Development of the visual monitoring system based on an industrial automation system (Glass Industry) by using VB.NET

Gunaseelan, Pavithran (2023) Development of the visual monitoring system based on an industrial automation system (Glass Industry) by using VB.NET. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

The project " Development Of The Visual Monitoring System Based On An Industrial Automation System (Glass Industry) By Using VB.NET" aims to gather detailed information, minimize human intervention to enhance quality control, safety and operational efficiency in the glass manufacturing sector. The glass manufacturing industry requires constant vigilance for maintaining quality and operational efficiency. Manual monitoring becomes challenging and error-prone, particularly in high-speed production environments. This project aims to address these challenges by implementing automated defect monitoring on the glass bottle manufacturing floor. The core of the system integrates cutting-edge computer vision capabilities with real-time control mechanisms in a manufacturing environment. The proposed system features a user-friendly Human-Machine Interface (HMI) through Visual Basic (VB.NET) and employs seamless interaction with image recognition techniques utilizing Convolutional Neural Networks (CNNs) deep learning algorithm for glass bottle monitoring. The methodology includes the design and implementation of the visual monitoring and inspection system, with an emphasis on the system architecture, hardware components, and software implementation. The results showcase the systems commendable performance in distinguishing between proper and defective glass bottles with high accuracy on the conveyor belt. The pre-trained predictive model, powered by Convolutional Neural Networks (CNNs), proves its efficacy in efficiently categorizing bottles, contributing to a streamlined defect detection process. Moreover, the real-time colour recognition feature enhances analytical capabilities by extracting and processing colour details from frames. The VB.NET interface serves as a comprehensive tool for generating detailed reports, offering insights into the monitoring process with statistics on proper and defective bottles. This empowers users to make informed decisions and optimize processes. Overall, by leveraging a camera sensor and seamlessly integrating advanced CNN-based monitoring with a versatile VB.NET interface, this system transforms manufacturing quality control, ensuring real-time, automated monitoring, and efficient segregation of defective products. Ultimately, this enhances overall production efficiency and product quality in a streamlined process

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