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研究生:戴萬源
研究生(外文):Wan-Yuan Tai
論文名稱:H型鋼構件智慧塗裝路徑優化研究
論文名稱(外文):The research of H-shaped steel intelligent coating
指導教授:陳介豪陳介豪引用關係
指導教授(外文):Jieh-Haur Chen
學位類別:碩士
校院名稱:國立中央大學
系所名稱:土木系營建管理碩士在職專班
學門:商業及管理學門
學類:其他商業及管理學類
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:68
中文關鍵詞:H型鋼構件鋼構件塗裝塗裝優化路徑路徑規劃
外文關鍵詞:H-steel componentcoating pathoptimizationrecognitionconstruction industry
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由於工廠的生產塗裝H型鋼結構部件的配置複雜性和尺寸因素,H型鋼結構部件的塗層工作通常由人工完成。 H型鋼結構件非常巨大且複雜,這是塗裝廠需要面對的生產率較低的問題,特別是對於行業中高度以客戶為導向的需求。研究目標是為H型鋼部件開發最佳的塗層路徑。文獻綜述提出了用於設置參數,識別構型和優化H型鋼組件塗層路徑的方法。通過針對大多數H型鋼部件的定義數據庫,構建了該算法以高精度(> 90%)識別鋼結構部件,然後進行塗層處理。與手動塗層相比,本研究模擬了100種不同的H型鋼塗層情況。研究結果表明,平均節省時間可達到總手動塗佈時間的24.6%。這是非常重要的,因為該行業正面臨熟練工人的短缺和高塗料成本,這兩者都給從業人員帶來了經營困難。
The coating work for H-steel components is usually carried out by manual due to factory capacity and H-steel components’ configuration complexity and size. The larger and more complex the H-steel components are the lower productivity a coating factory needs to face, especially for highly customer-oriented demands from the industries. The research objective is to develop optimal coating paths for H-steel components. The literature review suggests the methods setting parameters, recognizing configuration and optimizing coating paths for H-steel components. By a given database for most H-steel components, the algorithm is built to recognize the components with a high accuracy (>90%) and then to deal with coating. The evaluation simulates 100 different cases of H-steel coating, compared to manual coating. Findings turn out that the average time saving can reach 24.6% of the total manual coating time. It is significant since the industry is facing the shortage of skillful workers and high coating costs which both have caused difficult for practitioners to run their business.
目錄
中文摘要 i
ABSTRACT ii
目錄 iii
圖目錄 vi
表目錄 viii
第一章 緒論 1
1.1研究背景與動機 1
1.3研究目的 2
1.4研究範圍 2
1.5研究流程 2
第二章 文獻回顧 6
2.1 模式識別 6
2.1.1 模式識別之應用 6
2.1.2 於鋼構件之相關應用 8
2.2 最佳化路徑 8
2.2.1 最佳路徑規劃與產生的執行流程 9
2.2.2 塗裝路徑規劃中需考量的因素 9
2.2.2 相關塗裝路徑規劃的研究 10
2.3 鋼構件塗裝 11
第三章 塗裝優化路徑規劃 13
3.1 塗裝路徑研究設定 13
3.2 塗裝優化路徑的規劃流程 13
3.3路徑基礎型式確立 14
3.4塗裝參數考量 14
第四章 塗裝優化路徑 20
4.1 塗裝優化路徑的建制流程 20
4.2 H型鋼構件尺寸參數獲取 20
4.3 路徑建制 21
4.4塗裝優化路徑規劃與建制的成果 22
4.5成果驗證 24
4.5.1塗裝優化路徑的正確性及適用性驗證 24
4.5.2塗裝路徑優化之效果驗證 35
4.5.3以H型鋼構公規分析塗裝路徑優化之效果驗證 38
4.6成果分析與討論 42
第五章 結論與建議 51
5.1 結論 51
5.2 研究建議 52
參考文獻 53
附錄A 56
H型鋼構件斷面的詳細圖説 56
1. Huzurbazar, S., D. Kuang, and L. Lee, Landmark-based algorithms for group average and pattern recognition. Pattern Recognition, 2019. 86: p. 172-187.
2. G, M., S. Elizabeth, and S. Mathew Koshy, Circular mesh-based shape and margin descriptor for object detection. Pattern Recognition, 2018. 84: p. 97-111.
3. Clément, M., C. Kurtz, and L. Wendling, Learning spatial relations and shapes for structural object description and scene recognition. Pattern Recognition, 2018. 84: p. 197-210.
4. Nandhakumar, N. and J.K. Aggarwal, The artificial intelligence approach to pattern recognition—a perspective and an overview. Pattern Recognition, 1985. 18(6): p. 383-389.
5. Vento, M., A long trip in the charming world of graphs for Pattern Recognition. Pattern Recognition, 2015. 48(2): p. 291-301.
6. Pathirage, C.S.N., et al., Structural damage identification based on autoencoder neural networks and deep learning. Engineering Structures, 2018. 172: p. 13-28.
7. Chen, P.-H. and L.-M. Chang, Artificial intelligence application to bridge painting assessment. Automation in Construction, 2003. 12(4): p. 431-445.
8. Armingol, J.M., et al., Statistical Pattern Modeling in Vision-Based Quality Control Systems. Journal of Intelligent and Robotic Systems, 2003. 37(3): p. 321-336.
9. Balsamo, L., R. Betti, and H. Beigi, A structural health monitoring strategy using cepstral features. Journal of Sound and Vibration, 2014. 333(19): p. 4526-4542.
10. Babič, M., et al., A novel method for statistical pattern recognition using the network theory and a new hybrid system of machine learning. Materiali in tehnologije, 2019. 53(1): p. 95-100.
11. Michaelsen, E. and J. Meidow, Stochastic reasoning for structural pattern recognition: An example from image-based UAV navigation. Pattern Recognition, 2014. 47(8): p. 2732-2744.
12. Li, Q.-X., et al., Intelligent and automatic laser frequency locking system using pattern recognition technology. Optics and Lasers in Engineering, 2020. 126.
13. Wallhäußer, E., et al., On the usage of acoustic properties combined with an artificial neural network – A new approach of determining presence of dairy fouling. Journal of Food Engineering, 2011. 103(4): p. 449-456.
14. Balsamo, L. and R. Betti, Data-based structural health monitoring using small training data sets. Structural Control and Health Monitoring, 2015. 22(10): p. 1240-1264.
15. Kumar, A., et al., A big data MapReduce framework for fault diagnosis in cloud-based manufacturing. International Journal of Production Research, 2016. 54(23): p. 7060-7073.
16. Geisler, T. and A. Kolb, Pattern recognition of rough surfaces by using goniometric scattered light. Metrology and Measurement Systems, 2018. 25: p. 33-46.
17. Kempf, R. and J. Adamy, Sequential pattern recognition employing recurrent fuzzy systems. Fuzzy Sets and Systems, 2004. 146(3): p. 451-472.
18. Soualhi, M., K.T.P. Nguyen, and K. Medjaher, Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing. Mechanical Systems and Signal Processing, 2020. 142.
19. Gasparetto, A., et al., Automatic Path and Trajectory Planning for Robotic Spray Painting. 2012. 1-6.
20. Bai, Y., Intelligent Painting Process Planner for Robotic Bridge Painting. Journal of Construction Engineering and Management, 2007. 133(4): p. 335-342.
21. Qiao, L., A. Esmaeily, and H.G. Melhem, Signal Pattern Recognition for Damage Diagnosis in Structures. Computer-Aided Civil and Infrastructure Engineering, 2012. 27(9): p. 699-710.
22. Lu, W., et al., Stress Prediction for Distributed Structural Health Monitoring Using Existing Measurements and Pattern Recognition. Sensors (Basel), 2018. 18(2).
23. Ma, X., et al., Automatic detection of steel rebar in bridge decks from ground penetrating radar data. Journal of Applied Geophysics, 2018. 158: p. 93-102.
24. Jingzhong, H., et al., Strip Steel Surface Defects Recognition Based on SOCP Optimized Multiple Kernel RVM. Mathematical Problems in Engineering, 2018. 2018: p. 1-8.
25. Huang, X., et al. Automatic feature extraction and optimal path planning for robotic drawing. in 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). 2016.
26. Chen, W., et al., Automatic Spray Trajectory Optimization on Bézier Surface. Electronics, 2019. 8(2).
27. Suh, S.-H., I.-K. Woo, and S.-K. Noh, Automatic trajectory planning system (ATPS) for spray painting robots. Journal of Manufacturing Systems, 1991. 10(5): p. 396-406.
28. Xi, N., W. Sheng, and Y. Chen, General Framework of Optimal Tool Trajectory Planning for Free-Form Surfaces in Surface Manufacturing. Journal of Manufacturing Science and Engineering-transactions of The Asme - J MANUF SCI ENG, 2005. 127.
29. Heping, C., X. Ning, and C. Yifan. Multi-objective optimal robot path planning in manufacturing. in Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453). 2003.
30. Diao, X.D., S.X. Zeng, and V.W.Y. Tam, Development of an optimal trajectory model for spray painting on a free surface. Computers & Industrial Engineering, 2009. 57(1): p. 209-216.
31. Gasparetto, A., et al., Optimal Path Planning for Painting Robots. 2010. p. 601-608.
32. Andulkar, M.V. and S.S. Chiddarwar, Incremental approach for trajectory generation of spray painting robot. Industrial Robot: An International Journal, 2015. 42(3): p. 228-241.
33. Chen, W., et al., Optimized Combination of Spray Painting Trajectory on 3D Entities. Electronics, 2019. 8(1).
34. Chen, W. and D. Zhao, Path Planning for Spray Painting Robot of Workpiece Surfaces. Mathematical Problems in Engineering, 2013. 2013: p. 1-6.
35. Tang, Y., W.G. Yang, and W. Chen, Trajectory Planning for Spray Painting Robot of Free-Form Surfaces. Applied Mechanics and Materials, 2014. 543-547: p. 1309-1312.
36. Guan, L. and L. Chen, Trajectory planning method based on transitional segment optimization of spray painting robot on complex-free surface. Industrial Robot: the international journal of robotics research and application, 2019. 46(1): p. 31-43.
37. Trigatti, G., et al., A Look-Ahead Trajectory Planning Algorithm for Spray Painting Robots with Non-spherical Wrists, in Mechanism Design for Robotics. 2019. p. 235-242.
38. Trigatti, G., et al., A new path-constrained trajectory planning strategy for spray painting robots - rev.1. The International Journal of Advanced Manufacturing Technology, 2018. 98(9-12): p. 2287-2296.
39. Liu, Y., J. Teng, and X. Zhang, Optimal Trajectory Planning of Spray Manipulator for Furniture Surface Painting. Chinese Control and Decision Conference, 2008, CCDC 2008, 2008.
40. Chen, H., et al., OPTIMAL TOOL TRAJECTORY INTEGRATION IN SURFACE MANUFACTURING. IFAC Proceedings Volumes, 2005. 38(1): p. 211-216.
41. Park, K. and D. Jeon, Optimization of Tool Path Pitch of Spray Painting Robots for Automotive Painting Quality. International Journal of Control, Automation and Systems, 2018. 16(6): p. 2832-2838.
42. Bruzl, M., et al., Optimizing the Trajectory of the Painting Robot. 2016.
43. Chen, W., et al., Trajectory Optimization of Electrostatic Spray Painting Robots on Curved Surface. Coatings, 2017. 7(10).
44. Duncan, S., P. Jones, and P. Wellstead, A Frequency-Domain Approach to Determining the Path Separation for Spray Coating. IEEE Transactions on Automation Science and Engineering, 2005. 2(3): p. 233-239.
45. Girbacia, T., F. Girbacia, and G. Mogan, Virtual Planning of Robot Trajectories for Spray Painting Applications. Applied Mechanics and Materials, 2014. 658: p. 632-637.
46. P, P.V. and M. Al-Maadeed, Self-Repairing Composites for Corrosion Protection: A Review on Recent Strategies and Evaluation Methods. Materials (Basel), 2019. 12(17).
47. Nauzin, J.P., H. Jacobs, and F. Onno, Paint Finish in Automotive Bodies. SAE Technical Papers, 2002.
48. Anwo, A.B., et al., Efficacy of Corrosion phenomena, challenges and control in steel industry: An overview. Journal of Physics: Conference Series, 2019. 1378.
49. Mills, D.J. and S.S. Jamali, The best tests for anti-corrosive paints. And why: A personal viewpoint. Progress in Organic Coatings, 2017. 102: p. 8-17.
50. Lyon, S.B., R. Bingham, and D.J. Mills, Advances in corrosion protection by organic coatings: What we know and what we would like to know. Progress in Organic Coatings, 2017. 102: p. 2-7.
51. Itoh, Y., et al., Anticorrosive performance of repair painting as remedy for deterioration in metallised steel. Corrosion Engineering, Science and Technology, 2013. 48(7): p. 537-551.
52. Bonner, P.E. and J.F. Stanners, Protection of steel by metal spraying: A review. British Corrosion Journal, 2013. 1(9): p. 339-343.
53. El Sarraf, R. and H. Barker, Protecting the Auckland Harbour Bridge, New Zealand – developing a 40-year coatings maintenance plan. Proceedings of the Institution of Civil Engineers - Bridge Engineering, 2019. 172(4): p. 281-292.
54. van Westing, E.P.M., G.M. Ferrari, and J.H.W. de Wit, The determination of coating performance with impedance measurements—II. Water uptake of coatings. Corrosion Science, 1994. 36(6): p. 957-977.
55. Maeda, S., Steel surface chemistry affecting the performance of organic coatings. Progress in Organic Coatings, 1983. 11(1): p. 1-18.
56. Jamali, S.S. and D.J. Mills, Steel surface preparation prior to painting and its impact on protective performance of organic coating. Progress in Organic Coatings, 2014. 77(12): p. 2091-2099.
57. 研究生:蕭嘉文Path Planning for 5-DOF Robot in Electrostatic Powder Painting, 2012.730:p.22-23
58. 研究生:王秀姿Path Planning for 4-DOF Robot in Transfer Line Electrostatic Powder Painting g, 2012.730:p.09-15
59. 研究生:黃聖坤Automatic Process Planning for Electrostatic Powder Painting Using VR-based Simulator,2011.722:p.22-23
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