EVERGREEN

Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy

ISSN:2189-0420 (Print until Mar 2020)
ISSN:2432-5953 (Online)

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IoT-Based CNC Coolant Quality Detection using Photodiode and Gas Sensors with Incremental Learning Algorithms

Ardani Cesario Zuhri1, Mario Ardhany1,*, Agus Widodo1, Ratna Mayasari2, Albertus Rianto Suryaningrat1, Galang Ilman Islami1, Ahmad Musthofa1, Nasril1, Danny Mokhammad Gandana1, Cecep Sujana1
1Research Center for Manufacturing Technology of Production Machinery, National Research and Innovation Agency, Indonesia
2Research Center for Sustainable Industrial and Manufacturing Systems, National Research and Innovation Agency, Indonesia
*Author to whom correspondence should be addressed:
E-mail: mari026@brin.go.id (MA)
Evergreen, Vol. 13, Iss. 01, pp. 57–75, 2026
DOI: 10.5109/7405128
Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International
Received: May 27, 2025 | Revised: July 04, 2025 | Accepted: December 22, 2025 | Published: March 2026
Abstract
The use of coolant in CNC machining for a certain period affects the quality of coolant liquid and requires maintenance and replacement schedules. A coolant that is no longer suitable for use can pollute the surrounding air, water, and soil. The feasibility of using a coolant currently depends on manual observations by the operator. In this research, a sensor system was designed to identify critical change anomalies in coolant in a fast, precise, and nondestructive manner. The system was implemented on a Raspberry Pi equipped with photodiode and gas sensors to identify coolant conditions using IoT and cloud-based machine learning. The use of several gas sensors and one light sensor from this system can obtain different patterns for three coolant quality categories, namely very bad, bad, and good, based on pH testing and microbiological analysis. As new testing data may arrive gradually, this study emphasizes the use of incremental learning algorithms. Our experimental results indicate that the identification of coolant quality has a high success rate, with the highest average accuracy of 96.17% achieved by stochastic gradient descent-logistic regression, followed closely by stochastic gradient descent-SVM at 96.16%, gaussian naive bayes at 94.93%, passive-aggressive at 92.72%, and perceptron at 92.45%. The implementation of this system is expected to replace traditional measurement with the use of human nose and eye senses to obtain the same measurements as different human examiners obtain different results.
Keywords
classification; cnc; coolant quality; incremental learning; machine learning
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