Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels
This addresses a time-consuming and error-prone task in smart manufacturing for electrical control panel production, though it appears incremental as it applies existing methods to a specific industrial scenario.
The paper tackles automating compliance verification of electrical control panels by proposing a Neuro-Symbolic approach combining Deep Learning and Answer Set Programming, which identifies anomalies with limited training data, as demonstrated in experiments with an Italian company.
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.