Towards deep-learning-based image enhancement for optical camera-based monitoring system of laser powder bed fusion process

References

• Agustsson, E., and R. Timofte. 2017. NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE. 1122–1131. doi:10.1109/CVPRW.2017.150.
   Google Scholar
• Ali, U., Y. Mahmoodkhani, S. Imani Shahabad, R. Esmaeilizadeh, F. Liravi, E. Sheydaeian, K. Y. Huang, E. Marzbanrad, M. Vlasea, and E. Toyserkani. 2018. “On the Measurement of Relative Powder-Bed Compaction Density in Powder-Bed Additive Manufacturing Processes.” Materials & Design 155: 495–501. doi:10.1016/j.matdes.2018.06.030.
   Web of Science ®Google Scholar
• Bee, L., S. Son, H. Kim, S. Nah, and K. M. Lee. 2017. “Enhanced Deep Residual Networks for Single Image Super-Resolution.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW): IEEE.
   Google Scholar
• Berumen, S., F. Bechmann, S. Lindner, J.-P. Kruth, and T. Craeghs. 2010. “Quality Control of Laser- and Powder Bed-Based Additive Manufacturing (AM) Technologies.” Physics Procedia 5: 617–622. doi:10.1016/j.phpro.2010.08.089.
   Google Scholar
• Clijsters, S., T. Craeghs, S. Buls, K. Kempen, and J.-P. Kruth. 2014. “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 75 (5–8): 1089–1101. doi:10.1007/s00170-014-6214-8.
   Web of Science ®Google Scholar
• Deng, J., W. Dong, R. Socher, L. Li-Jia, L. Kai, and L. Fei-Fei. 2009 - 2009. “ImageNet: A Large-Scale Hierarchical Image Database.” In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–55: IEEE.
   Google Scholar
• Dong, C., C. C. Loy, H. Kaiming, and X. Tang. 2014. Learning a Deep Convolutional Network for Image Super-Resolution. Vol. 184–99. Cham: Springer. https://link.springer.com/chapter/10.1007/978-3-319-10593-2_13
   Google Scholar
• Dong, C., C. C. Loy, H. Kaiming, and X. Tang. 2016. “Image Super-Resolution Using Deep Convolutional Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (2): 295–307. doi:10.1109/tpami.2015.2439281.
   PubMed Web of Science ®Google Scholar
• Everton, S. K., M. Hirsch, P. Stravroulakis, R. K. Leach, and A. T. Clare. 2016. “Review of in-Situ Process Monitoring and in-Situ Metrology for Metal Additive Manufacturing.” Materials & Design 95: 431–445. doi:10.1016/j.matdes.2016.01.099.
   Web of Science ®Google Scholar
• Imani, F., A. Gaikwad, M. Montazeri, P. Rao, H. Yang, and E. Reutzel. 2018. “Layerwise in-Process Quality Monitoring in Laser Powder. Bed Fusion”.: American Society of Mechanical Engineers Digital Collection.
   Google Scholar
• Johnson, J., A. Alahi, and L. Fei-Fei. 2016. “Perceptual Losses for Real-Time Style Transfer and Super-Resolution”. In Computer Vision – ECCV 2016. B. Leibe, J. Matas, N. Sebe, and M. Welling edited by. Vol. 9906, 694–711.Lecture Notes in Computer Science 9906 Cham:Springer International Publishing.
   Google Scholar
• Ledig, C., L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, et al. 2017. “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): IEEE.
   Google Scholar
• Marco, G., and B. M. Colosimo. 2017. “Process Defects and in Situmonitoring Methods in Metal Powder Bed Fusion: A Review.” Measurement Science and Technology 28 (4): 44005. doi:10.1088/1361-6501/aa5c4f.
   Web of Science ®Google Scholar
• Michael, S., M. Merklein, D. Bourell, D. Dimitrov, T. Hausotte, K. Wegener, L. Overmeyer, F. Vollertsen, and G. N. Levy. 2017. “Laser Based Additive Manufacturing in Industry and Academia.” CIRP Annals 66 (2): 561–583. doi:10.1016/j.cirp.2017.05.011.
   Web of Science ®Google Scholar
• Mohr, G. 2019. Measurement of Particle Emissions in Laser Powder Bed Fusion (L-PBF) Processes and Its Potential for in-Situ Process Monitoring. https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docid/49387
   Google Scholar
• Mohr, G., S. J. Altenburg, A. Ulbricht, P. Heinrich, D. Baum, C. Maierhofer, and K. Hilgenberg. 2020. “In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography.” Metals 10 (1): 103. doi:10.3390/met10010103.
   Web of Science ®Google Scholar
• Neef, A., V. Seyda, D. Herzog, C. Emmelmann, M. Schönleber, and M. Kogel-Hollacher. 2014. “Low Coherence Interferometry in Selective Laser Melting.” Physics Procedia 56: 82–89. doi:10.1016/j.phpro.2014.08.100.
   Google Scholar
• Plessis, D., Anton, I. Yadroitsev, I. Yadroitsava, and S. G. Le Roux. 2018. “X-Ray Microcomputed Tomography in Additive Manufacturing: A Review of the Current Technology and Applications.” 3D Printing and Additive Manufacturing 5 (3): 227–247. doi:10.1089/3dp.2018.0060.
   Google Scholar
• Robinson, J., I. Ashton, P. Fox, E. Jones, and C. Sutcliffe. 2018. “Determination of the Effect of Scan Strategy on Residual Stress in Laser Powder Bed Fusion Additive Manufacturing.” Additive Manufacturing 23: 13–24. doi:10.1016/j.addma.2018.07.001.
   Web of Science ®Google Scholar
• Simonyan, K., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large-Scale Image Recognition.“ arXiv preprint arXiv:1409.1556.
   Google Scholar
• Spears, T. G., and S. A. Gold. 2016. “In-Process Sensing in Selective Laser Melting (SLM) Additive Manufacturing.” Integrating Materials and Manufacturing Innovation 5 (1): 16–40. doi:10.1186/s40192-016-0045-4.
   Google Scholar
• Spierings, A. B., K. Dawson, M. Voegtlin, F. Palm, and P. J. Uggowitzer. 2016. “Microstructure and Mechanical Properties of as-Processed Scandium-Modified Aluminium Using Selective Laser Melting.” CIRP Annals 65 (1): 213–216. doi:10.1016/j.cirp.2016.04.057.
   Web of Science ®Google Scholar
• Tai, Y., J. Yang, X. Liu, and X. Chunyan. 2017. “Memnet: A persistent memory network for image restoration.“ In Proceedings of the IEEE international conference on computer vision (pp. 4549–4557). IEEE. doi:10.1109/ICCV.2017.486.
   Google Scholar
• Tatsuaki, F., T. Ueda, M. R. Alkahari, and A. Hosokawa. 2013. “Investigation of Laser Consolidation Process for Metal Powder by Two-Color Pyrometer and High-Speed Video Camera.” CIRP Annals - Manufacturing Technology 62 (1): 223–226. doi:10.1016/j.cirp.2013.03.032.
   Web of Science ®Google Scholar
• Xiaodan, H., M. A. Naiel, A. Wong, M. Lamm, and P. Fieguth. 2019. “RUNet: A Robust UNet Architecture for Image Super-Resolution.” In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW): IEEE.
   Google Scholar
• Zenzinger, G., J. Bamberg, A. Ladewig, T. Hess, B. Henkel, and W. Satzger. 2015. “Process monitoring of additive manufacturing by using optical tomography.“ In AIP Conference Proceedings (Vol. 1650, pp. 164–170). AIP Publishing LLC
   Google Scholar
• Zhang, Z. 2000. “A Flexible New Technique for Camera Calibration.” IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (11): 1330–1334. doi:10.1109/34.888718.
   Web of Science ®Google Scholar
• Zhang, K., W. Zuo, and L. Zhang. 2018. “Learning a Single Convolutional Super-Resolution Network for Multiple Degradations.“ Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3262–3271). IEEE. doi:10.1109/CVPR.2018.00344.
   Google Scholar
• Zhihao, W., J. Chen, and S. C. H. Hoi. 2021. “Deep Learning for Image Super-Resolution: A Survey.” IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (10): 3365–3387. doi:10.1109/TPAMI.2020.2982166.
   PubMed Web of Science ®Google Scholar
• Zur Jacobsmühlen Joschka. 2018. Image-based methods for inspection of laser beam melting processes. RWTH Aachen University. http://publications.rwth-aachen.de/record/760489
   Google Scholar