Intelligent visual inspection of bottling production line through Neural Network

Abstract

This paper presents research done on developing an intelligent visual inspection system for automatic inspection of bottling production line. The objective of this research is to enhance on modeling, integrating, and implementation of intelligent visual inspection system in the process of quality control in industrial manufacturing. The system will inspect each individual product in real-time process. Levenberg-Marquardt backpropagation neural network has been applied for this system to differentiate between acceptable and unacceptable product, for example, the misplacement of a bottle cap. A simulation of the operation was attempted in the Robotics System Laboratory of Industrial Computing Department, Universiti Kebangsaan Malaysia. The experiments were done by using developed software (Real-TIVI) and hardware, i.e. conveyor belt, adjustable halogen lamp, personal computer, web camera (webcam) to capture the image, and plastic bottle as an object of visual inspection. From this experiment, the maximum regular speed of a rotating object was 106 rpm. The result shows the system is accurate to determine between acceptable (normal) and non-acceptable (no cap or misplace of cap) during the maximum speed when the distance between webcam and the object was at 15 cm.