DESIGN AND CONSTRUCTION OF METAL DETECTOR DEVICE

Student: Chinonso Emmanuel Alakwem
Supervisor: Dr Onwunta Emea Kalu Onwunta
HOD: Dr Onwunta Emea Kalu Onwunta
Department of Electrical /Electronics Engineering
Engineering
Gregory University, Uturu, Abia State

Abstract

ABSTRACT This project presents the design and implementation of an IoT-enabled metal detector device that integrates traditional inductive sensing technology with modern wireless communication capabilities. The system utilizes an ESP32 microcontroller as the central processing unit, coupled with a 12V inductive proximity sensor for metallic object detection. A custom-designed printed circuit board incorporates an MT3608 boost converter for power management, enabling efficient voltage regulation from a 3.7V Li-ion battery to the required 12V sensor supply. The device features multiple user interface elements including a 0.96-inch OLED display for local feedback, programmable WS2812B RGB LEDs for visual indication, and a buzzer system with transistor-based driver circuit for audio alerts. Real-time data transmission is achieved through Wi-Fi connectivity to the Blynk IoT platform, enabling remote monitoring and mobile application control. Comprehensive testing validated the system's performance across key parameters: detection accuracy of 99.95% for metallic objects within a 2-15mm range (ferrous metals) and 5-8mm range (non-ferrous metals), system response time under 100ms, and continuous operation for 12 hours on battery power. The wireless connectivity demonstrated 98.7% uptime with reliable cloud data transmission every 2 seconds. The complete system was implemented at a total cost of ₦141,500, incorporating professional PCB manufacturing and modular JST connector systems for easy maintenance. Testing procedures included power system verification, sensor calibration using standard metal targets, functional validation of all subsystems, and integration testing for seamless communication between hardware and cloud platforms. The project successfully demonstrates the feasibility of creating cost-effective, IoT-enabled detection systems using readily available components while maintaining high performance standards. The modular design approach and comprehensive documentation provide a foundation for future enhancements and application-specific customizations in industrial, security, and educational contexts.

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