成功大學博碩士論文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	李明勳 Lee, Min-Hsun
論文名稱：	選擇性雷射熔融製程於不鏽鋼316L粉末之熔池與粉末顆粒噴濺監測系統研究 Study on melt pool and spatters inspection system for selective laser melting process with stainless steel 316L power
指導教授：	羅裕龍 Lo, Yu-Lung
學位類別：	碩士 Master
系所名稱：	工學院 - 機械工程學系 Department of Mechanical Engineering
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	76
中文關鍵詞：	積層製造、選擇性雷射熔融、不鏽鋼316L 、熔池形貌量測、粉末顆粒噴濺追蹤、近紅外線高速攝影
外文關鍵詞：	Additive Manufacture, Selective Laser Melting, SS316L, Melt Pool Geometry Measurement, Spatter Tracking, NIR High-Speed Imaging
相關次數：	點閱：205 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

本研究建立了相對低價之高速攝影系統用以觀察不鏽鋼316L金屬粉末於選擇性雷射熔融製程中的熔池與粉末顆粒噴濺現象，並探討了(1)熔池長度與寬度(2)製程中粉末顆粒噴濺數量(3)粉末顆粒噴濺體積與模擬之蒸發體積比較，三個主題。
高速攝系統安裝於熔池的側邊因此會產生視野效應，故使用了視角轉換之數學方法將影像從熔池側邊轉換成熔池上視圖，並使用轉換之影像計算空間解析度。熔池的邊界定義方式使用了二階微分來尋找物質的液體至固體的不連續點來定義。但是此不連續處因為雜訊關係無法並清楚識別。故此研究提出了辨識此不連續點之方法來量測熔池的長度與寬度。熔池長度量測之最大平均誤差為-15%，熔池寬度最大平均誤差則為24%。
粉末顆粒在雷射燒熔的過程中會以非常快速的速度從熔池噴濺而出。因此於特定期間內進行粉末顆粒追蹤對於量測粉末之顆粒粒徑分布為必要步驟。此追蹤方法基於卡爾曼濾波器開發而成。其結果顯示了當輸入之雷射能量密度越大，產生之粉末顆粒噴濺則越多。噴濺之粉末顆粒總體積也與熱傳模擬之蒸發區域進行了比較，其結果顯示噴濺之粉末顆粒體積小於模擬之蒸發區域。此結果與物理理論相悖並需要進一步的研究。

This research built up a relatively low cost lateral off-line high-speed imaging system to observe the melt pool and the spatter behavior in selective laser melting with stainless steel 316L powder. The (1) melt pool geometry including melt pool length and width (2) spatter size distribution (3) total spatter volume comparing to the evaporation volume over 3200K with the simulation, which is the evaporation point of SS316L material. These three objectives are studied.
The imaging system is constructed on the side of the melt pool and therefore the image taken from the camera exists an angle of view. Thus, the perspective transformation is applied to transform the image from the side view to the top view mathematically and the spatial resolution was calculated. The melt pool was recognized using the liquid to the solid transition point of the material and used the second derivative method to identify the transition point. However, the transition point in the image was not clear enough to be identified because of the noises. As a result, the automatically detect algorithm to identify the transition point to measure the melt pool length and width was developed. The monitored length showed the maximum average error 15% while the error of the measured width was 24%.
A large number of spatters eject from the melt pool with very high speed during the process. Therefore, it is necessary to perform spatter tracking so as to get the spatter size distribution in a certain period. The tracking algorithm based on Kalman filter was developed. The result shows that the higher energy density, the more spatters generate. The volume of the spatter was also compared to the evaporation zone in the simulation. The result showed that the spatter volume was smaller than the evaporation volume in the simulation. This result should be further studied because of the contrary to physics.

Abstract	I
中文摘要	III
誌謝	V
Table of Contents	VII
List of Figures	IX
List of Tables	XIII
Chapter 1	Introduction	1
1.1	Preface	1
1.2	Literature review	3
1.3	Research motivation and objectives	7
1.4	Thesis overview	7
Chapter 2	Theory and Method	9
2.1	Melt pool shape extraction algorithm	9
2.1.1	Review of Plank’s Law	9
2.1.2	Camera lens distortion calibration	10
2.1.3	Perspective transformation and spatial resolution calibration	……………………………………………………………..15
2.1.4	Definition of the melt pool in the image	17
2.2	Spatter tracking algorithm	35
2.2.1	Blob analysis	36
2.2.2	Kalman filter	38
Chapter 3	Experimental Setup	40
3.1	Selective laser melting machine	40
3.2	Lateral imaging system set-up	41
3.2.1	High-speed camera set-up	41
3.2.2	Selection of the optical filter	43
3.3	Experiment parameters and arrangement	46
Chapter 4	Experimental Results	47
4.1	Melt pool geometry extraction	47
4.1.1	Camera lens distortion calibration	47
4.1.2	Perspective transformation and spatial resolution calculation	……………………………………………………………..48
4.1.3	Result for measuring melt pool length and width	49
4.2	Spatter analysis	68
Chapter 5	Conclusions and Future Works	71
References	73

 
List of Figures
Figure 1 Illustration of selective laser melting process[1].	1
Figure 2 The spatter size distribution for original metal powder and the spatter particles. The average size of the original spatters is 32um while the average size of the spatter particles is 162um.	6
Figure 3 Illustration of Plank’s law at four different temperatures.	10
Figure 4 Radial distortion(a)undistorted image(b) barrel distortion(c) pincushion distortion[17].	11
Figure 5 The non-parallel problem between the lens and the sensor.	11
Figure 6 Illustration for a pin-hole camera.	14
Figure 7 Pin-hole model with lens distortion.	14
Figure 8 The transformation of coordinates from the image plane to the work plane.	17
Figure 9 Typical image captured by the high-speed camera.	18
Figure 10 (a)attached spatter captured by high-speed camera(b) attached spatter[11] (c)vapor plume[5].	19
Figure 11 The derivative algorithm used to detect the discontinuity at the liquidus-solidus transition stage[5].	21
Figure 12 The intensity along the center of the melt pool and the corresponding first order derivative and second order derivative.	21
Figure 13 Flow chart for calculating the melt pool length	23
Figure 14 Horizontal projection to the right and the intensity distribution along the red line for P=300W and V=600mm/s	24
Figure 15 Take the absolute value of data points vector and shift it to the center for the data points vector in Figure 14	25
Figure 16 Gaussian function with a=1,b=0, σ=10	26
Figure 17 Signal in Figure 15 after Gaussian low pass filter	26
Figure 18 The comparison of the raw data in Figure 14 below and the result after applying Gaussian low pass filter.	27
Figure 19 The blurred part of the image.	29
Figure 20 The separation of the spatter using different threshold values. (a) the original image (b) T=0.1 (c) T=0.2 (d)T=0.3 (d) T=0.4 (e) T=0.45.	31
Figure 21 (a)original image (b) the spatter after morphological dilation (c) the image after spatter segmentation((a)-(b)=(c)).	32
Figure 22 The fully attached spatter.	32
Figure 23 Method to define the threshold value for melt pool width using the melt pool length boundary.	33
Figure 24 Flow chart for calculating the melt pool length.	34
Figure 25 (a)two sequence images without spatter tracking(b) two sequence images with spatter tracking.	35
Figure 26 Illustration for blob analysis.	36
Figure 27 Flow chart of BLOB analysis.	37
Figure 28 Tongtai AM-250 SLM machine.	40
Figure 29 The lateral image acquisition system.	42
Figure 30 Melt pool images compared using different filters with exposure time 220μs.	45
Figure 31 The spectral response for Miro C110 high-speed camera [30].	45
Figure 32 The scheme for the experimental setup.	46
Figure 33 A 5mm by 7mm tiny checkerboard.	47
Figure 34 The checkboard after Harris corner detection.	48
Figure 35 The image before and after perspective transformation. (a) the square before transformation (b) the square after transformation.	49
Figure 36 The plot for the raw data, raw data after the Gaussian low pass filter, the first derivative and the second derivative. (a) 300W, V=600mm/s(b) 250W, V=600mm/s(c) 200W, V=600mm/s(d) 150W, V=600mm/s.	51
Figure 37 The smoothing data comparison using low pass filter and spline fitting at two different times (p=1.3×10-6).	53
Figure 38 the second derivative comparison using low pass filter and spline fitting at two different times (p=1.3×10-6).	54
Figure 39 All data points of measured length. (a) measured length for P=300W and V=600mm/s (b) measured length for P=250W and V=600mm/s (c) measured length for P=150W and V=600mm/s (d) measured length for P=150W and V=600mm/s.	56
Figure 40 The comparison between the extracted length and simulated length.	58
Figure 41 Curve overlap of the temperature simulation profile and the monitored intensity profile.	62
Figure 42 All data points of measured width.(a) measured width for P=300W and V=600mm/s (b) measured width for P=250W and V=600mm/s (c) measured width for P=150W and V=600mm/s (d) measured width for P=150W and V=600mm/s.	64
Figure 43 The comparison between the monitored width, simulated width and the measured width using a microscope.	66
Figure 44 The single-track width in the microscope. (a)150W 600mm/s(b)200W 600mm/s(c)250W 600mm/s (d) 300W 600mm/s.	68
Figure 45 Spatter size distribution between 4 processing parameters.	69
 
List of Tables
Table 1 The equipment and the monitoring object comparison.	5
Table 2 Camera configuration.	43
Table 3 Spectral range selected in the literature.	44
Table 4 Four processing parameters conducted in this study.	46
Table 5 The comparison of measured length and simulated length	57
Table 6 The comparison of measured width and simulated length.	65
Table 7 Spatter volume compared to the simulation evaporation zone.	70
                                    

[1] M. Mani, S. Feng, B. Lane, A. Donmez, S. Moylan, and R. Fesperman, Measurement science needs for real-time control of additive manufacturing powder bed fusion processes, National Institute of Standards and Technology, 2015.
[2] M. Pacher, L. Mazzoleni, L. Caprio, A. G. Demir, and B. Previtali, "Estimation of melt pool size by complementary use of external illumination and process emission in coaxial monitoring of selective laser melting," Journal of Laser Applications, vol. 31, no. 2, p. 022305, 2019.
[3] J. C. Fox, B. M. Lane, and H. Yeung, "Measurement of process dynamics through coaxially aligned high speed near-infrared imaging in laser powder bed fusion additive manufacturing", International Society for Optics and Photonics conference, 2017.
[4] 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.
[5] J. C. Heigel and B. M. Lane, "Measurement of the melt pool length during single scan tracks in a commercial laser powder bed fusion process", Journal of Manufacturing Science and Engineering, vol. 140, no. 5, p. 051012, 2018.
[6] B. Cheng, J. Lydon, K. Cooper, V. Cole, P. Northrop, and K. Chou, "Melt Pool Dimension Measurement In Selective Laser Melting Using Thermal Imaging", Solid Freeform Fabrication Symposium, 2017.
[7] L. Mazzoleni, A. G. Demir, L. Caprio, M. Pacher, and B. Previtali, "Real-Time Observation of Melt Pool in Selective Laser Melting: Spatial, Temporal and Wavelength Resolution Criteria", IEEE Transactions on Instrumentation and Measurement, 2019.
[8] C. S. L. Xin Wang, Douglas A. Bristow, Robert G. Landers, and Edward C. Kinzel, "Effects of Thermal Camera Resolution on Feature Extraction in Selective Laser Melting", Solid Freeform Fabrication Symposium, 2018.
[9] U. S. Bertoli, G. Guss, S. Wu, M. J. Matthews, and J. M. Schoenung, "In-situ characterization of laser-powder interaction and cooling rates through high-speed imaging of powder bed fusion additive manufacturing", Materials & Design, vol. 135, pp. 385-396, 2017.
[10] M. T. Andani, R. Dehghani, M. R. 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.
[11] L. E. Criales, Y. M. Arısoy, B. Lane, S. Moylan, A. Donmez, and T. Özel, "Laser powder bed fusion of nickel alloy 625: experimental investigations of effects of process parameters on melt pool size and shape with spatter analysis", International Journal of Machine Tools and Manufacture, vol. 121, pp. 22-36, 2017.
[12] D. Wang, S. Wu, F. Fu, S. Mai, Y. Yang, Y. Liu, and C. Song, "Mechanisms and characteristics of spatter generation in SLM processing and its effect on the properties", Materials & Design, vol. 117, pp. 121-130, 2017.
[13] M. Simonelli, C. Tuck, N. T. Aboulkhair, I. Maskery, I. Ashcroft, R. D. Wildman, and R. Hague, "A study on the laser spatter and the oxidation reactions during selective laser melting of 316L stainless steel, Al-Si10-Mg, and Ti-6Al-4V", Metallurgical and Materials Transactions A, vol. 46, no. 9, pp. 3842-3851, 2015.
[14] R. Dougal, "The presentation of the Planck radiation formula (tutorial)", Physics Education, vol. 11, no. 6, p. 438, 1976.
[15] Z. Zhang, "A flexible new technique for camera calibration", IEEE Transactions on pattern analysis and machine intelligence, vol. 22, pp. 1330-1334, 2000.
[16] J. Wang, F. Shi, J. Zhang, and Y. Liu, "A new calibration model of camera lens distortion", Pattern Recognition, vol. 41, no. 2, pp. 607-615, 2008.
[17] J. Park, S.-C. Byun, and B.-U. Lee, "Lens distortion correction using ideal image coordinates", IEEE Transactions on Consumer Electronics, vol. 55, no. 3, pp. 987-991, 2009.
[18] J. Heikkila and O. Silven, "A four-step camera calibration procedure with implicit image correction", IEEE Computer Society Conference, 1997.
[19] J. Weng, P. Cohen, and M. Herniou, "Camera calibration with distortion models and accuracy evaluation", IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 10, pp. 965-980, 1992.
[20] O. Chum, T. Pajdla, and P. Sturm, "The geometric error for homographies", Computer Vision and Image Understanding, vol. 97, no. 1, pp. 86-102, 2005.
[21] M. Asselin, E. Toyserkani, M. Iravani-Tabrizipour, and A. Khajepour, "Development of trinocular CCD-based optical detector for real-time monitoring of laser cladding", IEEE International Conference Mechatronics and Automation, 2005.
[22] J. C. Fox, B. M. Lane, and H. Yeung, "Measurement of process dynamics through coaxially aligned high speed near-infrared imaging in laser powder bed fusion additive manufacturing", International Society for Optics and Photonics conference, 2017.
[23] R. N. Bracewell and R. N. Bracewell, The Fourier transform and its applications, McGraw-Hill New York, 1986.
[24] T.-N. Le and Y.-L. Lo, "Effects of sulfur concentration and Marangoni convection on melt-pool formation in transition mode of selective laser melting process", Materials & Design, vol. 179, p. 107866, 2019.
[25] R. C. Gonzalez and P. Wintz, Digital image processing (no. 13), Addison-Wesley, 2008, p. 451.
[26] T. B. Moeslund, Introduction to video and image processing: Building real systems and applications, Springer Science & Business Media, 2012.
[27] S.-K. Weng, C.-M. Kuo, and S.-K. Tu, "Video object tracking using adaptive Kalman filter", Journal of Visual Communication and Image Representation, vol. 17, no. 6, pp. 1190-1208, 2006.
[28] S. Clijsters, T. Craeghs, S. Buls, K. Kempen, and J.-P. Kruth, "In situ quality control of the selective laser melting process using a high-speed, real-time melt pool monitoring system", The International Journal of Advanced Manufacturing Technology, vol. 75, no. 5-8, pp. 1089-1101, 2014.
[29] G. Tapia and A. Elwany, "A review on process monitoring and control in metal-based additive manufacturing", Journal of Manufacturing Science and Engineering, vol. 136, no. 6, p. 060801, 2014.
[30] T. Kolb, P. Gebhardt, O. Schmidt, J. Tremel, and M. Schmidt, "Melt pool monitoring for laser beam melting of metals: assistance for material qualification for the stainless steel 1.4057", Procedia CIRP, vol. 74, pp. 116-121, 2018.
[31] Vision Research. The spectral response curve for Miro C110C210. Available: https://www.phantomhighspeed.com/products/cameras/mirocnn/c110
[32] C. G. Harris and M. Stephens, "A combined corner and edge detector", presented at the Alvey vision conference, 1988.
[33] M. F. Hutchinson and F. De Hoog, "Smoothing noisy data with spline functions", Numerische Mathematik, vol. 47, no. 1, pp. 99-106, 1985.

校內：2024-08-29公開
校外：2024-08-29公開

簡易檢索 / 詳目顯示

相關論文