Chapter 6. Conclusion and Future Works
6.2 Future Works
Many studies for deep learning applications to the industries have been widely conducted in deep learning framework for image recognition. High operation speed and low computational cost are essential to apply AI to the industries, but normally deep learning models have an inverse relationship between accuracy and speed. Reflecting this, many deep learning architectures have recently been developed that can be employed to the industry through efficient model structures and fast computational speed. In this regard, we look forward to applying the latest state-of-the-art models to improve the proposed monitoring system faster and more accurately under the low computation cost.
The proposed monitoring model can only be applied in full-penetration laser keyhole welding.
However, we would like to develop a monitoring system using only top surface of welding process since partial penetration laser welding is used in many manufacturing fields such as packaging components. Furthermore, laser dissimilar metal welding such as welding of Al alloy and copper is also being employed in many industrial fields, and we expect to apply the proposed monitoring system to analyze the dissimilar metal welding process and detect weld defects.
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References
[1] E. Assuncao, S. Williams, D. J. O. Yapp, and L. i. Engineering, "Interaction time and beam diameter effects on the conduction mode limit," vol. 50, no. 6, pp. 823-828, 2012.
[2] H. Ki, P. S. Mohanty, and J. Mazumder, "Modeling of laser keyhole welding: Part I.
Mathematical modeling, numerical methodology, role of recoil pressure, multiple reflections, and free surface evolution," (in English), Metall Mater Trans A, vol. 33, no. 6, pp. 1817-1830, Jun 2002, doi: DOI 10.1007/s11661-002-0190-6.
[3] H. Ki, P. S. Mohanty, and J. Mazumder, "Modeling of laser keyhole welding: Part II. Simulation of keyhole evolution, velocity, temperature profile, and experimental verification," (in English), Metall Mater Trans A, vol. 33, no. 6, pp. 1831-1842, Jun 2002, doi: DOI 10.1007/s11661-002- 0191-5.
[4] E. H. Amara and A. Bendib, "Modelling of vapour flow in deep penetration laser welding," (in English), J Phys D Appl Phys, vol. 35, no. 3, pp. 272-280, Feb 7 2002, doi: Pii S0022- 3727(02)28665-9
Doi 10.1088/0022-3727/35/3/317.
[5] M. Sohail, S.-W. Han, S.-J. Na, A. Gumenyuk, and M. Rethmeier, "Numerical investigation of energy input characteristics for high-power fiber laser welding at different positions," The International Journal of Advanced Manufacturing Technology, vol. 80, no. 5-8, pp. 931-946, 2015.
[6] J.-H. Cho and S.-J. Na, "Implementation of real-time multiple reflection and Fresnel absorption of laser beam in keyhole," Journal of Physics D: Applied Physics, vol. 39, no. 24, p. 5372, 2006.
[7] L. Zhang, J. Zhang, and S. Gong, "Mechanism study on the effects of side assisting gas velocity during CO 2 laser welding process," Journal of Applied Physics, vol. 106, no. 2, p. 024912, 2009.
[8] G. Xu, C. S. Wu, G. Qin, X. Wang, and S. Lin, "Adaptive volumetric heat source models for laser beam and laser+ pulsed GMAW hybrid welding processes," The International Journal of Advanced Manufacturing Technology, vol. 57, no. 1-4, pp. 245-255, 2011.
[9] S.-W. Han, J. Ahn, and S.-J. Na, "A study on ray tracing method for CFD simulations of laser keyhole welding: progressive search method," Welding in the World, vol. 60, no. 2, pp. 247- 258, 2016.
[10] H. Ki, P. S. Mohanty, and J. Mazumder, "Multiple Reflection and its Influence on Keyhole Evolution," J. Laser Appl., vol. 14, no. 1, pp. 39-45, FEB 2002. [Online]. Available: <Go to ISI>://000174256800009.
[11] K. J. I. T. o. a. Yee and propagation, "Numerical solution of initial boundary value problems involving Maxwell's equations in isotropic media," vol. 14, no. 3, pp. 302-307, 1966.
[12] C. Deng, J. Kim, S. Oh, and H. Ki, "Electrodynamic simulation of energy absorption in laser keyhole welding of zinc-coated and uncoated steel sheets," Journal of Materials Processing Technology, vol. 231, pp. 412-421, 2016, doi: 10.1016/j.jmatprotec.2016.01.011.
[13] S. Li, G. Chen, S. Katayama, and Y. J. A. S. S. Zhang, "Relationship between spatter formation and dynamic molten pool during high-power deep-penetration laser welding," vol. 303, pp.
481-488, 2014.
[14] J. Peng, L. Li, S. Lin, F. Zhang, Q. Pan, and S. J. M. P. i. E. Katayama, "High-speed x-ray transmission and numerical study of melt flows inside the molten pool during laser welding of aluminum alloy," vol. 2016, 2016.
[15] A. Matsunawa, J.-D. Kim, N. Seto, M. Mizutani, and S. J. J. o. l. a. Katayama, "Dynamics of keyhole and molten pool in laser welding," vol. 10, no. 6, pp. 247-254, 1998.
[16] A. Matsunawa, J.-D. Kim, N. Seto, M. Mizutani, and S. Katayama, "Dynamics of keyhole and molten pool in laser welding," J. Laser Appl., vol. 10, no. 6, pp. 247-254, 1998, doi:
10.2351/1.521858.
[17] M. Zhang, G. Chen, Y. Zhou, and S. J. O. e. Li, "Direct observation of keyhole characteristics in deep penetration laser welding with a 10 kW fiber laser," vol. 21, no. 17, pp. 19997-20004,
97 2013.
[18] Y. Zhang, G. Chen, H. Wei, J. J. O. Zhang, and L. i. Engineering, "A novel “sandwich” method for observation of the keyhole in deep penetration laser welding," vol. 46, no. 2, pp. 133-139, 2008.
[19] M. Zhang, G. Chen, Y. Zhou, S. Li, and H. J. A. S. S. Deng, "Observation of spatter formation mechanisms in high-power fiber laser welding of thick plate," vol. 280, pp. 868-875, 2013.
[20] M. J. Zhang, G. Y. Chen, Y. Zhou, S. C. Li, and H. Deng, "Observation of spatter formation mechanisms in high-power fiber laser welding of thick plate," Applied Surface Science, vol.
280, pp. 868-875, 2013, doi: 10.1016/j.apsusc.2013.05.081.
[21] R. Fabbro, F. Coste, D. Goebels, and M. J. J. o. p. D. A. p. Kielwasser, "Study of CW Nd-Yag laser welding of Zn-coated steel sheets," vol. 39, no. 2, p. 401, 2006.
[22] C.-H. Kim, D.-C. J. O. Ahn, and L. Technology, "Coaxial monitoring of keyhole during Yb:
YAG laser welding," vol. 44, no. 6, pp. 1874-1880, 2012.
[23] J. Kim, S. Oh, and H. Ki, "A study of keyhole geometry in laser welding of zinc-coated and uncoated steels using a coaxial observation method," (in English), Journal of Materials Processing Technology, vol. 225, pp. 451-462, Nov 2015. [Online]. Available: <Go to ISI>://WOS:000359171100047.
[24] L. J. Zhang, J. X. Zhang, A. Gumenyuk, M. Rethmeier, and S. J. Na, "Numerical simulation of full penetration laser welding of thick steel plate with high power high brightness laser,"
Journal of Materials Processing Technology, vol. 214, no. 8, pp. 1710-1720, 2014, doi:
10.1016/j.jmatprotec.2014.03.016.
[25] L. Cao, L. Zhang, and Y. Wu, "A data-driven model for weld bead monitoring during the laser welding assisted by magnetic field," The International Journal of Advanced Manufacturing Technology, vol. 107, no. 1-2, pp. 475-487, 2020, doi: 10.1007/s00170-020-05028-z.
[26] D. Wu et al., "Weld penetration in situ prediction from keyhole dynamic behavior under time- varying VPPAW pools via the OS-ELM model," The International Journal of Advanced Manufacturing Technology, vol. 104, no. 9-12, pp. 3929-3941, 2019, doi: 10.1007/s00170-019- 04142-x.
[27] C. Knaak et al., "Deep learning-based semantic segmentation for in-process monitoring in laser welding applications," presented at the Applications of Machine Learning, 2019.
[28] D. Poole, A. Mackworth, and R. Goebel, "Computational Intelligence," 1998.
[29] X. Xiong and F. De la Torre, "Supervised descent method and its applications to face alignment," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 532-539.
[30] N. Neshov and A. Manolova, "Pain detection from facial characteristics using supervised descent method," in 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2015, vol. 1: IEEE, pp. 251-256.
[31] Y. Cheng, "Supervised descent method based on appearance and shape for face alignment," in 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), 2016: IEEE, pp. 184-189.
[32] I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep learning (no. 2). MIT press Cambridge, 2016.
[33] A. Voulodimos, N. Doulamis, A. Doulamis, E. J. C. i. Protopapadakis, and neuroscience, "Deep learning for computer vision: A brief review," vol. 2018, 2018.
[34] J. Wang, Y. Ma, L. Zhang, R. X. Gao, and D. J. J. o. M. S. Wu, "Deep learning for smart manufacturing: Methods and applications," vol. 48, pp. 144-156, 2018.
[35] A. Shrestha and A. J. I. A. Mahmood, "Review of deep learning algorithms and architectures,"
vol. 7, pp. 53040-53065, 2019.
[36] T. Hoeser and C. J. R. S. Kuenzer, "Object detection and image segmentation with deep learning on Earth observation data: A review-part I: Evolution and recent trends," vol. 12, no. 10, p.
1667, 2020.
[37] X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun, "Monocular 3d object detection
98
for autonomous driving," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2147-2156.
[38] X. Wang, Y. Li, H. Zhang, and Y. J. a. p. a. Shan, "Towards Real-World Blind Face Restoration with Generative Facial Prior," 2021.
[39] L. Liu et al., "Deep learning for generic object detection: A survey," vol. 128, no. 2, pp. 261- 318, 2020.
[40] X. Kang, B. Song, and F. J. A. S. Sun, "A deep similarity metric method based on incomplete data for traffic anomaly detection in IoT," vol. 9, no. 1, p. 135, 2019.
[41] Y.-L. Boureau, J. Ponce, and Y. LeCun, "A theoretical analysis of feature pooling in visual recognition," in Proceedings of the 27th international conference on machine learning (ICML- 10), 2010, pp. 111-118.
[42] C. Nwankpa, W. Ijomah, A. Gachagan, and S. J. a. p. a. Marshall, "Activation functions:
Comparison of trends in practice and research for deep learning," 2018.
[43] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," (in English), P Ieee, vol. 86, no. 11, pp. 2278-2324, Nov 1998, doi: Doi 10.1109/5.726791.
[44] A. Krizhevsky, I. Sutskever, and G. E. J. A. i. n. i. p. s. Hinton, "Imagenet classification with deep convolutional neural networks," vol. 25, pp. 1097-1105, 2012.
[45] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE conference on computer vision and pattern recognition, 2009: Ieee, pp. 248-255.
[46] S. Dodge and L. Karam, "A study and comparison of human and deep learning recognition performance under visual distortions," in 2017 26th international conference on computer communication and networks (ICCCN), 2017: IEEE, pp. 1-7.
[47] M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," in European conference on computer vision, 2014: Springer, pp. 818-833.
[48] C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9.
[49] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770- 778.
[50] S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, "Aggregated residual transformations for deep neural networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1492-1500.
[51] J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132-7141.
[52] L. Jiao et al., "A survey of deep learning-based object detection," vol. 7, pp. 128837-128868, 2019.
[53] R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 580-587.
[54] S. Ren, K. He, R. Girshick, and J. J. a. p. a. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," 2015.
[55] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," p. arXiv:1506.02640. [Online]. Available:
https://ui.adsabs.harvard.edu/abs/2015arXiv150602640R
[56] W. Liu et al., "Ssd: Single shot multibox detector," in European conference on computer vision, 2016: Springer, pp. 21-37.
[57] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. J. I. j. o. c. v. Zisserman, "The pascal visual object classes (voc) challenge," vol. 88, no. 2, pp. 303-338, 2010.
[58] K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask R-CNN," p. arXiv:1703.06870. [Online].
Available: https://ui.adsabs.harvard.edu/abs/2017arXiv170306870H
[59] J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," p. arXiv:1804.02767.
99
[Online]. Available: https://ui.adsabs.harvard.edu/abs/2018arXiv180402767R
[60] G. Ghiasi, T.-Y. Lin, R. Pang, and Q. V. Le, "NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection," p. arXiv:1904.07392. [Online]. Available:
https://ui.adsabs.harvard.edu/abs/2019arXiv190407392G
[61] Z. Liu, T. Zheng, G. Xu, Z. Yang, H. Liu, and D. Cai, "Training-time-friendly network for real- time object detection," in Proceedings of the AAAI Conference on Artificial Intelligence, 2020, vol. 34, no. 07, pp. 11685-11692.
[62] M. Tan, R. Pang, and Q. V. Le, "EfficientDet: Scalable and Efficient Object Detection," p.
arXiv:1911.09070. [Online]. Available:
https://ui.adsabs.harvard.edu/abs/2019arXiv191109070T
[63] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," in arXiv e-prints, ed, 2020, p. arXiv:2004.10934.
[64] L. Liu, D. Ren, and F. J. M. Liu, "A review of dissimilar welding techniques for magnesium alloys to aluminum alloys," vol. 7, no. 5, pp. 3735-3757, 2014.
[65] X. Wang, H.-P. Wang, F. Lu, B. E. Carlson, and Y. J. T. I. J. o. A. M. T. Wu, "Analysis of solidification cracking susceptibility in side-by-side dual-beam laser welding of aluminum alloys," vol. 73, no. 1-4, pp. 73-85, 2014.
[66] M. M. Atabaki, N. Yazdian, and R. J. O. Kovacevic, "Partial penetration laser-based welding of aluminum alloy (AA 5083-H32)," vol. 127, no. 16, pp. 6782-6804, 2016.
[67] R. Lin, H.-p. Wang, F. Lu, J. Solomon, B. E. J. I. J. o. H. Carlson, and M. Transfer, "Numerical study of keyhole dynamics and keyhole-induced porosity formation in remote laser welding of Al alloys," vol. 108, pp. 244-256, 2017.
[68] L. Huang, X. Hua, D. Wu, and Y. J. T. I. J. o. A. M. T. Ye, "Role of welding speed on keyhole- induced porosity formation based on experimental and numerical study in fiber laser welding of Al alloy," vol. 103, no. 1, pp. 913-925, 2019.
[69] P. Liu, L. Huang, L. Gan, and Y. J. T. I. J. o. A. M. T. Lei, "Effect of plate thickness on weld pool dynamics and keyhole-induced porosity formation in laser welding of Al alloy," vol. 111, no. 3, pp. 735-747, 2020.
[70] R. Zhang, X. Tang, L. Xu, F. Lu, H. J. I. J. o. H. Cui, and M. Transfer, "Study of molten pool dynamics and porosity formation mechanism in full penetration fiber laser welding of Al-alloy,"
vol. 148, p. 119089, 2020.
[71] Y. Huang, C. Shen, X. Ji, F. Li, Y. Zhang, and X. J. J. o. M. P. T. Hua, "Correlation between gas-dynamic behaviour of a vapour plume and oscillation of keyhole size during laser welding of 5083 Al-alloy," vol. 283, p. 116721, 2020.
[72] Y. Huang et al., "Effects of Mg content on keyhole behaviour during deep penetration laser welding of Al-Mg alloys," vol. 125, p. 106056, 2020.
[73] D. P’ng and P. Molian, "Q-switch Nd:YAG laser welding of AISI 304 stainless steel foils,"
Materials Science and Engineering: A, vol. 486, no. 1-2, pp. 680-685, 2008, doi:
10.1016/j.msea.2007.08.063.
[74] V. A. Ventrella, J. R. Berretta, and W. de Rossi, "Pulsed Nd:YAG laser seam welding of AISI 316L stainless steel thin foils," (in English), Journal of Materials Processing Technology, vol.
210, no. 14, pp. 1838-1843, Nov 1 2010, doi: 10.1016/j.jmatprotec.2010.06.015.
[75] V. A. Ventrella, J. R. Berretta, W. J. I. J. o. M. De Rossi, and P. Technology, "Application of pulsed Nd: YAG laser in thin foil microwelding," vol. 48, no. 1-4, pp. 194-204, 2014.
[76] J. Kim et al., "Effect of beam size in laser welding of ultra-thin stainless steel foils," Journal of Materials Processing Technology, vol. 233, pp. 125-134, 2016, doi:
10.1016/j.jmatprotec.2016.02.019.
[77] M. I. S. Ismail, Y. Okamoto, A. Okada, and Y. Uno, "Experimental investigation on micro- welding of thin stainless steel sheet by fiber laser," American Journal of Engineering and Applied Sciences, vol. 4, no. 3, pp. 314-320, 2011.
[78] M. Seiler, A. Patschger, and J. Bliedtner, "Investigations of welding instabilities and weld seam formation during laser microwelding of ultrathin metal sheets," J. Laser Appl., vol. 28, no. 2, 2016, doi: 10.2351/1.4944446.
100
[79] M. Petrich, M. Stambke, and J. P. Bergmann, "Examinations on Laser Remote Welding of Ultra- thin Metal Foils Under Vacuum Conditions," Physics Procedia, vol. 56, pp. 768-775, 2014, doi:
10.1016/j.phpro.2014.08.084.
[80] M. V. Larin, Y. B. Pevzner, O. I. Grinin, and I. T. Lasota, "The use of single-mode fiber laser for welding of stainless steel thin thickness," in Journal of Physics: Conference Series, 2018, vol. 1109, 1 ed., doi: 10.1088/1742-6596/1109/1/012036. [Online]. Available:
https://www2.scopus.com/inward/record.uri?eid=2-s2.0- 85058310537&doi=10.1088%2f1742-
6596%2f1109%2f1%2f012036&partnerID=40&md5=869f5c6a02a608b144558d3c4e4134ac [81] H. Ki, P. S. Mohanty, and J. Mazumder, "Multiple reflection and its influence on keyhole
evolution," in International Congress on Applications of Lasers & Electro-Optics, 2001, vol.
2001, no. 1: Laser Institute of America, pp. 933-942.
[82] W. M. Steen and J. Mazumder, "Laser material processing," Springer-Verlag, London, vol. 4th ed., 2010.
[83] J. A. Roden and T. Kramer, "The convolutional PML for FDTD analysis: Transient electromagnetic absorption from DC to daylight," in 2011 IEEE International Symposium on Electromagnetic Compatibility, 2011: IEEE, pp. 892-898.
[84] A. E. Siegman, "Lasers," University Science Books, USA, 1986.
[85] J. Kim, S. Oh, and H. Ki, "Effect of keyhole geometry and dynamics in zero-gap laser welding of zinc-coated steel sheets," Journal of Materials Processing Technology, vol. 232, pp. 131-141, 2016.
[86] A. Kolesnikov et al., "Big transfer (BiT): General visual representation learning," arXiv preprint arXiv:1912.11370, 2019.
[87] S. Gross and M. Wilber, "Training and investigating residual nets,"
http://torch.ch/blog/2016/02/04/resnets.html, 2016.
[88] D. P. Kingma and J. L. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.
[89] K. He, X. Zhang, S. Ren, and J. Sun, "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition," p. arXiv:1406.4729. [Online]. Available:
https://ui.adsabs.harvard.edu/abs/2014arXiv1406.4729H
[90] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, "Path Aggregation Network for Instance Segmentation,"
p. arXiv:1803.01534. [Online]. Available:
https://ui.adsabs.harvard.edu/abs/2018arXiv180301534L
[91] C.-Y. Wang, H.-Y. M. Liao, I.-H. Yeh, Y.-H. Wu, P.-Y. Chen, and J.-W. Hsieh, "CSPNet: A New Backbone that can Enhance Learning Capability of CNN," p. arXiv:1911.11929. [Online].
Available: https://ui.adsabs.harvard.edu/abs/2019arXiv191111929W
[92] J. Redmon and A. Farhadi, "YOLO9000: better, faster, stronger," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263-7271.
[93] J. Yu, Y. Jiang, Z. Wang, Z. Cao, and T. Huang, "UnitBox: An Advanced Object Detection
Network," p. arXiv:1608.01471. [Online]. Available:
https://ui.adsabs.harvard.edu/abs/2016arXiv160801471Y
[94] Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, "Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression," p. arXiv:1911.08287. [Online]. Available:
https://ui.adsabs.harvard.edu/abs/2019arXiv191108287Z
[95] S. Oh, H. Kim, K. Nam, and H. Ki, "Deep-learning approach for predicting laser-beam absorptance in full-penetration laser keyhole welding," Opt. Express, vol. 29, no. 13, pp. 20010- 20021, 2021/06/21 2021, doi: 10.1364/OE.430952.
[96] J. Kim and H. Ki, "Scaling law for penetration depth in laser welding," Journal of Materials Processing Technology, vol. 214, no. 12, pp. 2908-2914, 2014, doi:
10.1016/j.jmatprotec.2014.06.025.
101
Acknowledgement
First, I would like to express my deepest appreciation to my advisor, Professor Hyungson Ki for trusting, supporting, and guiding my doctoral course. I would not have done my doctor course without his dedicated support and guidance, and I always admired everything about him.
Besides my advisor, I would like to appreciate the rest of dissertation committee as well: Professor Jaeseon Lee, Professor Jaesung Jang, Dr. Jaehun Kim, and Dr. Hyuntae Hwang, for listening carefully my research and giving me a lot of advice for improving my doctoral thesis.
Also, I would like to thank to my lab members: Dr. Jaehun Kim, Dr. Chun Deng, Sanseo Kim, Dr.
Keunhee Lee, Dr. Haram Yeo, Dr. Sehyeok Oh, Hyung Kook Jin, Myeonggyun Son, Hyunkyung Kim, Hojun Na, Kimoon Nam, Myungrin Woo, and Jeonghyun Yoo. Since I studied with them, I was able to complete my doctoral course.
Finally, I would like to thank my father and mother. I hope my doctoral thesis will be a small courage and pleasure during struggle against disease and I have no doubt that my father will recover because he is strong man. Thank you both again and I will try harder to be a great son.