Acta Mechanica Slovaca 2025, 29(3):24-29 | DOI: 10.21496/ams.2025.025

Design of Control System Architecture for Intelligent Fixture

Ján Kušnír1, *, Jozef Brindza1, Michal Demko1, Filip Dominik1, Marek Vrabeľ1
Prototyping and Innovation Centre, Faculty of Mechanical Engineering, Technical University of Kosice, Slovak Republic, Park Komenského 12A, 042 00 Košice-Sever, Slovakia

We present a compact architecture for an intelligent fixture aimed at stabilizing the milling of thin-walled aerospace parts. The system fuses multi-sensor inputs tri-axial accelerometers (5-30 kHz) for vibration/chatter, strain or dynamometer signals (1-5 kHz) for cutting/clamping loads, and low-rate pressure/temperature (≤ 100 Hz) for thermal/fixturing state with an "edge to cloud" computing stack. A Raspberry Pi 5 performs synchronized windowing (0.5-1.0 s, 50% overlap), time-frequency analysis (STFT/wavelets), and lightweight features (RMS, crest factor, band energies, relative wavelet energy, entropy). Unsupervised detectors (one-class models, LSTM autoencoders) provide fast on-device deviation alerts, while server services handle training/retuning, dashboards, a model registry, and over-the-air deployment. Telemetry uses MQTT for efficient streaming and OPC UA for typed information models, PTP (IEEE-1588) aligns timestamps. A private QoS-aware 5G link carries features and event-driven raw snippets, supporting a split control strategy, safety-critical actions stay local, and supervisory updates (feeds/speeds, ae/ap, clamping) close over 5G. Anticipated benefits include improved accuracy and surface integrity, reduced scrap/rework, and better adaptability across parts and machines. Validation will proceed via stability-lobe experiments and trials on aerospace-grade components, with a planned upgrade to simultaneous-sampling IEPE acquisition and Acoustic Emission sensing for higher bandwidth and earlier wear detection.

Keywords: Intelligent fixtures, thin-walled components, in-process monitoring, edge computing, private 5G, machining.

Received: September 10, 2025; Revised: September 10, 2025; Accepted: October 21, 2025; Published: October 1, 2025  Show citation

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Kušnír, J., Brindza, J., Demko, M., Dominik, F., & Vrabeľ, M. (2025). Design of Control System Architecture for Intelligent Fixture. Acta Mechanica Slovaca29(3), 24-29. doi: 10.21496/ams.2025.025
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References

  1. . Möhring, H.-C.; Denkena, B.; Ahrens, M.; Hess, S. Intelligent Fixtures for High Performance Machining. Procedia CIRP 2016, 46, 383-390. Go to original source...
  2. . Feng, Q.; Zhang, Z.; Li, L.; et al. Intelligent Soft Jaws for Clamping Complex Geometric Surfaces Using Active-Controlled MRF in a 3D-Printed TPU Cushion. Production Engineering 2025. Go to original source...
  3. . Li, Z.; Fei, F.; Zhang, G. Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors. Sensors 2022, 22(15), 5901. Go to original source...
  4. . Cheng, J.; Xu, C.; Chen, W.; Xu, X. 5G in Manufacturing: A Literature Review and Future Research. International Journal of Advanced Manufacturing Technology 2022, 120, 1301-1323.
  5. . Shokrani, A.; Doğan, Ö.; Burian, A.; et al. Sensors for In-Process and On-Machine Monitoring of Machining Operations. CIRP Journal of Manufacturing Science and Technology 2024. Go to original source...
  6. . Farooq, M.S.; et al. A Survey on the Role of Industrial IoT in Manufacturing. Sensors 2023. Go to original source...
  7. . Bakker, O.J.; Papastathis, T.N.; Popov, A.A.; Ratchev, S. Active Fixturing: Literature Review and Future Research Directions. International Journal of Production Research 2013, 51(11), 3171-3190. Go to original source...
  8. . Nee, A.Y.C.; Senthil Kumar, A.; Tao, Z.J. An Intelligent Fixture with a Dynamic Clamping Scheme. Proceedings of the IMechE Part B: Journal of Engineering Manufacture 2000, 214, 183-196. Go to original source...
  9. . Wang, Y.F.; Wong, Y.S.; Fuh, J.Y.H. Off-line Modelling and Planning of Optimal Clamping Forces for an Intelligent Fixturing System. International Journal of Machine Tools & Manufacture 1999, 39(2), 253-271. Go to original source...
  10. . Busboom, A.; et al. Automated Generation of OPC UA Information Models. Journal of Industrial Information Integration 2024. Go to original source...
  11. . Kasiviswanathan, S.; et al. Machine-Learning- and Internet-of-Things-Driven Techniques for Monitoring Tool Wear in Machining: A Review. Journal of Sensor and Actuator Networks 2024, 13(5), 53. Go to original source...
  12. . Teti, R.; Jemielniak, K.; O'Donnell, G.; Dornfeld, D. Advanced Monitoring of Machining Operations. CIRP Annals 2010, 59(2), 717-739. Go to original source...
  13. . Sürücü, O.; Bonab, H.R.; Şimşek, B. Condition Monitoring Using Machine Learning: A Review of the Current State and Future Trends. Expert Systems with Applications 2023, 224, 120037. Go to original source...
  14. . Wang, W.-K.; Han, S.; Wang, C.; Zhang, J. Chatter Detection Methods in Machining Processes: A Comprehensive Review. International Journal of Machine Tools and Manufacture 2022, 185, 103707. Go to original source...
  15. . Navarro-Devia, J.H.; Rimpault, X.; Biermann, D. Chatter Detection in Milling Processes - A Review on Signal Processing and Condition Classification. International Journal of Advanced Manufacturing Technology 2023, 129, 4441-4476. Go to original source...
  16. . Hauptfleischová, B.; Hadas, Z.; Hrušecká, D.; et al. In-Process Chatter Detection in Milling: Comparison of the Robustness of Selected Entropy Methods. Journal of Manufacturing and Materials Processing 2022, 6(5), 125. Go to original source...
  17. . Raspberry Pi Ltd. Raspberry Pi 5 Product Brief/Documentation, 2024. From https://datasheets.raspberrypi.com/rpi5/raspberry-pi-5-product-brief.pdf
  18. . National Instruments. NI USB-6001 Specifications. Technical Datasheet, Rev. 374369A. From https://www.ni.com/docs/en-US/bundle/usb-6001-specs/resource/374369a.pdf
  19. . Maia, L.H.A.; Fernandes, L.H.; da Silva, N.C.; et al. Real-Time Monitoring of Tool Wear with Acoustic Emission and STFT Techniques. Lubricants 2024, 12(11), 380. Go to original source...
  20. . Quectel. RM530N-GL 5G Module Datasheet, 2024. From https://www.quectel.com/product/5g-rm530n-gl/
  21. . Wu, Y.; Ni, J.; Menq, C.-H. Feature Extraction and Assessment Using Wavelet Packets for Monitoring of Machining Processes. Journal of Manufacturing Science and Engineering 1996, 118(3), 367-372. Go to original source...
  22. . Peng, Z.K.; Peter, W.T.; Chu, F.-L. A Review of the Application of Wavelet Transform in Machine Condition Monitoring and Fault Diagnosis. Mechanical Systems and Signal Processing 2004, 18(2), 199-221. Go to original source...
  23. . Zhang, Y.; Guo, X.; Wu, J.; et al. Tool Wear Condition Monitoring Based on Deep Learning and Time-Frequency Images. Sensors 2023, 23(10), 4595. Go to original source...
  24. . Xie, Z.; Hua, Y.; Tang, B.; et al. Data-Driven Unsupervised Anomaly Detection of Multi-Sensor Machine Tools via Hierarchical Augmented Autoencoders. Mechanical Systems and Signal Processing 2024, 210, 111237.
  25. . Omole, S.; Hou, X.; Zolotarev, M.; et al. Using Machine Learning for Cutting Tool Condition Monitoring: A Review and Opportunities for Deep Learning. International Journal of Advanced Manufacturing Technology 2024, 120(7-8), 4293-4329.
  26. . IEEE Std 1588-2019. IEEE Standard for a Precision Clock Synchronization Protocol for Networked Measurementand Control Systems. From https://standards.ieee.org/ieee/1588/6825/
  27. . Denzler, P.; et al. Static Timing Analysis of OPC UA PubSub. Technical University of Denmark, 2021. Go to original source...
  28. . Rezabek, F.; et al. Assessment of OPC UA PubSub at Scale Using TSN. Technical University of Munich, 2024.
  29. . Athar, A.; Liu, H.; Gopalsamy, S.; et al. Deep Learning-Based Anomaly Detection Using LSTM Autoencoders in CNC Machine Centers. PeerJ Computer Science 2024, 10, e2389. Go to original source...
  30. . Stouffer, K.; Pillitteri, V.; Lightman, S.; et al. NIST Special Publication 800-82 Rev. 3: Guide to Operational Technology (OT) Security. National Institute of Standards and Technology, 2023. Go to original source...
  31. . Çekik, R.; Toygar, Ö.; Karagöz, T. Deep Learning for Anomaly Detection in CNC Machine Vibration Data: A RoughLSTM-Based Approach. Applied Sciences 2025, 15(6), 3179. Go to original source...
  32. . Drew, D.; Kumar, S.; Mohan, S.; et al. Application of Machine Learning for Tool Condition Monitoring Using a Sensor-Integrated Tool Holder. Journal of Manufacturing Processes 2025, 96, 339-351.

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