Design of Real-Time Monitoring System for Milling Machine

Liew, Weng Keat (2023) Design of Real-Time Monitoring System for Milling Machine. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

Text Full Text-LIEW WENG KEAT - RMH (1).pdf Restricted to Registered users only Download (13MB)

Abstract

This study is a designing of a real time monitoring system for milling machining process and a dashboard to display the monitoring results. The monitoring systems were designed to detect machine overheating and eventual failure, but as manufacturing processes have become more sophisticated, there is a need for an inventive, low-cost way of monitoring equipment conditions. In order to achieve the objective, the system is using Internet of Things (IoT) to achieve real-time monitoring. Sensors are used to detect the temperature and vibration condition of the milling machine. The detected data by the sensors are send to the Arduino for data storing. Raspberry Pi with inbuilt wifi module. Node-RED then share the sensors information to Microsoft azure which is the cloud data platform. The data information are displayed on the dashboard by receiving the shared data on the cloud. Therefore monitoring, capturing data and analysis can be done using the real-time monitoring system. Similar system are studied in order to further understand the current market system and understand the concept behind. This study presents the development of a real-time monitoring system for a milling machine using an accelerometer and a microcontroller. The system is designed to capture the acceleration data, which is then used to calculate the velocity root mean square (VRMS) to identify the vibration condition of the machine. The objective of this study is to develop a reliable monitoring system that can accurately detect and report any changes in the vibration level of the milling machine, thereby reducing the risk of machine failure and downtime. The study involves the use of an accelerometer and a microcontroller to capture and process the acceleration data, which is then transmitted wirelessly to a dashboard for real-time visualization and analysis. The dashboard features include graphs for acceleration and VRMS data, as well as alerts for abnormal vibration levels. The data collected is used as a benchmark to analyze the vibration and temperature severity of the machine based on the ISO. Overall, the monitoring system developed in this study can be used as an effective tool for condition monitoring and predictive maintenance of milling machines.