| 研究生: |
林鈞慧 Lin, Chun-Hui |
|---|---|
| 論文名稱: |
金屬積層製造之智慧鋪粉監測與工件非破壞性評估 Smart Powder Coating Monitoring and Workpiece Non-destructive Assessment for Metallic Additive Manufacturing |
| 指導教授: |
王士豪
Wang, Shyh-Hau |
| 學位類別: |
博士 Doctor |
| 系所名稱: |
電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering |
| 論文出版年: | 2024 |
| 畢業學年度: | 112 |
| 語文別: | 英文 |
| 論文頁數: | 99 |
| 中文關鍵詞: | 金屬積層製造 、鋪粉監測 、工件非破壞性評估 、深度學習 、超音波量測 |
| 外文關鍵詞: | Metallic additive manufacturing, powder coating monitoring, workpiece non- destructive assessment, deep learning, ultrasound measurement |
| 相關次數: | 點閱:207 下載:0 |
| 分享至: |
| 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 |
[1] Z. Liu et al., "Additive Manufacturing of Metals: Microstructure Evolution and Multistage Control," J Mater Sci Technol, vol. 100, pp. 224-236, 2022.
[2] W. E. Frazier, "Metal Additive Manufacturing: A Review,", J Mater Eng Perform, vol. 23, no. 6, pp. 1917-1928, 2014.
[3] E. Atzeni and A. Salmi, "Economics of Additive Manufacturing for End-usable Metal Parts,", Int J Adv Manuf Tech, vol. 62, no. 9-12, pp. 1147-1155, 2012.
[4] T. S. Tamir et al., "3D Printing in Materials Manufacturing Industry: A Realm of Industry 4.0," Heliyon, 2023.
[5] H. Bikas, P. Stavropoulos, and G. Chryssolouris, "Additive Manufacturing Methods and Modelling Approaches: A Critical Review," The International Journal of Advanced Manufacturing Technology, vol. 83, pp. 389-405, 2016.
[6] S. M. DelVecchio, "Introduction," in Women in 3D Printing: From Bones to Bridges and Everything in Between, S. M. DelVecchio Ed. Cham: Springer International Publishing, 2021, pp. 1-5.
[7] W. Gao et al., "The Status, Challenges, and Future of Additive Manufacturing in Engineering," Comput Aided Design, vol. 69, pp. 65-89, 2015.
[8] M. Attaran, "The Rise of 3-D Printing: The Advantages of Additive Manufacturing over Traditional Manufacturing," Bus Horizons, vol. 60, no. 5, pp. 677-688, 2017.
[9] B. Duwa, E. P. Onakpojeruo, B. Uzun, I. Ozsahin, and D. U. Ozsahin, "Comparative Evaluation of 3D Filaments, Used in Additive Manufacturing of Biomedical Tools; Using Fuzzy Promethee," 2022.
[10] D. D. Singh, T. Mahender, and A. R. Reddy, "Powder Bed Fusion Process: A Brief Review," Materials Today: Proceedings, vol. 46, pp. 350-355, 2021.
[11] J. Gunasekaran, P. Sevvel, and I. John Solomon, "Metallic Materials Fabrication by Selective Laser Melting: A Review," Materials Today: Proceedings, vol. 37, pp. 252-256, 2021.
[12] H. C. Tran and Y. L. Lo, "Systematic Approach for Determining Optimal Processing Parameters to Produce Parts with High Density in Selective Laser Melting Process," Int J Adv Manuf Tech, vol. 105, no. 10, pp. 4443-4460, 2019.
[13] J. Reijonen, A. Revuelta, T. Riipinen, K. Ruusuvuori, and P. Puukko, "On the Effect of Shielding Gas Flow on Porosity and Melt Pool Geometry in Laser Powder Bed Fusion Additive Manufacturing," Additive Manufacturing, vol. 32, p. 101030, 2020.
[14] P. D. Murugan et al., "A Current State of Metal Additive Manufacturing Methods: A Review," Materials Today: Proceedings, vol. 59, pp. 1277-1283, 2022.
[15] C. Li, Z. Y. Liu, X. Y. Fang, and Y. B. Guo, "Residual Stress in Metal Additive Manufacturing," Procedia CIRP, vol. 71, pp. 348-353, 2018.
[16] B. Zhang, Y. Li, and Q. Bai, "Defect Formation Mechanisms in Selective Laser Melting: A Review," Chinese Journal of Mechanical Engineering, vol. 30, no. 3, pp. 515-527, 2017.
[17] G. Yang, Y. Xie, S. Zhao, L. Qin, X. Wang, and B. Wu, "Quality Control: Internal Defects Formation Mechanism of Selective Laser Melting Based on Laser-powder-melt Pool Interaction: A Review," Chinese Journal of Mechanical Engineering: Additive Manufacturing Frontiers, vol. 1, no. 3, p. 100037, 2022.
[18] D. Chen et al., "Research on In Situ Monitoring of Selective Laser Melting: A State of the Art Review," The International Journal of Advanced Manufacturing Technology, vol. 113, pp. 3121-3138, 2021.
[19] B. Wu et al., "In Situ Monitoring Methods for Selective Laser Melting Additive Manufacturing Process Based on Images - A Review," China Foundry, vol. 18, no. 4, pp. 265-285, 2021.
[20] T. Craeghs, S. Clijsters, J. P. Kruth, F. Bechmann, and M. C. Ebert, "Detection of Process Failures in Layerwise Laser Melting with Optical Process Monitoring," Physics Procedia, vol. 39, pp. 753-759, 2012.
[21] J. Li, L. Cao, J. Xu, S. Wang, and Q. Zhou, "In Situ Porosity Intelligent Classification of Selective Laser Melting Based on Coaxial Monitoring and Image Processing," Measurement, vol. 187, p. 110232, 2022.
[22] G. Repossini, V. Laguzza, M. Grasso, and B. M. Colosimo, "On the Use of Spatter Signature for In-Situ Monitoring of Laser Powder Bed Fusion," Additive Manufacturing, vol. 16, pp. 35-48, 2017.
[23] T.-N. Le, M.-H. Lee, Z.-H. Lin, H.-C. Tran, and Y.-L. Lo, "Vision-Based In-Situ Monitoring System for Melt-Pool Detection in Laser Powder Bed Fusion Process," J Manuf Process, vol. 68, pp. 1735-1745, 2021.
[24] 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," (in English), IEEE T Autom Sci Eng, vol. 18, no. 1, pp. 269-281, 2021.
[25] L. Wang, E. L. Li, H. Shen, R. P. Zou, A. B. Yu, and Z. Y. Zhou, "Adhesion Effects on Spreading of Metal Powders in Selective Laser Melting," Powder Technol, vol. 363, pp. 602-610, 2020.
[26] Y. M. Fouda and A. E. Bayly, "A DEM Study of Powder Spreading in Additive Layer Manufacturing," Granul Matter, vol. 22, no. 1, pp. 1-18, 2020.
[27] T. M. Wischeropp, C. Emmelmann, M. Brandt, and A. Pateras, "Measurement of Actual Powder Layer Height and Packing Density in A Single Layer in Selective Laser Melting," Additive Manufacturing, vol. 28, pp. 176-183, 2019.
[28] A. Phua, P. S. Cook, C. H. J. Davies, and G. W. Delaney, "Powder Spreading Over Realistic Laser Melted Surfaces In Metal Additive Manufacturing," Additive Manufacturing Letters, vol. 3, p. 100039, 2022.
[29] S. J. Qin and L. H. Chiang, "Advances And Opportunities In Machine Learning For Process Data Analytics," Comput Chem Eng, vol. 126, pp. 465-473, 2019.
[30] D. Buchbinder, H. Schleifenbaum, S. Heidrich, W. Meiners, and J. Bültmann, "High Power Selective Laser Melting (HP SLM) Of Aluminum Parts," Physics Procedia, vol. 12, pp. 271-278, 2011.
[31] T. Craeghs, S. Clijsters, E. Yasa, and J.-P. Kruth, "Online Quality Control Of Selective Laser Melting," in 2011 International Solid Freeform Fabrication Symposium, 2011: University of Texas at Austin.
[32] M. Abdelrahman, E. W. Reutzel, A. R. Nassar, and T. L. Starr, "Flaw Detection In Powder Bed Fusion Using Optical Imaging," Additive Manufacturing, vol. 15, pp. 1-11, 2017.
[33] Z. Lin et al., "A New Method For Automatic Detection Of Defects In Selective Laser Melting Based On Machine Vision," Materials, vol. 14, no. 15, p. 4175, 2021.
[34] H. Y. Chen et al., "Deep Learning Applied to Defect Detection in Powder Spreading Process of Magnetic Material Additive Manufacturing," Materials, vol. 15, no. 16, p. 5662, 2022.
[35] H. C. Tran and Y. L. Lo, "Heat Transfer Simulations Of Selective Laser Melting Process Based On Volumetric Heat Source With Powder Size Consideration," J Mater Process Tech, vol. 255, pp. 411-425, 2018.
[36] T.-C. Chuang, Y.-L. Lo, H.-C. Tran, Y.-A. Tsai, C.-Y. Chen, and C.-P. Chiu, "Optimization Of Butt-Joint Laser Welding Parameters For Elimination Of Angular Distortion Using High-Fidelity Simulations And Machine Learning," Optics & Laser Technology, vol. 167, p. 109566, 2023.
[37] C. Kamath, "Data Mining And Statistical Inference In Selective Laser Melting," Int J Adv Manuf Tech, vol. 86, no. 5-8, pp. 1659-1677, 2016.
[38] M. Khanzadeh, S. Chowdhury, M. A. Tschopp, H. R. Doude, M. Marufuzzaman, and L. Bian, "Monitoring Of Melt Pool Images For Porosity Prediction In Directed Energy Deposition Processes," IISE Trans, vol. 51, no. 5, pp. 437-455, 2019.
[39] B. M. Colosimo, Q. Huang, T. Dasgupta, and F. Tsung, "Opportunities And Challenges Of Quality Engineering For Additive Manufacturing," J Qual Technol, vol. 50, no. 3, pp. 233-252, 2018.
[40] S. Zhang, S. Rauniyar, S. Shrestha, A. Ward, and K. Chou, "An Experimental Study Of Tensile Property Variability In Selective Laser Melting," J Manuf Process, vol. 43, pp. 26-35, 2019.
[41] J. Robinson, M. Stanford, and A. Arjunan, "Correlation Between Selective Laser Melting Parameters, Pore Defects And Tensile Properties Of 99.9% Silver," Materials Today Communications, vol. 25, p. 101550, 2020.
[42] I. Segovia Ramírez, F. P. García Márquez, and M. Papaelias, "Review On Additive Manufacturing And Non-Destructive Testing," Journal of Manufacturing Systems, vol. 66, pp. 260-286, 2023.
[43] D. Cerniglia, M. Scafidi, A. Pantano, and J. Rudlin, "Inspection Of Additive-Manufactured Layered Components," Ultrasonics, vol. 62, pp. 292-8, 2015.
[44] S. Yuan and X. Yu, "Ultrasonic Non-Destructive Evaluation Of Selectively Laser-Sintered Polymeric Nanocomposites," Polym Test, vol. 90, p. 106705, 2020.
[45] L. W. Koester, H. Taheri, T. A. Bigelow, P. C. Collins, and L. J. Bonds, "Nondestructive Testing for Metal Parts Fabricated Using Powder-Based Additive Manufacturing," Mater Eval, vol. 76, no. 4, pp. 514-524, 2018.
[46] R. J. Smith, W. Q. Li, J. Coulson, M. Clark, M. G. Somekh, and S. D. Sharples, "Spatially Resolved Acoustic Spectroscopy For Rapid Imaging Of Material Microstructure And Grain Orientation," Meas Sci Technol, vol. 25, no. 5, p. 055902, 2014.
[47] C. Kim, H. S. Yin, A. Shmatok, B. C. Prorok, X. Y. Lou, and K. H. Matlack, "Ultrasonic Nondestructive Evaluation Of Laser Powder Bed Fusion 316L Stainless Steel," Additive Manufacturing, vol. 38, p. 101800, 2021.
[48] N. Huang, O. J. Cook, R. L. Smithson, C. M. Kube, A. P. Argüelles, and A. M. Beese, "Use Of Ultrasound To Identify Microstructure-Property Relationships In 316 Stainless Steel Fabricated With Binder Jet Additive Manufacturing," Additive Manufacturing, vol. 51, p. 102591, 2022.
[49] M. Meng, Y. J. Chua, E. Wouterson, and C. P. K. Ong, "Ultrasonic Signal Classification And Imaging System For Composite Materials Via Deep Convolutional Neural Networks," Neurocomputing, vol. 257, pp. 128-135, 2017.
[50] N. Munir, H. J. Kim, S. J. Song, and S. S. Kang, "Investigation Of Deep Neural Network With Drop Out For Ultrasonic Flaw Classification In Weldments," J Mech Sci Technol, vol. 32, no. 7, pp. 3073-3080, 2018.
[51] D. Medak, L. Posilovic, M. Subasic, M. Budimir, and S. Loncaric, "Automated Defect Detection From Ultrasonic Images Using Deep Learning," IEEE Trans Ultrason Ferroelectr Freq Control, vol. 68, no. 10, pp. 3126-3134, 2021.
[52] W. L. Xu, X. H. Li, and J. Zhang, "Multi-Feature Fusion Imaging Via Machine Learning For Laser Ultrasonic Based Defect Detection In Selective Laser Melting Part,", Opt Laser Technol, vol. 150, p. 107918, 2022.
[53] Z. Y. Wang, S. H. Lin, J. Xie, and Y. B. Lin, "Pruning Blocks for CNN Compression and Acceleration via Online Ensemble Distillation," (in English), IEEE Access, vol. 7, pp. 175703-175716, 2019.
[54] C. H. Lin, C. J. Lin, and S. H. Wang, "Genetic-Algorithm-Based Local Binary Convolutional Neural Network For Gender Recognition," Sensor Mater, vol. 33, no. 6, pp. 1917-1927, 2021.
[55] C. J. Lin, C. H. Lin, and S. H. Wang, "Integrated Image Sensor and Light Convolutional Neural Network for Image Classification," Math Probl Eng, vol. 2021, 2021.
[56] W. Pedrycz, "Neurocomputations in Relational Systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 3, pp. 289-297, 1991.
[57] P. V. de Campos Souza, "Fuzzy Neural Networks And Neuro-Fuzzy Networks: A Review The Main Techniques And Applications Used In The Literature," Applied Soft Computing, vol. 92, p. 106275, 2020.
[58] K. P. Korshunova, "A Convolutional Fuzzy Neural Network for Image Classification," in 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC), 18-25 2018, pp. 1-4.
[59] M. J. Hsu, Y. H. Chien, W. Y. Wang, and C. C. Hsu, "A Convolutional Fuzzy Neural Network Architecture for Object Classification with Small Training Database," (in English), International Journal of Fuzzy Systems, vol. 22, no. 1, pp. 1-10, 2020.
[60] C.-J. Lin and T.-Y. Yang, "A Fusion-Based Convolutional Fuzzy Neural Network for Lung Cancer Classification," International Journal of Fuzzy Systems, vol. 25, no. 2, pp. 451-467, 2023.
[61] H. Javidrad, M. Ghanbari, and F. Javidrad, "Effect Of Scanning Pattern And Volumetric Energy Density On The Properties Of Selective Laser Melting Ti-6Al-4V Specimens," Journal of Materials Research and Technology, vol. 12, pp. 989-998, 2021.
[62] R. Li, J. Liu, Y. Shi, L. Wang, and W. Jiang, "Balling Behavior Of Stainless Steel And Nickel Powder During Selective Laser Melting Process," The International Journal of Advanced Manufacturing Technology, vol. 59, pp. 1025-1035, 2012.
[63] C. Panwisawas, Y. Sovani, R. P. Turner, J. W. Brooks, H. C. Basoalto, and I. Choquet, "Modelling Of Thermal Fluid Dynamics For Fusion Welding," J Mater Process Tech, vol. 252, pp. 176-182, 2018.
[64] C. Y. Yap et al., "Review Of Selective Laser Melting: Materials And Applications," Appl Phys Rev, vol. 2, no. 4, 2015.
[65] J. P. Kruth, L. Froyen, J. Van Vaerenbergh, P. Mercelis, M. Rombouts, and B. Lauwers, "Selective Laser Melting Of Iron-Based Powder," J Mater Process Tech, vol. 149, no. 1, pp. 616-622, 2004.
[66] K. Q. Le, C. Tang, and C. H. Wong, "On The Study Of Keyhole-Mode Melting In Selective Laser Melting Process," International Journal of Thermal Sciences, vol. 145, p. 105992, 2019.
[67] W. E. King et al., "Observation Of Keyhole-Mode Laser Melting In Laser Powder-Bed Fusion Additive Manufacturing," J Mater Process Tech, vol. 214, no. 12, pp. 2915-2925, 2014.
[68] X. Yang, Y. Li, and B. Li, "Formation Mechanisms Of Lack Of Fusion And Keyhole-Induced Pore Defects In Laser Powder Bed Fusion Process: A Numerical Study," International Journal of Thermal Sciences, vol. 188, p. 108221, 2023.
[69] R. Lin, H.-p. Wang, F. Lu, J. Solomon, and B. E. Carlson, "Numerical Study Of Keyhole Dynamics And Keyhole-Induced Porosity Formation In Remote Laser Welding Of Al Alloys," International Journal of Heat and Mass Transfer, vol. 108, pp. 244-256, 2017.
[70] L. Huang, X. Hua, D. Wu, and F. Li, "Numerical Study Of Keyhole Instability And Porosity Formation Mechanism In Laser Welding Of Aluminum Alloy And Steel," J Mater Process Tech, vol. 252, pp. 421-431, 2018.
[71] J. Ning, W. Wang, B. Zamorano, and S. Y. Liang, "Analytical Modeling Of Lack-Of-Fusion Porosity In Metal Additive Manufacturing," Applied Physics A, vol. 125, pp. 1-11, 2019.
[72] M. Tang, P. C. Pistorius, and J. L. Beuth, "Prediction Of Lack-Of-Fusion Porosity For Powder Bed Fusion," Additive Manufacturing, vol. 14, pp. 39-48, 2017.
[73] W. Wang, J. Ning, and S. Y. Liang, "Analytical Prediction Of Balling, Lack-Of-Fusion And Keyholing Thresholds In Powder Bed Fusion," Applied Sciences, vol. 11, no. 24, p. 12053, 2021.
[74] P. Wang, M. H. Goh, Q. Li, M. L. S. Nai, and J. Wei, "Effect Of Defects And Specimen Size With Rectangular Cross-Section On The Tensile Properties Of Additively Manufactured Components," Virtual and Physical Prototyping, vol. 15, no. 3, pp. 251-264, 2020.
[75] P. Mercelis and J. P. Kruth, "Residual Stresses In Selective Laser Sintering And Selective Laser Melting," Rapid Prototyping J, vol. 12, no. 5, pp. 254-265, 2006.
[76] K. Kempen, B. Vrancken, L. Thijs, S. Buls, J. Van Humbeeck, and J.-P. Kruth, "Lowering Thermal Gradients In Selective Laser Melting By Pre-Heating The Baseplate," in Solid Freeform Fabrication Symposium Proceedings, 2013.
[77] N. Bastola, M. P. Jahan, N. Rangasamy, and C. S. Rakurty, "A Review Of The Residual Stress Generation In Metal Additive Manufacturing: Analysis Of Cause, Measurement, Effects, And Prevention," Micromachines, vol. 14, no. 7, p. 1480, 2023.
[78] K. K. Shung, Diagnostic Ultrasound: Imaging And Blood Flow Measurements. CRC press, 2005.
[79] H.-C. Lin, "Compounding Nakagami Parameter Ratio Imaging and Deep Learning Approach with Contrast-Enhanced Ultrasound for Tissue Lesion Assessment," Doctoral dissertation, National Cheng Kung University, 2021.
[80] K. K. Shung and G. A. Thieme, Ultrasonic Scattering In Biological Tissues. CRC press, 2022.
[81] P. M. Morse and K. U. Ingard, Theoretical Acoustics. Princeton university press, 1986.
[82] W. Pedrycz, "Fuzzy Neural Networks And Neurocomputations," Fuzzy Sets and Systems, vol. 56, no. 1, pp. 1-28, 1993.
[83] L. A. Zadeh, "A Fuzzy-Algorithmic Approach To The Definition Of Complex Or Imprecise Concepts," International Journal of Man-machine studies, vol. 8, no. 3, pp. 249-291, 1976.
[84] C.-T. Lin and C. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism To Intelligent Systems. Prentice-Hall, Inc., 1996.
[85] J. J. Buckley and Y. Hayashi, "Fuzzy Neural Networks: A Survey," Fuzzy sets and systems, vol. 66, no. 1, pp. 1-13, 1994.
[86] N. Oliver, "Nonlinear System Identification: From Classical Approaches To Neural Networks And Fuzzy Models," Ch, vol. 11, no. 4, pp. 294-296, 2001.
[87] F. Da, "Decentralized Sliding Mode Adaptive Controller Design Based On Fuzzy Neural Networks For Interconnected Uncertain Nonlinear Systems," IEEE Transactions on Neural Networks, vol. 11, no. 6, pp. 1471-1480, 2000.
[88] W. Pedrycz and F. Gomide, An Introduction To Fuzzy Sets: Analysis And Design. MIT press, 1998.
[89] D. Dubois and H. Prade, "An Introduction To Fuzzy Systems," Clinica Chimica Acta, vol. 270, no. 1, pp. 3-29, 1998.
[90] P. V. C. Souza, "Regularized Fuzzy Neural Networks For Pattern Classification Problems," International Journal of Applied Engineering Research, vol. 13, no. 5, pp. 2985-2991, 2018.
[91] G. Khodabandelou and M. M. Ebadzadeh, "Fuzzy Neural Network With Support Vector-Based Learning For Classification And Regression," Soft Comput, vol. 23, no. 23, pp. 12153-12168, 2019.
[92] C.-J. Lin and J.-Y. Jhang, "Intelligent Traffic-Monitoring System Based On YOLO And Convolutional Fuzzy Neural Networks," IEEE Access, vol. 10, pp. 14120-14133, 2022.
[93] C.-J. Lin, J.-Y. Jhang, S.-H. Chen, and K.-Y. Young, "Using An Interval Type-2 Fuzzy Neural Network And Tool Chips For Flank Wear Prediction," IEEE Access, vol. 8, pp. 122626-122640, 2020.
[94] Z. J. Hou et al., "Online Monitoring Technology of Metal Powder Bed Fusion Processes: A Review," Materials, vol. 15, no. 21, p. 7598, 2022.
[95] Z. Y. Zhang, "A Flexible New Technique For Camera Calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330-1334, 2000.
[96] H. C. Tran, Y. L. Lo, and M. H. Huang, "Analysis of Scattering and Absorption Characteristics of Metal Powder Layer for Selective Laser Sintering,", IEEE-ASME T Mech, vol. 22, no. 4, pp. 1807-1817, 2017.
[97] R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural Features for Image Classification," IEEE T Syst Man Cyb, vol. Smc3, no. 6, pp. 610-621, 1973.
[98] M. O'Byrne, B. Ghosh, V. Pakrashi, and F. Schoefs, "Texture Analysis Based Detection And Classification Of Surface Features On Ageing Infrastructure Elements," in BCRI2012 Bridge & Concrete Research in Ireland, 2012.
[99] C. J. Lin, C. J. Lin, and X. Q. Lin, "Automatic Sleep Stage Classification Using a Taguchi-Based Multiscale Convolutional Compensatory Fuzzy Neural Network," Appl Sci-Basel, vol. 13, no. 18, p. 10442, 2023.
[100] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-Based Learning Applied To Document Recognition," P IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[101] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking The Inception Architecture For Computer Vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818-2826.
[102] M. Tan and Q. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," presented at the Proceedings of the 36th International Conference on Machine Learning, Proceedings of Machine Learning Research, 2019.
[103] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," pp. 770-778, 2016.
[104] X. Zhang, X. Zhou, M. Lin, and J. Sun, "Shufflenet: An Extremely Efficient Convolutional Neural Network For Mobile Devices," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 6848-6856.
[105] F. Chollet, "Xception: Deep Learning With Depthwise Separable Convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251-1258.
[106] F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size," arXiv preprint arXiv:1602.07360, 2016.
[107] A. G. Howard et al., "Mobilenets: Efficient Convolutional Neural Networks For Mobile Vision Applications," arXiv preprint arXiv:1704.04861, 2017.
[108] Z. Liu, X. Wang, H. Kim, Y. Zhou, W. Cong, and H. Zhang, "Investigations of Energy Density Effects on Forming Accuracy and Mechanical Properties of Inconel 718 Fabricated by LENS Process," Procedia Manufacturing, vol. 26, pp. 731-739, 2018.
[109] H.-C. Tran, Y.-L. Lo, T.-N. Le, A. K.-T. Lau, and H.-Y. Lin, "Multi-Scale Simulation Approach For Identifying Optimal Parameters For Fabrication Ofhigh-Density Inconel 718 Parts Using Selective Laser Melting," Rapid Prototyping J, vol. 28, no. 1, pp. 109-125, 2021.
[110] A. B. Spierings, M. Schneider, and R. Eggenberger, "Comparison Of Density Measurement Techniques For Additive Manufactured Metallic Parts," Rapid Prototyping J, vol. 17, no. 5, pp. 380-386, 2011.
[111] M. M. Pasternak et al., "High-Frequency Ultrasound Detection Of Cell Death: Spectral Differentiation Of Different Forms Of Cell Death In Vitro," Oncoscience, vol. 3, no. 9-10, pp. 275-287, 2016.
[112] H. Chih-Chung and W. Shyh-Hau, "Blood Coagulation And Clot Formation Studies Using High Frequency Ultrasounds," in IEEE Ultrasonics Symposium, 2004, 23-27, 2004, vol. 3, pp. 1757-1760.
[113] O. Cook, N. Huang, R. Smithson, C. Kube, A. Beese, and A. Argüelles, "Ultrasonic Characterization of Porosity in Components Made by Binder Jet Additive Manufacturing," Mater Eval, vol. 80, no. 4, pp. 37-44, 2022.
[114] İ. Sarpün, V. Özkan, A. Yönetken, and A. Erol, "Mean Grain Size and Pore Effects οn Ultrasonic Properties οf WC-Fe-Ni and SiC-Fe-Ni Composites," Acta Physica Polonica A, vol. 123, no. 4, pp. 688-694, 2013.
[115] V. Dutt and J. F. Greenleaf, "Ultrasound Echo Envelope Analysis Using A Homodyned K Distribution Signal Model," Ultrasonic Imaging, vol. 16, no. 4, pp. 265-287, 1994.
[116] P. H. Tsui and C. C. Chang, "Imaging Local Scatterer Concentrations By The Nakagami Statistical Model," Ultrasound Med Biol, vol. 33, no. 4, pp. 608-19, 2007.
[117] P. H. Tsui and S. H. Wang, "The Effect Of Transducer Characteristics On The Estimation Of Nakagami Paramater As A Function Of Scatterer Concentration," Ultrasound Med Biol, vol. 30, no. 10, pp. 1345-53, 2004.
[118] P. Mohana Shankar, "A General Statistical Model For Ultrasonic Backscattering From Tissues," IEEE Trans Ultrason Ferroelectr Freq Control, vol. 47, no. 3, pp. 727-36, 2000.
[119] M. Xu et al., "Value Of Histogram Of Gray-Scale Ultrasound Image In Differential Diagnosis Of Small Triple Negative Breast Invasive Ductal Carcinoma And Fibroadenoma," Cancer Management and Research, pp. 1515-1524, 2022.
[120] A. H. Farhan and M. Y. Kamil, "Texture Analysis of Breast Cancer via LBP, HOG, and GLCM techniques," in IOP conference series: materials science and engineering, 2020, vol. 928, no. 7: IOP Publishing, p. 072098.
[121] S. Barburiceanu, R. Terebes, and S. Meza, "3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors," Appl Sci-Basel, vol. 11, no. 5, p. 2332, 2021.
[122] C. B. Marschner, M. Kokla, J. M. Amigo, E. A. Rozanski, B. Wiinberg, and F. J. McEvoy, "Texture Analysis Of Pulmonary Parenchymateous Changes Related To Pulmonary Thromboembolism In Dogs - A Novel Approach Using Quantitative Methods," BMC Vet Res, vol. 13, no. 1, p. 219, 2017.
[123] D. C. R. Novitasari, A. Lubab, A. Sawiji, and A. H. Asyhar, "Application Of Feature Extraction For Breast Cancer Using One Order Statistic, GLCM, GLRLM, and GLDM," Advances in Science, Technology and Engineering Systems Journal (ASTESJ), vol. 4, no. 4, pp. 115-120, 2019.
[124] S. Muhtadi and H. Hamid, "Analysis of GLRLM Texture Features Derived From Computed Tomography Scans For COVID-19 Diagnosis," in 2021 13th Biomedical Engineering International Conference (BMEiCON), 2021: IEEE, pp. 1-5.
[125] D. Li, J. Zhang, Q. Zhang, and X. Wei, "Classification of ECG signals based on 1D convolution neural network," in 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), 2017: IEEE, pp. 1-6.
[126] H. Sabnis, J. Angel Arul Jothi, and A. M. Deva Prasad, "Microstructure Image Classification of Metals Using Texture Features and Machine Learning," in Soft Computing and its Engineering Applications, Cham, K. K. Patel, G. Doctor, A. Patel, and P. Lingras, Eds., 2022// 2022: Springer International Publishing, pp. 235-248.
[127] A. Chowdhury, E. Kautz, B. Yener, and D. Lewis, "Image Driven Machine Learning Methods For Microstructure Recognition," Comp Mater Sci, vol. 123, pp. 176-187, 2016.
[128] W. Zouhri et al., "Optical Process Monitoring For Laser-Powder Bed Fusion (L-PBF)," CIRP Journal of Manufacturing Science and Technology, vol. 31, pp. 607-617, 2020.
[129] C. Ding and H. Peng, "Minimum Redundancy Feature Selection From Microarray Gene Expression Data," Journal of bioinformatics and computational biology, vol. 3, no. 2, pp. 185-205, 2005.
[130] S. M. Lundberg and S.-I. Lee, "A Unified Approach To Interpreting Model Predictions," Advances in neural information processing systems, vol. 30, 2017.
[131] K. Walton and M. Skliar, "Ultrasonic Characterization of Spatially Varying Material Properties in Metal Components Fabricated by Additive Manufacturing," in 2019 IEEE International Ultrasonics Symposium (IUS), 6-9 Oct. 2019 2019, pp. 1260-1263.
[132] G. Hamada and V. Joseph, "Developed Correlations Between Sound Wave Velocity And Porosity, Permeability And Mechanical Properties Of Sandstone Core Samples," Petroleum Research, vol. 5, no. 4, pp. 326-338, 2020.
[133] Y. Song, C. M. Kube, Z. Peng, J. A. Turner, and X. Li, "Flaw Detection With Ultrasonic Backscatter Signal Envelopes," J Acoust Soc Am, vol. 145, no. 2, p. EL142, 2019.
校內:2028-01-18公開