Quality control assessment in industrial field

Description

Numerous research activities implement non-destructive quality control and diagnostic systems in the industrial field as part of collaborations with partner companies and regional, national, or European projects. Multiple optical techniques are applied in industry, such as matrix and linear cameras, high-speed cameras, hyperspectral cameras, telecentric vision systems, and lidar for dimensional measurements, crack identification, surface defects, or material characterization. Vision-based systems such as laser line triangulation profilometers are integrated into production lines for non-contact dimensional control of industrial components with measurement ranges varying from micrometers to meters depending on the application (electronics, automotive, metallurgy, steel industry, etc.). The use of software algorithms such as pattern matching, edge detection, blob analysis, and point-cloud analysis, combined with the hardware development of custom systems dedicated to hostile industrial environments, allows for early defect identification and preventive diagnosis. As part of our research activities, we develop applications up to TRL 7, where complete automated quality control stations are integrated in production line and connected to digital platforms. Other areas of application include manufacturing, aerospace, civil/construction, rail, and electronics.

Publications
  • Minnetti, E.; Chiariotti, P.; Paone, N.; Garcia, G.; Vicente, H.; Violini, L.; Castellini, P. A Smartphone Integrated Hand-Held Gap and Flush Measurement System for in Line Quality Control of Car Body Assembly. Sensors 2020, 20, 3300. https://doi.org/10.3390/s20113300
  • Pasquinelli V., Martarelli M., Paone N., Van De Kamp W., Verhoef B., Laser Line Triangulation Sensor with Wide Measurement Range: a Steel Industry Use Case, 2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT, pp. 470-475, DOI: 10.1109/MetroInd4.0IoT61288.2024.10584183
  • Giovanni S., Calcagni M. T., Martarelli M., Revel G. M. (2024). Metrological evaluation of an AI-based vision computing model for crack detection on masonry structures. MATEC Web of Conferences. 403. 10.1051/matecconf/202440304002.
  • Pasquinelli V., Martarelli M., Montalto L., Nisi M., Van De Kamp W., Verhoef B., Paone N., Laser line triangulation measurement on incandescent steel objects: methodologies to improve optical signal to noise ratio. Acta Imeko, Vol. 14 No. 2 (2025),  DOI: https://doi.org/10.21014/actaimeko.v14i2.2069
  • Baleani, Alessia & Paone, Nicola & Gladines, Jona & Vanlanduit, S.. (2022). Surface roughness measurements of turned parts through a vision-based measurement system: uncertainty analysis and performance comparison with state-of-the-art instruments. 17-22. 10.1109/MetroInd4.0IoT54413.2022.9831674.
Staff

Prof. Nicola Paone
Tel. +39 071 220 4490
email: n.paone@staff.univpm.it

Prof. Paolo Castellini
Tel. +39 071 220 4441
email: p.castellini@staff.univpm.it

Prof.ssa Milena Martarelli
Tel. +39 071 220 4542
email: m.martarelli@staff.univpm.it