Automatic definition of engineer archetypes: A text mining approach

Lupi, Francesco; Mabkhot, Mohammed M.; Boffa, Eleonora; Ferreira, Pedro; Antonelli, Dario; Maffei, Antonio; Lohse, Niels; Lanzetta, Michele

doi:10.1016/j.compind.2023.103996

Cited by 2 publications

(1 citation statement)

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“…The system also aims to increase the efficiency of quality control by reducing programming time and increasing effectiveness offering the ability to carry out CAVI offline, which reduces the impact on shop floor productivity. Finally, the proposed framework also allows for hardware reconfiguration and software parametrization for new product families with similar vision features reducing the need for product-specific staff skills [81].…”

Section: Framework Resultsmentioning

confidence: 99%

A framework for flexible and reconfigurable vision inspection systems

Lupi,

Biancalana,

Rossi

et al. 2023

Int J Adv Manuf Technol

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Reconfiguration activities remain a significant challenge for automated Vision Inspection Systems (VIS), which are characterized by hardware rigidity and time-consuming software programming tasks. This work contributes to overcoming the current gap in VIS reconfigurability by proposing a novel framework based on the design of Flexible Vision Inspection Systems (FVIS), enabling a Reconfiguration Support System (RSS). FVIS is achieved using reprogrammable hardware components that allow for easy setup based on software commands. The RSS facilitates offline software programming by extracting parameters from real images, Computer-Aided Design (CAD) data, and rendered images using Automatic Feature Recognition (AFR). The RSS offers a user-friendly interface that guides non-expert users through the reconfiguration process for new part types, eliminating the need for low-level coding. The proposed framework has been practically validated during a 4-year collaboration with a global leading automotive half shaft manufacturer. A fully automated FVIS and the related RSS have been designed following the proposed framework and are currently implemented in 7 plants of GKN global automotive supplier, checking 60 defect types on thousands of parts per day, covering more than 200 individual part types and 12 part families.

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Section: Framework Resultsmentioning

confidence: 99%