Data Acquisition System to Support Predictive Maintenance on Soft Laminator Machines in an Electronics Manufacturing Company
Abstract
This study presents the design and implementation of an Internet of Things (IoT)-based data acquisition system for a soft laminator (profile wrapping) machine used in electronic audio device manufacturing. The system aims to enable real-time monitoring of critical process parameters, including heater roll temperature, heater dry zone temperature, and roll spacing, which are essential for maintaining product quality and reducing machine downtime. The proposed system employs an ESP32 microcontroller integrated with DS18B20 temperature sensors and VL53L0X distance sensors, supported by an Ethernet W5500 module for reliable data transmission to a MySQL-based server. A web-based dashboard was developed to visualize sensor data, display alerts, and log historical records. Experimental results show that the system achieved high accuracy, with mean absolute errors of 0.38 °C (0.63%) for heater roll temperature, 0.44 °C (0.73%) for heater dry zone temperature, and 0 mm (0%) for all distance sensors, well within the industrial tolerance of <1%. Additionally, the indicator subsystem—consisting of LEDs and buzzers—responded consistently to simulated fault conditions such as sensor failure and network disconnection. Overall, the developed system demonstrates reliable performance for industrial monitoring applications and offers a foundation for implementing predictive maintenance in manufacturing environments.
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