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Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels

Published online by Cambridge University Press:  10 July 2023

VITO BARBARA ,
MASSIMO GUARASCIO ,
NICOLA LEONE ,
GIUSEPPE MANCO ,
ALESSANDRO QUARTA ,
FRANCESCO RICCA Open the ORCID record for FRANCESCO RICCA [Opens in a new window]  and
ETTORE RITACCO
VITO BARBARA
Affiliation:
University of Calabria, Rende, Italy (e-mail: barbara.vito@unical.it)
MASSIMO GUARASCIO
Affiliation:
ICAR-CNR, Arcavacata, Italy (e-mail: massimo.guarascio@icar.cnr.it)
NICOLA LEONE
Affiliation:
University of Calabria, Rende, Italy (e-mail: nicola.leone@unical.it)
GIUSEPPE MANCO
Affiliation:
ICAR-CNR, Arcavacata, Italy (e-mail: giuseppe.manco@icar.cnr.it)
ALESSANDRO QUARTA
Affiliation:
Sapienza University of Rome, Rome, Italy (e-mail: alessandro.quarta@uniroma1.it)
FRANCESCO RICCA
Affiliation:
University of Calabria, Rende, Italy (e-mail: francesco.ricca@unical.it)
ETTORE RITACCO
Affiliation:
University of Udine, Udine, Italy (e-mail: ettore.ritacco@uniud.it)
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Abstract

Artificial Intelligence plays a main role in supporting and improving smart manufacturing and Industry 4.0, by enabling the automation of different types of tasks manually performed by domain experts. In particular, assessing the compliance of a product with the relative schematic is a time-consuming and prone-to-error process. In this paper, we address this problem in a specific industrial scenario. In particular, we define a Neuro-Symbolic approach for automating the compliance verification of the electrical control panels. Our approach is based on the combination of Deep Learning techniques with Answer Set Programming (ASP), and allows for identifying possible anomalies and errors in the final product even when a very limited amount of training data is available. The experiments conducted on a real test case provided by an Italian Company operating in electrical control panel production demonstrate the effectiveness of the proposed approach.

Keywords

automated quality control systemsanswer set programmingcomputer visiondata scarcity
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 4: 2023 International Conference on Logic Programming , July 2023 , pp. 748 - 764
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Copyright
© The Author(s), 2023. Published by Cambridge University Press

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Footnotes

*

The authors express sincere gratitude to the anonymous referees for their valuable suggestions, which contributed to the improvement of our work. We would also like to acknowledge the exceptional support of Dimitri Buelli, Stefano Ierace, Salvatore Iiritano, Giovanni Laboccetta, and Valerio Pesenti elapsed during the development of the system presented in this paper. Their expertise and dedication have been instrumental in shaping the success of our research. Research partially supported by MISE (today MIMIT) under projects “MAP4ID – Multipurpose Analytics Platform 4 Industrial Data” Proj. N. F/190138/01-03/X44 and MUR under PNRR project PE0000013-FAIR, Spoke 9 – Green-aware AI – WP9.1. This work has been carried out while Alessandro Quarta was enrolled in the Italian National Doctorate on Artificial Intelligence run by Sapienza University of Rome with University of Calabria. A preliminary version appeared in CEUR Workshop Proceedings vol. 3204 pp. 247–253.

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