Optimising Pressure Testing in Manufacturing: Machine Learning for Energy Efficiency and Sustainability
Abstract
This study explores the application of Machine Learning (ML) techniques to optimise pressure drop tests, which are vital for assessing quality and ensuring the integrity of products during manufacturing. Traditional pressure testing methods are energy-intensive and time-consuming, posing industrial efficiency and sustainability challenges. Leveraging a dataset of 1.7 million test records of fuel pumps collected over 15 months, this study developed predictive models that significantly reduced test durations—from an average of 10 seconds to as low as 2 seconds, thereby reducing energy consumption and increasing efficiency. The Random Forest model demonstrated superior performance, achieving an RMSE of 0.0017 and an R² score of 0.9925. This research underscores the potential of ML in transforming manufacturing processes, offering enhanced quality assurance, operational efficiency, and environmental sustainability. These findings provide practical solutions for improving energy efficiency and advancing quality control in manufacturing processes.
Keywords
References
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Details
Primary Language
English
Subjects
Electrical Engineering (Other)
Journal Section
Research Article
Publication Date
June 24, 2026
Submission Date
February 19, 2026
Acceptance Date
February 25, 2026
Published in Issue
Year 2026 Volume: 6 Number: 2
APA
Yildiz, E., & Yurtseven, M. B. (2026). Optimising Pressure Testing in Manufacturing: Machine Learning for Energy Efficiency and Sustainability. Turkish Journal of Electrical Power and Energy Systems, 6(2), 79-87. https://doi.org/10.67047/tepes.1892407
AMA
1.Yildiz E, Yurtseven MB. Optimising Pressure Testing in Manufacturing: Machine Learning for Energy Efficiency and Sustainability. TEPES. 2026;6(2):79-87. doi:10.67047/tepes.1892407
Chicago
Yildiz, Erhan, and Mustafa Berker Yurtseven. 2026. “Optimising Pressure Testing in Manufacturing: Machine Learning for Energy Efficiency and Sustainability”. Turkish Journal of Electrical Power and Energy Systems 6 (2): 79-87. https://doi.org/10.67047/tepes.1892407.
EndNote
Yildiz E, Yurtseven MB (June 1, 2026) Optimising Pressure Testing in Manufacturing: Machine Learning for Energy Efficiency and Sustainability. Turkish Journal of Electrical Power and Energy Systems 6 2 79–87.
IEEE
[1]E. Yildiz and M. B. Yurtseven, “Optimising Pressure Testing in Manufacturing: Machine Learning for Energy Efficiency and Sustainability”, TEPES, vol. 6, no. 2, pp. 79–87, June 2026, doi: 10.67047/tepes.1892407.
ISNAD
Yildiz, Erhan - Yurtseven, Mustafa Berker. “Optimising Pressure Testing in Manufacturing: Machine Learning for Energy Efficiency and Sustainability”. Turkish Journal of Electrical Power and Energy Systems 6/2 (June 1, 2026): 79-87. https://doi.org/10.67047/tepes.1892407.
JAMA
1.Yildiz E, Yurtseven MB. Optimising Pressure Testing in Manufacturing: Machine Learning for Energy Efficiency and Sustainability. TEPES. 2026;6:79–87.
MLA
Yildiz, Erhan, and Mustafa Berker Yurtseven. “Optimising Pressure Testing in Manufacturing: Machine Learning for Energy Efficiency and Sustainability”. Turkish Journal of Electrical Power and Energy Systems, vol. 6, no. 2, June 2026, pp. 79-87, doi:10.67047/tepes.1892407.
Vancouver
1.Erhan Yildiz, Mustafa Berker Yurtseven. Optimising Pressure Testing in Manufacturing: Machine Learning for Energy Efficiency and Sustainability. TEPES. 2026 Jun. 1;6(2):79-87. doi:10.67047/tepes.1892407