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研究生: 郭宗翰
Kuo, Tsung-Han
論文名稱: 開發雷射粉床熔融機台之回授控制迴路
Development of Feedback Control Loops for the Laser Powder Bed Fusion Machine
指導教授: 鄭芳田
Cheng, Fan-Tien
共同指導教授: 楊浩青
Yang, Haw-Ching
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 製造資訊與系統研究所
Institute of Manufacturing Information and Systems
論文出版年: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 67
中文關鍵詞: 積層製造風場補償鋪粉補償拋光補償全自動虛擬量測智慧積層製造量測
外文關鍵詞: Additive Manufacturing, Flowing Compensation, Spreading Compensation, Polishing Compensation, Automatic Virtual Metrology (AVM), Intelligent Additive Manufacturing Metrology
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  • 金屬積層製造生產過程中,使用相同製程參數,以目前開迴路製程的機台而言,其所產製的品質也會有所差異,例如當風場穩定度、鋪粉均勻度、與粉粒尺寸和積層過程有所變異時,上述製程變異將導致的成品瑕疵,將造成時間與材料的浪費。因此,如何對此種機台進行上述的製程變異偵測與補償,並達到即時控制的目標,為本研究的挑戰。
    本研究針對開迴路粉床式金屬積層製造機台進行補償控制,包含風場、鋪粉、與拋光補償等三大閉迴路控制。在風場補償上,透過風場流速回授與控制,可在不擾動金屬粉層下有效排出腔體內的煙霧顆粒。在鋪粉補償上,透過全域相機以光學檢測鋪粉均勻度之回授,以重鋪或補粉方式控制鋪粉品質。在拋光補償上,藉由同軸相機以即時檢測熔池變化,並應用全自動虛擬量測方法以估測列印品質之回授,並參考積層製造模擬之建議,逐層調整雷射參數以進行拋光補償,以控制逐層粗糙度的穩定度。
    研究結果顯示,當應用本研究所開發的閉迴路控制系統,相對開迴路而言,風場可減少92.5 %之變異,鋪粉均勻度可提改善37%,而列印品質包含粗糙度、密度、拉力等指標有提升。因此,本研究所開發的閉迴路補償功能,實可大幅改善製程變異所造成的成品瑕疵情況。

    When using a metal additive manufacturing (AM) machine with open-loop control, the built qualities of parts are varying while using the identical process conditions. Due to variations of air flowing stability, powder spreading homogeneity, powder-size uniformity, and building stability, time and materials costs will be increased when defects existed in finished parts. Hence, the challenge of this research is how to detect and compensate the variations to achieve the goal of real-time control of the AM process.
    This research aims at compensation control of an open-loop powder-bed metal AM machine with close-loop controls of air flowing, powder spreading, and part building compensation. In air flowing compensation, without disturbing powder layers, the discharge smoke and particles can be effectively exhausted from the chamber by using the air-speed feedback control. For spreading powder compensation, the feedback of spreading powder uniformity is optically detected through a global camera, and the spreading quality is controlled by means of spreading or re-filling powder. In part building compensation, the coaxial camera is used to detect the change of the molt-pool in real time, and the automatic virtual metrology system is used to estimate printing quality, and refer to recommendations of AM simulation to adjust the laser parameters layer by layer. Polishing compensation is performed to control the stability of roughness layer by layer.
    The research results show that when the close-loop control system developed by this research is applied, the flow rate variation can be reduced by 92.5%, and the homogeneity of powder spreading can be improved by 37%, and the printing quality includes indexes such as roughness, density, and tensile have been improved. Therefore, the close-loop compensation functions developed by this research can greatly improve defects of a built part caused by the process variations.

    摘要 I ABSTRACT II ACKNOWLEDGEMENTS III TABLE OF CONTENTS IV LIST OF TABLES VII LIST OF FIGURES VIII CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Motivation and Purpose 3 1.3 Organization 5 CHAPTER 2 LITERATURE REVIEW 6 2.1 The Focus Quality Item 6 2.2 The Effect of Roughness Compensation and Heat Treatment 7 2.3 In-situ Measurement 8 2.4 Melt Pool Image Characteristic 10 2.5 Quality Estimation 13 2.6 Control Strategy and Method 14 CHAPTER 3 CLOSE-LOOP COMPENSATION OF AM PROCESS 16 3.1 Function of Intelligent Additive Manufacturing Architecture 17 3.2 Close-loop Design for the LPBF Machine 19 3.3 Realization of Close-loop Control for AM Process 22 3.3.1 Interfaces among AM Machine and Devices 23 3.3.2 Flowing Compensation 24 3.3.3 Spreading Compensation 25 3.3.4 Polishing Compensation 27 CHAPTER 4 CASE STUDY OF CLOSE-LOOP COMPENSATION 28 4.1 Flowing Compensation (Case 1) 28 4.1.1 Threshold Setting for Flow Rate 28 4.1.2 Result of Flow Rate Compensation 30 4.1.3 Conclusion on Flow Rate Compensation 31 4.2 Spreading Compensation (Case 2) 32 4.2.1 Threshold Setting for Spreading Compensation 32 4.2.2 Result of Homogeneity Compensation 33 4.2.2.1 Spreading Defects Solved Automatically 34 4.2.2.2 Spreading Defects Solved Manually 34 4.2.2.3 Conclusion on Spreading Compensation 35 4.3 Polishing Compensation (Case 3) 36 4.3.1 Comparison of the Roughness and Density 36 4.3.1.1 Experiment Setup for Roughness and Density Comparison 36 4.3.1.2 Experimental Result of Roughness and Density Comparison 38 4.3.2 Comparison of the Tensile Strength 39 4.3.2.1 Experiment Setup for the Tensile Strength Comparison 39 4.3.2.2 Experimental Result of Tensile Strength Comparison 40 4.3.3 Conclusion on Polishing Compensation 42 4.4 Challenges of IAMA System Integration 43 CHAPTER 5 CONCLUSIONS AND FUTURE WORK 46 5.1 Conclusions 46 5.2 Future Work 47 REFERENCE 48 APPENDIX 51 A. PLC Signal Position and Definition 51 B. Signal Flow for Different Process 57

    [1] S.-H. Huang, P. Liu, A. Mokasdar and L. Hou, “Additive manufacturing and its societal impact: a literature review,” The International Journal of Advanced Manufacturing Technology, vol. 67, issue. 5-8, pp. 1191-1203, July 2013.
    [2] W. S. W. Harun, M. S. I. N. Kamariah, N. Muhamad, S. A. C. Ghani, F. Ahmad, and Z. Mohamed, “A review of powder additive manufacturing processes for metallic biomaterials,” Powder Technology, vol. 327, pp. 128–151, 2018.
    [3] Y. Kok, X. P. Tan, P. Wang, M. L. S. Nai, N. H. Loh, E. Liu, and S. B. Tor, “Anisotropy and heterogeneity of microstructure and mechanical properties in metal additive manufacturing: A critical review,” Materials & Design, vol. 139, pp. 565–586, 2018.
    [4] W. E. King, A. T. Anderson, R. M. Ferencz, N. E. Hodge, C. Kamath, S. A. Khairallah, and A. M. Rubenchik, “Laser powder-bed fusion additive manufacturing of metals; physics, computational, and materials challenges,” Additive Manufacturing Handbook, pp. 461–502, 2017.
    [5] W. E. Frazier, “Metal Additive Manufacturing: A Review,” Journal of Materials Engineering and Performance, vol. 23, pp.1917–1928, 2014.
    [6] W. Gao, Y. Zhang, D. Ramanujan, K. Ramani, Y. Chen, C. B. Williams, C. C. L. Wang, Y. C. Shin, S. Zhang, and P. D. Zavattieri, “The status, challenges, and future of additive manufacturing in engineering,” Computer-Aided Design, vol. 69, pp. 65–89, 2015.
    [7] H.-C. Yang, C.-H. Huang, M. Adnan, C.-H. Hsu, C.-H. Lin, and F.-T. Cheng, “An Online AM Quality Estimation Architecture- from Pool to Layer,” IEEE Transaction on Automation and Science, Early Access, 2020.
    [8] H.-C. Yang, M. Adnan, C.-H. Huang, F.-T. Cheng, Y.-L. Lo, and C.-H. Hsu, “An intelligent metrology architecture with AVM for metal additive manufacturing,” IEEE Robot. Autom. Lett., vol. 4, no. 3, pp. 2886–2893, 2019.
    [9] A. Bača, R. Konečná, G. Nicoletto, and L. Kunz, “Effect of Surface Roughness on the Fatigue Life of Laser Additive Manufactured Ti6Al4V Alloy,” Manufacturing Technology, vol. 15, no. 4, pp. 498–502, 2015.
    [10] L. Thijs, F. Verhaeghe, T. Craeghs, J. V. Humbeeck, and J.-P. Kruth, “A study of the microstructural evolution during selective laser melting of Ti–6Al–4V,” Acta Materialia, vol. 58, no. 9, pp. 3303–3312, 2010.
    [11] E. Yasa, J. Deckers, and J. P. Kruth, “The investigation of the influence of laser re‐melting on density, surface quality and microstructure of selective laser melting parts,” Rapid Prototyping Journal, vol. 17, no. 5, pp. 312–327, 2011.
    [12] X. Wang and K. Chou, “The effects of stress relieving heat treatment on the microstructure and residual stress of Inconel 718 fabricated by laser metal powder bed fusion additive manufacturing process,” Journal of Manufacturing Processes, vol. 48, pp. 154–163, 2019.
    [13] A. Temmler, D. Liu, J. Preußner, S. Oeser, J. Luo, R. Poprawe, and J. H. Schleifenbaum, “Influence of laser polishing on surface roughness and microstructural properties of the remelted surface boundary layer of tool steel H11,” Materials & Design, vol. 192, p. 108689, 2020.
    [14] S. K. Everton, M. Hirsch, P. Stravroulakis, R. K. Leach, and A. T. Clare, “Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing,” Materials & Design, vol. 95, pp. 431–445, 2016.
    [15] T. Craeghs, S. Clijsters, E. Yasa, F. Bechmann, S. Berumen, and J.-P. Kruth, “Determination of geometrical factors in Layerwise Laser Melting using optical process monitoring,” Optics and Lasers in Engineering, vol. 49, no. 12, pp. 1440–1446, 2011.
    [16] F. Imani, A. Gaikwad, M. Montazeri, P. Rao, H. Yang, and E. Reutzel, “Layerwise In-Process Quality Monitoring in Laser Powder Bed Fusion,” Volume 1: Additive Manufacturing; Bio and Sustainable Manufacturing, 2018.
    [17] D. Ye, K. Zhu, J. Y. Fuh, Y. Zhang, and H. G. Soon, “The investigation of plume and spatter signatures on melted states in selective laser melting,” Optics & Laser Technology, vol. 111, pp. 395–406, 2019.
    [18] Y. Zhang, G. S. Hong, D. Ye, K. Zhu, and J. Y. H. Fuh, “Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring,” Materials & Design, vol. 156, pp. 458–469, 2018.
    [19] M. Taheri Andani, R. Dehghani, M. Karamooz-Ravari, R. Mirzaeifar and J. Ni, "Spatter formation in selective laser melting process using multi-laser technology", Materials & Design, vol. 131, pp. 460-469, 2017
    [20] ] H.-C. Yang, H. Tieng, and F.-T. Cheng, “Automatic virtual metrology for wheel machining automation,” International Journal of Production Research, vol. 54, no. 21, pp. 6367–6377, 2015.
    [21] H. Tieng, T.-H. Tsai, C.-F. Chen, H.-C. Yang, J.-W. Huang, and F.-T. Cheng, “Automatic Virtual Metrology and Deformation Fusion Scheme for Engine-Case Manufacturing,” IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 934–941, 2018.
    [22] Y. Hsieh, C. Lin, Y. Yang, M. Hung and F. Cheng, “Automatic Virtual Metrology for Carbon Fiber Manufacturing,” IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2730-2737, July 2019.
    [23] L. Thijs, F. Verhaeghe, T. Craeghs, J. V. Humbeeck, and J.-P. Kruth, “A study of the microstructural evolution during selective laser melting of Ti–6Al–4V,” Acta Materialia, vol. 58, no. 9, pp. 3303–3312, 2010.
    [24] M. Vlasea, B. Lane, F. Lopez, S. Mekhontsev and A. Donmez, "DEVELOPMENT OF POWDER BED FUSION ADDITIVE MANUFACTURING TEST BED FOR ENHANCED REAL-TIME PROCESS CONTROL", in Proceedings of the international solid freeform fabrication symposium, Austin, Texas,USA, 2015.
    [25] A.-min Li, C.-hui Zhang, H.-lin Li, Z.-yang Xu, X.-hui Chen, G.-le Qin, and S.-wei Ye, “Design of Automatic Welding Machine Based on PLC,” 2011 Fourth International Conference on Intelligent Computation Technology and Automation, 2011.
    [26] F. Jinbing, “Design of the Control System for Automatic Riveting Machine Based on PLC,” 2017 International Conference on Robots & Intelligent System (ICRIS), 2017.
    [27] B. Lane et al., "DESIGN, DEVELOPMENTS, AND RESULTS FROM THE NIST ADDITIVE MANUFACTURING METROLOGY TESTBED (AMMT)", in Solid Freeform Fabrication (SFF) Symposium, Austin, Texas, USA, 2016, pp. 1145-1160
    [28] H. Giberti, L. Sbaglia, and M. Silvestri, “Mechatronic Design for an Extrusion-Based Additive Manufacturing Machine,” Machines, vol. 5, no. 4, p. 29, 2017
    [29] R. M. Haralick, K. Shanmugam and I. Dinstein, "Textural features for image classification", IEEE Trans. Syst. Man Cybern., vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
    [30] D. Gadkari, "Image quality analysis using GLCM", 2004.

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