FPGA應用於5G智慧化加工之實時異常現象監控;Real-time Anomaly Monitoring of 5G-Enabled Smart Manufacturing using FPGA

NCUIR > Engineering College > Graduate Institute of Mechanical Engineering > Electronic Thesis & Dissertation > Item 987654321/93868

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93868

Title:	FPGA應用於5G智慧化加工之實時異常現象監控;Real-time Anomaly Monitoring of 5G-Enabled Smart Manufacturing using FPGA
Authors:	曾國翔;Tseng, Kuo-Hsiang
Contributors:	機械工程學系
Keywords:	FPGA;5G;ADC;監控;電化學放電加工;FPGA;5G;ADC;Anomaly Monitoring;ECDM
Date:	2023-08-19
Issue Date:	2024-09-19 17:43:32 (UTC+8)
Publisher:	國立中央大學
Abstract:	現今因「工業4.0」的驅使之下，利用大數據、人工智慧等技術來融入工業自動化，又稱為「智慧化加工」。因有高速且大量的數據傳輸需求，使得具備低延遲、高速度、多連結等特性的第五代行動通訊技術(5G)是必須仰賴的關鍵，且市面上許多訊號擷取系統無法負荷高傳輸速度與大頻寬的需求，本研究決定開發現場可程式化邏輯閘陣列(FPGA)，利用硬體描述語言(HDL)設計其架構，並使用電流探棒量取智慧化工廠內的電化學放電加工(ECDM)機之加工訊號，訊號儲存至FPGA中並進行處理後，利用5G傳輸至智慧化工廠之中央伺服器與邊緣計算模組，以獲取最新加工狀態，若從中發現異常現象，便命令電化學放電加工(ECDM)調整加工參數，使其成為一「閉迴路實時監控系統」，在無人監控狀態下排除異常現象。本研究自行開發「高速資料擷取系統」，其具有體積輕量化、高取樣率等優勢，作為加工異常現象之偵測工具，可為「工業4.0」帶來貢獻。;Due to the driving force of "Industry 4.0", the integration of technologies such as big data and artificial intelligence into industrial automation, also known as "smart manufacturing," has become prominent. With the high-speed and large-volume data transmission requirements, the 5th Generation Wireless System (5G) with features such as low latency, high speed, and multiple connections has become essential. However, many signal acquisition systems available on the market cannot meet the demands of high transmission speeds and large bandwidth. Therefore, this study is going to use Hardware Description Language (HDL) to develop a Field-Programmable Gate Array (FPGA) for this purpose. The signal during processing of Electrochemical Discharge Machining (ECDM) machine in the intelligent factory will be captured by a current probe and stored in the FPGA for further processing. The processed signals will be transmitted via 5G to the central server and edge computing module in the intelligent factory to obtain real-time information on the latest manufacturing status. If any abnormal phenomena are detected through this system, the Electrochemical Discharge Machining (ECDM) will be commanded to adjust the processing parameters. This system will be a “closed-loop real-time monitoring system”, that can optimize the manufacturing quality in an unmanned monitoring state. Developing a "high-speed data acquisition system" in this study, which has advantages such as lightweight and high sampling rate. This system serves as a detection tool for identify anomalies in process, and making further contributions to "Industry 4.0."
Appears in Collections:	[Graduate Institute of Mechanical Engineering] Electronic Thesis & Dissertation

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