RECENT ADVANCES IN ANOMALY AND DEFECT DETECTION USING MACHINE LEARNING IN SMART MANUFACTURING - Laxkar, Dr. Pradeep (Journal of Global Research in Mathematical Archives(JGRMA), 2025)
Smart manufacturing is a paradigm shift in conducting industrial processes, which is caused by the interdependence of improved digital technologies and smart automation. This evolution is of utmost importance for anomaly and defect detection, enabling a product with quality, efficiency, and predictive maintenance. This article reviews the key production techniques that enable the creation of smart factory ecosystems: Cyber-Physical Systems, Big Data analytics, cloud computing, autonomous robotics, and IIoT frameworks. It analyses the different levels of smart factory platforms, including mechanical design, process control, IoT configuration, and communication infrastructure. Moreover, it evaluates machine learning methods for anomaly and defect detection, including supervised, unsupervised, and semi-supervised approaches, and highlights their advantages and disadvantages. It also reviews recent trends such as Digital Twin integration, Edge AI, privacy-preserving learning, transfer learning, and explainable AI as indicators of new developments in industrial intelligence. Concomitantly, the paper identifies the main limitations, including security risks, difficulties in system integration, and safety concerns in human–robot collaboration. The present study provides unified views that can be utilised to enhance the reliability and efficiency of the smart manufacturing sector.