AUTOMATED GREEN TECH METAL AND NON-METAL MECHATRONICS SORTER USING RASPBERRY PI AND REALTIME DATABASE

Abstract

Automation is a vital driver in modern industrial operations, enhancing efficiency, reducing human intervention, and ensuring precision in complex processes. Material sorting, essential in manufacturing, recycling, and mass production, demands speed, accuracy, and sustainability. This study introduces an automated material sorting system powered by a Raspberry Pi as the central processor, connected to sensors that differentiate metallic and non-metallic materials in real time. Automating this process reduces dependence on manual labor, minimizes human error, and promotes a safer, more efficient workplace. The system also features a real-time database for continuous monitoring, remote access to performance data, and instant alerts, facilitating timely decision-making and maintenance. This smart sorting solution boosts productivity while addressing the need for intelligent, scalable, and eco-friendly industrial technologies The researchers employed an experimental prototyping and development approach to design and construct the proposed automated material sorting system. The primary objective was to create a fully functional prototype using the specified components and materials. The prototype underwent multiple testing cycles and iterative refinements to optimize its performance, reliability, and efficiency. To provide a comprehensive understanding of the system, the study included detailed 2D and 3D design models, visually representing the physical structure and layout of the prototype. A comprehensive schematic diagram was also presented to illustrate the electrical connections and sensor integrations. The entire development process—from material selection to assembly and calibration—was thoroughly documented. To assess the system’s effectiveness and user acceptability, the researchers distributed structured questionnaires, and the resulting data were analyzed to inform potential enhancements and validate the system's practicality. This chapter presents the implementation outcomes and results of the project. It includes a detailed schematic diagram that illustrates the configuration and interconnections of all electronic components integrated into the prototype. Furthermore, it outlines the step-by-step assembly procedure followed during the system's construction, ensuring transparency and replicability. The completed prototype underwent comprehensive testing and was verified to be fully operational, successfully executing its intended functions by the defined design specifications and project objectives. This chapter presents the key findings from the development and implementation of the Inventory Conveyor Mechatronics System, which integrates a Raspberry Pi 5 and Arduino Uno with capacitive and inductive proximity sensors and a real-time database management system. The prototype successfully manages the movement and classification of containers on a motorized conveyor belt by utilizing sensor data to differentiate between metallic and non-metallic materials. Effective coordination between the Raspberry Pi and Arduino Uno enables streamlined data processing, control logic execution, and sensor response. The real-time database supports ongoing monitoring, performance evaluation, and data storage. Critical aspects such as hardware optimization, material efficiency, power consumption, and user feedback were thoroughly assessed, demonstrating the system’s reliability, functionality, and practical applicability in industrial automation and inventory management settings.