PT  - JOURNAL ARTICLE
AU  - Kušnír, Ján
AU  - Brindza, Jozef
AU  - Demko, Michal
AU  - Dominik, Filip
AU  - Vrabeľ, Marek
TI  - Design of Control System Architecture for Intelligent Fixture
DP  - 2025 Oct 1
TA  - Acta Mechanica Slovaca
PG  - 24--29
VI  - 29
IP  - 3
AID - 10.21496/ams.2025.025
IS  - 13352393
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.