虛實整合之加工精度與效能優化智能監控系統

Cyber-Physical Intelligent Monitoring System for Machining Accuracy and Performance Optimization

10.6840/cycu201800112

張人文

iKM ； CPS ； 工具機 ； 雲端 ； 虛擬平台 ； iKM ； CPS ； Machine ； cloud ； Virtual platform

中原大學機械工程學系學位論文

2018年

碩士

王世明

繁體中文

本研究發展出一套可在加工前根據實驗建模與演算法同時優化精度、效率及耗能，進而產出適當CNC加工程式的虛實整合系統。該系統也與知識雲端資料庫(iKM)整合，建立雙向資訊收集模組與iKM分析引擎與生產優化模組，使可進一步優化加工程式，以避免加工異常的發生，其中整合多種線上加工監控模組(含:切削異常模組、精度指標模組、機台效能優化模組)，執行不同演算法分析以建議最佳的加工參數，同時也會回傳相關設計資料至iKM雲端資料庫，介面系統以Visual C#建立並串接整合所有功能模組及加工機台，收集模擬結果、加工參數與實際切削結果，持續更新iKM的專家資料庫，為未來設計優化的基礎，最後以實際加工驗證比較有經過優化模組與無經過優化模組兩者之差異性，例如以鋁材料之實驗，加工時間從2.6分鐘縮短至1.7分鐘，同時表面精度從0.97μm提升至0.49μm，證明此系統確實可達到精度與效能同步優化之成效。

This study is to develop a system that we can build an optimization of NC programs and predict abnormal processing at pre-process by modeling and algorithm. The system is also integrated with the Intelligent Knowledge Management (IKM) to build a two-way information gathering module and IKM analysis engine and production optimization module to further optimize machining programs to avoid machining anomalies, including the integration of a variety of in-line process control modules (Abnormal Monitoring Control module, Roughness module, Energy Consumption module.). A large of data collected from each module, and each module will use these data to perform different algorithms analysis. After that, the best NC processing will be obtained from the optimization module. Finally, the actual machining verification Compare the difference between optimized module and non-optimized module to prove the reliability of the system. For example, in the experiment of aluminum materials, the processing time was shortened from 2.6 minutes to 1.7 minutes while the surface accuracy was improved from 0.97μm to 0.49μm, which proves that the system can achieve the effect of simultaneous optimization of accuracy and efficiency.

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   連結：
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   連結：
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   連結：
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    連結：
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    連結：
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    連結：
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    連結：
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    連結：
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    連結：
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    連結：
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    連結：
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    連結：
27. [2] Jeongcheol Lee, Sungbum Jun, Tai-Woo Chang, and Jinwoo Park, “A Smartness Assessment Framework for SmartFactories Using Analytic Network Process”, Sustainability 2017, 9, 794.
    連結：
28. [3] Kyung-Joon Park, Jaemin Kim, Hyuk Lim, and Yongsoon Eun, “Robust Path Diversity for Network Quality of Servicein Cyber-Physical Systems”, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 4, NOVEMBER 2014.
    連結：
29. [4]Sergio F,Giancarlo Fortino,Giuseppe Di Fatta,”Cyber-physical systems, internet of things and big data”, Volume 75, October 2017, Pages 82-84.
    連結：
30. [5] Shiyong Yin, Jinsong Bao, Yiming Zhang, Xiaodi Huang, “M2M Security Technology of CPS Based on Blockchains”, Symmetry 2017, 9(9), 193; doi:10.3390/sym9090193
    連結：
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    連結：
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    連結：
33. [8] Xiuli, F., et al. Research on Predictive Model Surface Roughness in High Speed Milling for Aluminum Alloy 7050-T7451. in 2010 International Conference on Computing, Control and Industrial Engineering. 2010.
    連結：
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    連結：
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    連結：
36. [12] Hazza, M.H.F.A., et al. Cutting Temperature and Surface Roughness Optimization in CNC End Milling Using Multi Objective Genetic Algorithm. in 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT). 2012.
    連結：
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    連結：
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    連結：
39. [16] 陳傑，”工具機預估工件銑削品質可行性之研究”， 國立中正大學 機械工程系碩士論文，2014。
    連結：
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    連結：
41. [21] M. Mori, M. Fujishima, Y. Inamasu, Y. Oda, “A study on energy efficiency improvement for machine tools,” CIRP Annals -Manufacturing Technology, vol. 60, no. 1, 2011.
    連結：
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    連結：
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    連結：
44. [26] Zhehe Yao, Deqing Mei, Zichen Chen”On-line Chatter detection and identification based on wavelet and support vector machine”，Journal of Materials Processing Tech. Volume: 210, Issue: 5, March 19, 2010, pp. 713-719.
    連結：
45. [28] Panda, S.S., Singh, A.K., Chakraborty, D., Pal, S.K., “Drill wear monitoring using back propagation neural network”, Journal of Materials Processing Technology, pp.283-290, 2004.
    連結：
46. [30] 王貫宇, “複合指標加工參數優化及刀具臨界壽命監控智慧系統整合研究”, 私立中原大學機械工程研究所碩士論文,2016。
    連結：
47. [31] 陳楚河, “CNC工具機加工精度與節能同步優化設計”, 私立中原大學機械工程研究所碩士論文,2016。
    連結：
48. [32] 李軍逸, “CNC工具機產效暨與節能同步優化設計與空刀率之智能監測研究”, 私立中原大學機械工程研究所碩士論文,2016。
    連結：
49. [33] 蔡柏承, “新線上顫振監控系統與切削穩定圖輔佐抑制顫振之研究”, 私立中原大學機械工程研究所碩士論文, 2014。
    連結：
50. [34] 何建鐽, “CNC工具機線上切削異常智能監控與製程優化輔助系統之研究”, 私立中原大學機械工程研究所碩士論文, 2011。
    連結：
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