Enhancing manufacturing process accuracy: A multidisciplinary approach integrating computer vision, machine learning, and control systems

Abstract

Manufacturing industries face significant challenges in producing high-quality, faultless products within limited timeframes. Conventional human-based inspection methods are still prone to errors and cannot guarantee precise component placement, potentially leading to product failures, user hazards, and substantial financial and reputational losses. This research presents a workflow to automate an inspection system that integrates computer vision, machine learning, image processing, and control systems to address these challenges. The proposed system employs a microcontroller and stepper motors to control a highly calibrated camera, enabling precise and efficient product inspection. At its core, the system utilizes the YOLOv5 model for object detection, specifically identifying hole marks and holes on products pre-assembly. This deep learning model was chosen for its real-time detection capabilities and high accuracy, achieving a mean Average Precision (mAP) of 0.95, which surpasses many current industry standards. Following object detection, advanced image processing techniques are applied to determine the precise position of detected features. Our approach achieves a notable error rate of 0.2 %, offering improvements over traditional inspection methods. Our system offers the potential to reduce inspection processing time and improve fault identification accuracy in real-time applications. Our research contributes to the field of industrial automation by introducing a seamless integration of state-of-the-art computer vision techniques with practical control systems. The system's modular design allows for easy adaptation to various manufacturing environments, benefiting industries with complex assembly processes, such as electronics, automotive manufacturing, etc. While the current implementation focuses on hole detection, future work will explore expanding the system's capabilities to identify a broader range of defects and adapt to different product types. This research paves the way for more intelligent and efficient quality control processes in Industry 4.0, promising to enhance product quality, reduce waste, and improve overall manufacturing efficiency.