VIRTUAL PROTOTYPES

Course objectives

The class of Virtual Prototypes aims to teach the modeling and simulation techniques of virtual prototypes for industrial use, to optimize and deepen the design, production, and operation requirements through virtual and augmented reality tools. Through theory lessons and exercises with experimental activities, the student will understand the purpose and potential of the software and hardware tools necessary for the development and use of virtual prototypes, by applying them in industrial contexts. More in detail the theory will concern the understanding of the role of virtual prototypes in the product life cycle and their mature (performance, assembly, maintenance, and safety) and advanced (conceptual and executive design, production, operation, and marketing) fields of use, the knowledge of the main hardware (virtual, mixed and augmented reality), setup steps and modeling software. Through exercises and experimental activities, the student will learn how to model and simulate prototypes in the various fields of use as well as he/she will understand in practice ways of fields and ways of use, limits, and potentiality.

Channel 1
FRANCESCA CAMPANA Lecturers' profile

Program - Frequency - Exams

Course program
- Definition of Virtual Prototyping - Definition of the context of use with presentation of case studies - What are the fundamentals for creating a Virtual Prototyping: hardware and software - Software modeling tools - Hardware tools - Application project development
Prerequisites
Basic knowledge in PC interface, and CAD modeling
Books
Lecture notes will be provided during the course based on the latest studies and applications
Frequency
Warmly suggested due to the lab activities
Exam mode
The student will discuss the work done during the course and will be asked to delve deeper into the general concepts developed during the course program
Bibliography
Rojko, A.: Industry 4.0 concept: Background and overview. International Journal of Interactive Mobile Technologies 11(5), 77–90 (2017), DOI: 10.3991/ijim.v11i5.7072 Grandi, F., Khamaisi, R.K., Morganti, A., Peruzzini, M., Pellicciari, M.: Human-Centric Design of Automated Production Lines Using Virtual Reality Tools and Human Data Analysis. In: Stecca, G., Rinaldi, R. (eds.) FAIM 2023 – 32nd International Conference on Flexible Automation and Intelligent Manufacturing, Lecture Notes in Mechanical Engineering, vol. 2024, pp. 518–526. Springer, Cham (2024), DOI: 10.1007/978-3-031-38165-2_61 Aheleroff, S., Huang, H., Xu, X., Zhong, R.Y.: Toward sustainability and resilience with Industry 4.0 and Industry 5.0. Frontiers in Manufacturing Technology 2, Article 951643 (2022), DOI:10.3389/fmtec.2022.951643 Ghosh, S., Hughes, M., Hughes, P., Hodgkinson, I.: Digital twin, digital thread, and digi-tal mindset in enabling digital transformation: A socio-technical systems perspective. Technovation 144, Article 103240 (2025), DOI: 10.1016/j.technovation.2025.103240. Jeong, D.-Y., Baek, M.-S., Lim, T.-B., Kim, Y.-W., Kim, S.-H., Lee, Y.-T., Jung, W.-S., Lee, I.-B.: Digital Twin: Technology Evolution Stages and Implementation Layers with Technology Elements. IEEE Access 10, 52609–52620 (2022), DOI: 10.1109/ACCESS.2022.3174220. Ferrari, A., Willcox, K.: Digital twins in mechanical and aerospace engineering. Nature Computational Science 4(3), 178–183 (2024). https://doi.org/10.1038/s43588-024-00613-8. . Semeraro, C., Lezoche, M., Panetto, H., Dassisti, M.: Digital twin paradigm: A systematic literature review. Computers in Industry 130, Article 103469 (2021), DOI: 10.1016/j.compind.2021.103469 Duan, L., Xu, L.D.: Data Analytics in Industry 4.0: A Survey. Information Systems Frontiers 26(6), 2287–2303 (2024), DOI: https://doi.org/10.1007/s10796-021-10190-0. Bachelor, G., Brusa, E., Ferretto, D., Mitschke, A.: Model-Based Design of Complex Aeronautical Systems Through Digital Twin and Thread Concepts. IEEE Systems Jour-nal 14(2), 1568–1579 (2020). Pasquariello, A., Bouhali, I., Leherbauer, D., Abdeljabbar, N., Mhenni, F., Patalano, S., Hehenberger, P., Rega, A.: Model-Based Systems Engineering for Digital Twin System Development Applied to an Aircraft Seat Test Bench. IEEE Access 13, 71908–71929 (2025), DOI: 10.1109/ACCESS.2025.3562932. Hämäläinen, M.: Urban development with dynamic digital twins in Helsinki city. IET Smart Cities 3(4), 201–210 (2021), DOI: 10.1049/smc2.12015. Evangelou, T., Gkeli, M., Potsiou, C.: Building Digital Twins for Smart Cities: A Case Study in Greece. In: 17th 3D GeoInfo Conference (3DGeoInfo 2022), ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. X-4/W2-2022, pp. 61–68. Copernicus Publications, Göttingen (2022), DOI: 10.5194/isprs-annals-X-4-W2-2022-61-2022. Guo, K., Wan, X., Liu, L., Gao, Z., Yang, M.: Fault diagnosis of intelligent production line based on digital twin and improved random forest. Applied Sciences (Switzerland) 11(16), Article 7733 (2021), DOI: https://doi.org/10.3390/app11167733 Schroeder, G.N., Steinmetz, C., Rodrigues, R.N., Henriques, R.V.B., Rettberg, A., Pe-reira, C.E.: A Methodology for Digital Twin Modeling and Deployment for Industry 4.0. Proceedings of the IEEE 109(4), 556–567 (2021), DOI: 10.1109/JPROC.2020.3032444 23. Bublil, T., Cohen, R., Kenett, R.S., Bortman, J.: Machine Health Indicators and Digital Twins. Sensors 25(7), Article 2246 (2025), DOI: https://doi.org/10.3390/s25072246
Lesson mode
lessons and lab activities
  • Lesson code10609412
  • Academic year2025/2026
  • CourseGreen Industrial Engineering for Sustainable Development
  • CurriculumGREEN TECHNOLOGIES
  • Year2nd year
  • Semester1st semester
  • SSDING-IND/15
  • CFU6