Cloud Based CNC Monitoring System

Abstract

Computer Numerical Control (CNC) machines are widely used in modern manufacturing industries because they provide high precision and consistent production quality. However, many CNC machines are still monitored manually or through limited local systems, which makes it difficult to identify faults at an early stage. This project presents a Cloud Based CNC Monitoring System that allows real-time monitoring of important machine parameters such as temperature and current. In this system, sensors are connected to an ESP32 microcontroller to collect machine condition data. The collected data is transmitted to the cloud platform using the MQTT communication protocol through Wi-Fi. AWS IoT Core is used to receive and process the data, while visualization dashboards such as Grafana or AWS QuickSight display the machine parameters. The system enables operators to monitor the machine remotely and identify abnormal conditions quickly. This approach improves machine reliability, reduces downtime, and supports modern smart manufacturing practices.

Introduction

Computer Numerical Control (CNC) machines are widely used in modern manufacturing industries because they produce components with high precision and efficiency. However, CNC machines operate under various mechanical and electrical conditions, and parameters such as temperature, current, vibration, and load can affect their performance. Traditional monitoring methods are often manual and provide limited information, which makes it difficult to detect faults early and may lead to machine downtime and higher maintenance costs.

To improve monitoring, this study proposes an IoT-based cloud monitoring system for CNC machines. The system uses sensors such as the DHT11 temperature sensor and ACS712 current sensor connected to an ESP32 microcontroller. The ESP32 collects machine data and sends it to a cloud platform using Wi-Fi and the MQTT communication protocol. The data is then stored and visualized on cloud dashboards using platforms like AWS IoT, Grafana, or AWS QuickSight, allowing operators to monitor machine conditions remotely.

The system components include ESP32 microcontroller, sensors, relay module, DC motor (for simulation), power supply, and AWS IoT cloud platform. The methodology involves collecting sensor data, processing it with the microcontroller, transmitting it to the cloud, and displaying it through real-time dashboards.

Experimental testing using a DC motor as a CNC spindle simulation showed that the system successfully monitored temperature and current in real time. The dashboard displayed graphical data that helped identify conditions such as motor overheating or increased load, which could indicate potential machine problems. The system demonstrated quick response time and reliable data transmission.

Overall, the proposed IoT and cloud-based CNC monitoring system improves machine reliability, enables real-time monitoring, predictive maintenance, and data-driven decision making, and supports the implementation of Industry 4.0 technologies in modern manufacturing industries.

Conclusion

The Cloud Based CNC Monitoring System developed in this study demonstrates the practical application of Internet of Things (IoT) technology and cloud computing in industrial machine monitoring. The system is capable of collecting important machine parameters such as temperature and electrical current through sensors and transmitting the collected data to a cloud platform for real-time observation. This approach provides a more efficient way of monitoring machine performance compared to traditional manual monitoring methods. The monitoring dashboard plays an important role in presenting the collected data in a clear and understandable form. By displaying parameters such as temperature and current in graphical format, operators and engineers can easily observe the operating condition of the machine. The real-time visualization of machine data helps in identifying unusual variations and abnormal operating conditions at an early stage. Another advantage of the developed system is the ability to perform remote monitoring. Since the data is stored in the cloud platform, machine conditions can be accessed from any location with an internet connection. This feature reduces the need for constant physical inspection of machines and allows engineers to supervise machine performance more efficiently. The monitoring system also contributes to improving machine reliability and reducing unexpected downtime. By continuously tracking machine parameters, the system can help detect possible faults before they lead to serious machine failure. Early fault detection enables timely maintenance actions and helps industries avoid costly production interruptions. In addition, the integration of sensors, microcontrollers, and cloud technology supports the concept of smart manufacturing and Industry 4.0. The use of digital monitoring systems allows industries to collect valuable operational data that can be used for performance analysis and maintenance planning. Overall, the proposed Cloud Based CNC Monitoring System provides a practical and cost-effective solution for monitoring industrial machines. The system enhances machine safety, improves operational efficiency, and supports modern manufacturing practices through real-time data monitoring and analysis.

References

[1] Ahmad, M. B. I., IoT-Based CNC Monitoring System, 2020. [2] Mosaad, N., Cloud-Based CNC Machine Diagnostic System, 2021. [3] Justus, V., Machine Monitoring Using Sensor Data and Machine Learning, 2019. [4] Martinov, G. M., Cloud Architecture for CNC Systems, 2018. [5] Patel, D., Predictive Maintenance using AWS IoT, 2022. [6] Amazon Web Services Documentation, AWS IoT Core. [7] Espressif Systems, ESP32 Technical Reference Manual. [8] Shao, Y., & Li, Z. (2021). Cloud-based Real-Time CNC Machine Monitoring and Data Analytics Using IoT [9] Chung, H. S., & Lee, C. (2020). An Integrated IoT Solution for CNC Machine Condition Monitoring and Predictive Maintenance. [10] García, M., & Rodríguez, R. (2022). Predictive Maintenance in CNC Machines Using AWS Cloud and IoT Technologies. [11] Teng, S., & Liu, Z. (2019). CNC Machine Tool Health Monitoring Using Cloud and Edge Computing for Industrial IoT. [12] Hassan, M., & Lee, J. (2021). Cloud-Enabled Smart CNC Systems for Predictive Maintenance and Quality Monitoring. [13] Zhou, H., & Zhang, Z. (2020). Big Data and Cloud Computing for Machine Tool Diagnostics in Industry 4.0. [14] Zhao, X., & Wu, X. (2020). Edge Computing for CNC Machine Monitoring and Smart Manufacturing.

Copyright

Copyright © 2026 Infant Jegan Rakesh A J, Nandhini S, Balakrishnan R, Messco Baxter Banda, Muhammed Ghaous A. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.