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Volume 11
Issue 3
10.3390/pr11030772
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Article

Comparative Analysis of Machine Learning Approaches to Predict Impact Energy of Hydraulic Breakers

by
Sung-Hyun Kim
1,
Jong-Won Park
1,* and
Jae-Hoon Kim
2
1
Department of Reliability Assessment, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of Korea
2
School of Mechanical Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
*
Author to whom correspondence should be addressed.
Processes 2023, 11(3), 772; https://doi.org/10.3390/pr11030772
Submission received: 16 October 2022 / Revised: 3 March 2023 / Accepted: 3 March 2023 / Published: 5 March 2023
(This article belongs to the Special Issue Reliability and Engineering Applications)
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Abstract

Impact energy, the main performance subject of hydraulic breakers, is required to evaluate value from consumers. This study proposes a neural network algorithm-based model to predict the impact energy of a hydraulic breaker without measuring it. The proposed model was developed using 1451 data points for various parameters as an input to predict the impact energy of hydraulic breakers in a small class to a large class. Different machine learning methods have been studied, including correlation analysis, linear regression, and neural networks. The results revealed that the working pressure, working flow rate, chisel diameter, nitrogen gas pressure, operating frequency, and power significantly influenced impact energy formation. The results obtained provide a reliable model for predicting the impact energy of hydraulic circuit breakers of various sizes.
Keywords: hydraulic breaker; machine learning approach; impact energy; neural network hydraulic breaker; machine learning approach; impact energy; neural network

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MDPI and ACS Style

Kim, S.-H.; Park, J.-W.; Kim, J.-H. Comparative Analysis of Machine Learning Approaches to Predict Impact Energy of Hydraulic Breakers. Processes 2023, 11, 772. https://doi.org/10.3390/pr11030772

AMA Style

Kim S-H, Park J-W, Kim J-H. Comparative Analysis of Machine Learning Approaches to Predict Impact Energy of Hydraulic Breakers. Processes. 2023; 11(3):772. https://doi.org/10.3390/pr11030772

Chicago/Turabian Style

Kim, Sung-Hyun, Jong-Won Park, and Jae-Hoon Kim. 2023. "Comparative Analysis of Machine Learning Approaches to Predict Impact Energy of Hydraulic Breakers" Processes 11, no. 3: 772. https://doi.org/10.3390/pr11030772

APA Style

Kim, S.-H., Park, J.-W., & Kim, J.-H. (2023). Comparative Analysis of Machine Learning Approaches to Predict Impact Energy of Hydraulic Breakers. Processes, 11(3), 772. https://doi.org/10.3390/pr11030772

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