term of independent learning blocks which can be easily integrated in existing mainstream programs in the domain of industrial, mechanical, production and electrical engineering.
Project Outputs
Mabkhot, M.M.; Ferreira, P.; Maffei, A.; Podržaj, P.; Mądziel, M.; Antonelli, D.; Lanzetta, M.; Barata, J.; Boffa, E.; Finžgar, M.; Paśko Ł.; Minetola P.; Chelli R.; Nikghadam-Hojjati S.; Wang V.; Priarone P.C.; Litwin P.; Stadnicka D.; Lohse N. (2021). Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals. Sustainability 2021, 13, 2560. https://doi.org/10.3390/su13052560
- Output 2 - Definition of new competences in the domain of Industry 4.0 for different engineering profiles
Francesco Lupi, Mohammed M. Mabkhot, Miha Finžgar, Paolo Minetola, Dorota Stadnicka, Antonio Maffei, Paweł Litwin, Eleonora Boffa, Pedro Ferreira, Primož Podržaj, Riccardo Chelli, Niels Lohse, Michele Lanzetta,
Toward a Sustainable Industry 4.0 Engineer. Pre-print
Francesco Lupi, Mohammed M. Mabkhot, Miha Finžgar, Paolo Minetola, Dorota Stadnicka, Antonio Maffei, Paweł Litwin, Eleonora Boffa, Pedro Ferreira, Primož Podržaj, Riccardo Chelli, Niels Lohse, Michele Lanzetta (2022), Toward a sustainable educational engineer archetype through Industry 4.0, Computers in Industry, Volume 134,
2022, 103543, https://doi.org/10.1016/j.compind.2021.103543.
- Output 3 - Development of constructively aligned courses in the domain of Industry 4.0
- AR and VR for Assembly
- Decision Support System
- Process comparison: additive manufacturing vs. machining
- Learning materials PART 1 PART 2
- Time series in machine learning: time series clustering PART 1 PART 2
- Set of data - customers_clustering
- Set of data - non_outlier_accbalance
- Lean Manufacturing
- AM in medical implants
Maffei, A.; Boffa, E.; Lupi, F.; Lanzetta, M. On the Design of Constructively Aligned Educational Unit. Educ. Sci. 2022, 12, 438. https://doi.org/10.3390/educsci12070438
- Output 5 - Exploitation though Synergies in Education