Revolutionizing defect recognition in hard metal industry through AI explainability, human-in-the-loop approaches and cognitive mechanisms

Abstract

Defect detection is one of the main areas that Industry 4.0 concepts like automation, IoT, digitization and AI aimed to provide solutions. In this work, a platform that extends the aforementioned concepts with ones coming from Industry 5.0 like reconciliation and collaboration between humans and machines is introduced. The proposed platform provides defect detection and localization services for hard metal industry by extending current AI solutions with exponentially growing technologies such as interpretable and explainable AI (XAI), human-in-the-loop (HITL) approaches and cognitive retraining mechanisms. In particular, the platform, that is built on a micro-service architecture to enable its software sustainability, is powered by machine and deep learning models for defect detection and localization. These models are able to recognize the defects, locate them with high precision and present them to end users so to maximize resiliency in quality inspection processes. Alongside the state-of-the-art AI algorithms that are utilized by the platform, the human in the loop mechanisms and interfaces that enable human experts to inject their knowledge for AI models training are considered as key innovative aspects of this work. To enhance further the human and AI collaboration, XAI mechanisms are included in the platform,to enable the experts to gain useful insights regarding the operation of the AI models in order to be able to provide further feedback that can trigger on-the-fly platform’s cognition mechanisms to improve models’ accuracy and performance.