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研究生： 陳佳琳
Chia-Lin Chen
論文名稱： 整合機器學習和基於網頁的數控加工機雲端製造執行系統
Cloud manufacturing execution system based on web and machine learning for CNC machine
指導教授： 陳明志
Ming-Jyh Chern
口試委員: 林柏廷
Po-Ting Lin
王謹誠
Chin-Cheng Wang
學位類別： 碩士
Master
系所名稱： 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年： 2021
畢業學年度： 109
語文別： 英文
論文頁數： 93
中文關鍵詞： Manufacturing execution system 、Web-based applications 、Cloud manufacturing 、Internet of things 、Machine learning 、Teachable machine
外文關鍵詞： 製造執行系統, 基於網頁的應用程式, 雲製造, 物聯網, 機器學習, 可教式機器
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    • 在現今的社會中，製造執行系統(MES)已經成為各個公司不可或缺的一部分。有效的連接物理機床與軟體之間的橋樑，是智能工廠必備的條件之一。這個困難在於監控的過程中缺乏有效的獲取、接收及處理數據，因此使用MES可以獲取數據到特定的雲端上，以此降低人工操作的失誤，提升數據的一致性及雲計算的快速性。
    然而MES還缺乏決策機制，應用人工智能中的機器學習到製造領域，達成智能製造執行系統，利用機器學習的技術透過完整的數據訓練，達成加工是否完全的判斷。

    本研究透過Google開源的可教式機器應用程式介面來訓練收集到的資料。即時的數據傳輸，達成遠端也能實時監控工件的功能。而客戶端藉由MES，也可期望在任何時間地點或不同的使用環境來管理及觀察即時顯現的系統資訊。使用到的技術包涵虛實整合系統、物聯網、雲製造、基於網頁的應用程式、機器學習和資訊安全。\\

    本研究透過數控工具機(CNC)工廠作為測試對象，首先架設雲端平台，透過網路接收網頁上傳至伺服器的檔案，並傳送訊號給工具機。當工具機讀取到信號後，會自動下載檔案並加工。最後設立工作站，透過相機捕捉圖像並上傳至雲端。在網頁應用程式中，應用機器學習的方法分辨工件是否加工完全。相較於以往的安裝軟體的金錢和時間的成本，以及未來隨著科技的發展，該系統的彈性及成本都有很大的發展空間。

    In today’s society, the manufacturing execution system (MES) has become an indispensable part of each company. The effective connection between physical machines and software is one of the necessary conditions for smart factories. The difficulty of effective connection lies in the lack of effective acquisition, reception, and processing of data in the monitoring process. Therefore, using MES can obtain data to a cloud, thereby reducing manual operation errors, improving data consistency and the speed of cloud computing.

    On the other hand, MES still lacks the decision support capabilities of the workshop. The study utilizes machine learning (ML) technology to achieve this goal through complete training data. Teachable Machine is employed to train collected data. Teachable Machine is an open-source library for machine learning from Google. The quality of workpieces can be remotely monitored in real-time. The client also expects to manage and observe the real-time information at any time and place. The technology included a cyber-physical system (CPS), Internet of Things (IoT), cloud manufacturing (CM), web-based applications, machine learning (ML), and information security.

    This study considers a computer numerical control (CNC) machine center as the test object of this study. First, a web application on a cloud platform is built. The physical message will be obtained through the internet. Secondly, users upload NC files to the CNC machine center and start the machine as soon as the machining center receives the signal from the central control system. Thirdly, a workstation that can capture images through the camera is established. In the end, the machine learning method is adopted in the web application to distinguish whether the workpiece is properly processed. The web-based cloud MES is more time-saving and cost-saving than the previous MES. In the future, since this system is very extendable, new technologies can be easily included in the present system.

    CONTENTS Chinese Abstract . . . . . . . . . . . . . . . . .i Abstract . . . . . . . . . . . . . . . . . . . iii Acknowledgements . . . . . . . . . . . vi Contents . . . . . . . . . . . . . . . . . . . vii Nomenclatures . . . . . . . . . . . . . . . . . . xi List of Tables . . . . . . . . . . . . . . . . xv List of Figures . . . . . . . . . . . . . . . . xv 1 INTRODUCTION 1 1.1 Motivation . . . . . . . . . . . . . . . . . .1 1.2 Literature review . . . . . . . . . . . . . 3 1.3 Objectives . . . . . . . . . . . . . . . . . . 6 1.4 Synopsis . . . . . . . . . . . . . . . . . . 7 2 METHODOLOGY 8 2.1 Manufacturing execution system . . . . . . 9 2.1.1 Physical objects layer . . . . . . . . . . . . 10 2.1.2 Cloud layer . . . . . . . . . . . . . . . . . . . 11 2.1.3 Service layer . . . . . . . . . . . . . . . . . . 12 2.2 Internet of things . . . . . . . . . . . . . . . . 12 2.3 Cloud manufacturing . . . . . . . . . . . . . . 14 2.4 Machine learning . . . . . . . . . . . . . . . . . 15 2.4.1 Teachable Machine . . . . . . . . . . . . . . .16 2.4.2 Deep learning . . . . . . . . . . . . . . . . . . 16 2.4.3 How to evaluate the model’s performance. . . . . . 18 2.5 Open source computer vision library. . . . . . . . . . . . 22 2.6 Web-based application . . . . . . . . . . . . . . . . . . . . . 22 2.6.1 Resource representational state transfer. . . . . . . . 23 2.7 Information security . . . . . . . . . . . . . . . . . . . . . . .24 2.7.1 Password encryption . . . . . . . . . . . . . . . . . . . . . .24 2.7.2 Resource authorization and allocation . . . . . . . . . . 25 2.7.3 Resist brute force attacks. . . . . . . . . . . . . . . . . . . 26 3 RESULTS AND DISCUSSION 27 3.1 The functions on the web application . . . . . . . . . . . . 27 3.1.1 Files uploading and machining . . . . . . . . . . . . . . . . 28 3.1.2 Error information recording . . . . . . . . . . . . . . . . . . 29 3.1.3 Judging the quality of the workpieces . . . . . . . . . . . 30 3.2 Camera specifications comparison. . . . . . . . . . . . . . . 31 3.3 Model evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4 Objects classification . . . . . . . . . . . . . . . . . . . . . . . . 34 4 CONCLUSIONS AND FUTURE WORKS 36 4.1 Conclusions . . . . . . . . . . . . . . . . . . 36 4.2 Future works . . . . . . . . . . . . . . . . . . 37 BIBLIOGRAPHY . . . . . . . . . .. . . . . . . . 39 APPENDIX . . . . . . . . . . . . . . . . . . . . . 47 A Machine Learning for the Web 60 B The database of this study 64

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