PNEUMATIC CONVEYOR SORTING SYSTEM WITH MACHINE VISION

Abstract

Automation in industrial sorting systems plays a vital role in improving efficiency, accuracy, and productivity. This project presents the design and implementation of a Pneumatic Conveyor Sorting System integrated with Machine Vision for automated object classification based on size and shape. The system consists of a proximity sensor and PLC-based logic for size identification, along with a camera module for shape detection. A Raspberry Pi acts as the central processing unit, receiving inputs from both the sensor system and the vision module.The captured data is processed using image processing techniques to extract object features such as dimensions and contours. Based on the classification results, control signals are sent through a relay module to actuate servo motors and pneumatic actuators. These actuators direct objects into designated bins according to their size and shape.The integration of sensor-based size detection with vision-based shape identification ensures high accuracy and reliability. The system reduces manual effort, minimizes sorting errors, and enhances operational efficiency. This solution is suitable for applications in manufacturing, packaging, and material handling industries, with future scope for advanced AI-based classification and IoT integration.

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