Automated manual assembly station using computer vision

Ch'ng, Shin Joe (2024) Automated manual assembly station using computer vision. Final Year Project, UTAR.

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Abstract

This project aims to design an intelligent, data-driven workstation that efficiently measures operators' performance, assists operators in enhancing productivity and improving quality control, and addresses long-standing challenges within traditional manual assembly stations. This innovative technology is intended to replace outdated proprietary systems and paper-based processes, which provide little room for innovation and flexibility. This system includes sensors, open-source software, and computer vision to transform the assembly process. This project implements an integrated quality inspection model based on real-time picture data for immediate fault detection to streamline processes and remove roadblocks. This workstation's implementation of a unique QR code-based triggering event mechanism is a novel feature. This inventive method allows the system to decode QR code values to identify and initiate particular assembly tasks, bringing a new level of accuracy and efficiency to the assembly process. This innovative workstation's release has the potential to change the manufacturing industry completely. It's not only more affordable for startups, but it also positively impacts overall excellence by increasing quality standards, efficiency, and adaptability in a constantly changing industry. This project introduces a dynamic assembly process, advocates open-source architectures, and seamlessly integrates quality assurance throughout the assembly workflow to accomplish this goal.

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