Chen, Jian WenJian WenChenMENG-SHIUN TSAIHung, Che LunChe LunHung2026-01-082026-01-0820259798331568740https://www.scopus.com/record/display.uri?eid=2-s2.0-105022750421&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/735166Real-time monitoring is essential in industrial applications such as tool wear detection and temperature compensation, where maintaining process stability and ensuring product quality are critical. To address the challenges caused by heterogeneous data sources and device types, this paper proposes a real-time edge computing system based on the Intel Edge Insights for Industrial (EII) platform. The system utilizes multiple AI Neural Compute Sticks (NCS) to enable accelerated and parallel inference for multiple tasks. The system integrates two AI models in separate containers. One model performs tool wear detection using a U-Net segmentation network, and the other conducts temperature compensation through a one-dimensional convolutional neural network. Each container operates on an independent NCS to support efficient and isolated task execution. Data communication is managed by the EII Message Bus, stored in InfluxDB, and visualized using Grafana, enabling complete data flow from acquisition to real-time presentation. Experimental results demonstrate that the system can concurrently handle multiple monitoring tasks with low latency and reliable performance. The proposed architecture enhances operational efficiency, supports intelligent automation, and provides a scalable solution for smart manufacturing environments.falseEdge ComputingReal-time MonitoringSmart ManufacturingTemperature CompensationTool Wear Detection[SDGs]SDG9conference paper10.1109/HPCC67675.2025.001432-s2.0-105022750421