Sarcouncil Journal of Engineering and Computer Sciences

Abstract: This article presents AI-enhanced machine vision systems and their transformative impact on electric vehicle battery manufacturing and quality inspection processes. The article explores integrating advanced computer vision technologies with intelligent automation systems to address the unprecedented precision requirements of modern EV production, particularly in battery manufacturing, where microscopic defects can lead to catastrophic failures. The article shows real-time quality assurance protocols through advanced vision systems, including high-resolution imaging, thermal monitoring, and multi-modal inspection techniques that enable manufacturers to achieve superior defect detection rates while maintaining high-speed production requirements. The article analyzes battery manufacturing safety validation through vision-based systems that monitor electrode alignment, surface defect detection, electrolyte filling validation, and wire-bonding inspection across the entire production lifecycle. Furthermore, the article shows integrated automation and process intelligence systems that combine 3D vision technologies with artificial intelligence to create sophisticated manufacturing ecosystems capable of predictive quality assurance and comprehensive component traceability. The findings demonstrate that AI-enhanced machine vision systems represent a fundamental paradigm shift from reactive quality control to predictive manufacturing intelligence, enabling the automotive industry to meet zero-defect manufacturing objectives while supporting the rapid scaling of electric vehicle production to meet global market demands.