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.

翻译：人工智能在支持与改进智能制造及工业4.0中发挥关键作用，通过实现领域专家手动执行的各类任务的自动化。其中，评估产品与其相关原理图的合规性是一项耗时且易出错的过程。本文针对特定工业场景解决该问题，具体提出了一种神经符号方法，用于自动验证电气控制面板的合规性。该方法融合深度学习技术与回答集编程（ASP），即使在训练数据极为有限的情况下，也能识别最终产品中的潜在异常与错误。基于一家意大利电气控制面板生产企业的真实案例实验，验证了所提方法的有效性。