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Real-Time Torque Control and Industry 4.0 Integration of Industrial Hand Tools Using Artificial Intelligence

Kader Nikbay Oylum1✉,
Turgay Tugay Bilgin2,
Ahmet Emir Belkan3
1Trex Dijital Akıllı Üretim Sistemleri A.Ş.
2Bursa Teknik Üniversitesi
3Trex Dijital Akıllı Üretim Sistemleri A.Ş.
Published:December 31, 2024
DOI: 10.56038/ejrnd.v4i4.565
Vol. 4, No. 4

Abstract

In this study, a novel system has been developed to adapt legacy equipment widely used in industrial production lines to Industry 4.0 standards. This research, which aims to digitalize electro-mechanical hand tools used in critical assembly operations and integrate them with higher-level system software, presents an innovative approach based on the hybrid use of artificial neural networks and embedded systems. The developed system can perform real-time torque level prediction by analyzing integrated data from multiple sensors, including voltage measurements, motor current readings, accelerometer data, and gyroscope measurements. The artificial intelligence component of the system consists of the integration of Long Short-Term Memory (LSTM) models running on the server side and optimized Multilayer Perceptron (MLP) models running on the embedded system. In tests conducted on a balanced dataset of 6000 samples, the LSTM model achieved an accuracy rate of 94.8%. Additionally, the embedded MLP model demonstrated a 92.3% binary classification success with a response time lower than 100ms. The system integration implemented using TCP/IP Open Protocol achieved network latency values below 50ms and successfully delivered 99.9% of data packets without loss or corruption. The system developed as a result of this study has demonstrated that legacy equipment can be made compatible with Industry 4.0 with minimal hardware modifications, while automating quality control processes and establishing data-driven decision-making mechanisms. This approach stands out as a cost-effective and scalable solution in industrial digital transformation projects.

Keywords
Industry 4.0Embedded SystemsArtificial Neural NetworksLSTMMLPDigital TransformationTorque ControlIndustrial AutomationQuality ControlSmart Manufacturing

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Cite This Article
Oylum, K. N., Bilgin, T. T., Belkan, A. E. (2024). Real-Time Torque Control and Industry 4.0 Integration of Industrial Hand Tools Using Artificial Intelligence. *The European Journal of Research and Development*, 4(4), 247-263. https://doi.org/10.56038/ejrnd.v4i4.565

Bibliographic Info

JournalThe European Journal of Research and Development
Volume4
Issue4
Pages247–263
PublishedDecember 31, 2024
eISSN2822-2296