Integrating Low-Cost Mini CNC Machines with IoTEnabled Energy Monitoring and Machine Learning for Sustainable Manufacturing

Integrating Low-Cost Mini CNC Machines with IoTEnabled Energy Monitoring and Machine Learning for Sustainable Manufacturing
  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-6
Year of Publication : 2024
Author : Tawan Katduang, Dechrit Maneetham, Padma Nyoman Crisnapati, Wichian Srichaipanya
DOI : 10.14445/22315381/IJETT-V72I6P109

How to Cite?

Tawan Katduang, Dechrit Maneetham, Padma Nyoman Crisnapati, Wichian Srichaipanya, "Integrating Low-Cost Mini CNC Machines with IoTEnabled Energy Monitoring and Machine Learning for Sustainable Manufacturing," International Journal of Engineering Trends and Technology, vol. 72, no. 6, pp. 82-91, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I6P109

Abstract
This research investigates the integration of low-cost mini CNC machines with the Internet of Things (IoT)-enabled energy monitoring and machine learning techniques to enhance sustainable manufacturing practices. Through meticulous mechanical, electronic, and software design, a mini CNC machine based on the ESP8266 platform is developed, enabling comprehensive data acquisition and analysis of energy consumption patterns during machining processes. Leveraging machine learning classification techniques, including Logistic Regression, K-Nearest Neighbors, Support Vector Classification, Decision Trees, Random Forest, Gradient Boosting, and AdaBoost, Gradient Boosting emerges as the most effective approach for energy consumption prediction in mini CNC operations, showcasing notable accuracy and robustness. By providing insights into energy efficiency and sustainability in manufacturing, this research contributes to the ongoing discourse on sustainable practices and lays the groundwork for further advancements in CNC technology and education. This integration offers a practical solution to the challenges of accessibility and affordability in CNC education and small-scale manufacturing, giving a solution for the broader adoption of sustainable manufacturing practices in various industrial settings.

Keywords
Mini CNC machines, Internet of Things, Energy monitoring, Machine Learning, Sustainable manufacturing.

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