PT Journal
AU Kusnir, J
   Brindza, J
   Demko, M
   Dominik, F
   Vrabel, M
TI Design of Control System Architecture for Intelligent Fixture
SO Acta Mechanica Slovaca
PY 2025
BP 24
EP 29
VL 29
IS 3
DI 10.21496/ams.2025.025
DE Intelligent fixtures; thin-walled components; in-process monitoring; edge computing; private 5G; machining.
AB 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.
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