High-speed 3D optical sensing for manufacturing research and industrial sensing applications

This paper presents examples of high-speed 3D optical sensing for research and applications in the manufacturing community. Specifically, this paper will focus on the fringe projection technique as a special technology that can be extremely beneficial to manufacturing applications, given its merits...

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Autores:: Li, Beiwen

Tipo de recurso:: Article of journal

Fecha de publicación:: 2022

Institución:: Universidad Tecnológica de Bolívar

Repositorio:: Repositorio Institucional UTB

Idioma:: eng

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network_acronym_str	UTB2
network_name_str	Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv	High-speed 3D optical sensing for manufacturing research and industrial sensing applications
dc.title.translated.spa.fl_str_mv	High-speed 3D optical sensing for manufacturing research and industrial sensing applications
title	High-speed 3D optical sensing for manufacturing research and industrial sensing applications
spellingShingle	High-speed 3D optical sensing for manufacturing research and industrial sensing applications High-speed 3D optical sensing fringe projection manufacturing in-situ monitoring post-process quality evaluation
title_short	High-speed 3D optical sensing for manufacturing research and industrial sensing applications
title_full	High-speed 3D optical sensing for manufacturing research and industrial sensing applications
title_fullStr	High-speed 3D optical sensing for manufacturing research and industrial sensing applications
title_full_unstemmed	High-speed 3D optical sensing for manufacturing research and industrial sensing applications
title_sort	High-speed 3D optical sensing for manufacturing research and industrial sensing applications
dc.creator.fl_str_mv	Li, Beiwen
dc.contributor.author.eng.fl_str_mv	Li, Beiwen
dc.subject.eng.fl_str_mv	High-speed 3D optical sensing fringe projection manufacturing in-situ monitoring post-process quality evaluation
topic	High-speed 3D optical sensing fringe projection manufacturing in-situ monitoring post-process quality evaluation
description	This paper presents examples of high-speed 3D optical sensing for research and applications in the manufacturing community. Specifically, this paper will focus on the fringe projection technique as a special technology that can be extremely beneficial to manufacturing applications, given its merits of simultaneous high-speed and high-accuracy 3D surface measurements. This paper will introduce the basic principles of 3D optical sensing based on the fringe projection technique as well as the enabled manufacturing research applications, including both in-situ/in-process monitoring and post-process quality assurance.
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-07-28 16:45:26 2025-05-21T19:15:45Z
dc.date.available.none.fl_str_mv	2022-07-28 16:45:26
dc.date.issued.none.fl_str_mv	2022-07-28
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.eng.fl_str_mv	info:eu-repo/semantics/article
dc.type.coar.eng.fl_str_mv	http://purl.org/coar/resource_type/c_6501
dc.type.local.eng.fl_str_mv	Journal article
dc.type.content.eng.fl_str_mv	Text
dc.type.version.eng.fl_str_mv	info:eu-repo/semantics/publishedVersion
dc.type.coarversion.eng.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
format	http://purl.org/coar/resource_type/c_6501
status_str	publishedVersion
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12585/13505
dc.identifier.url.none.fl_str_mv	https://doi.org/10.32397/tesea.vol3.n2.490
dc.identifier.doi.none.fl_str_mv	10.32397/tesea.vol3.n2.490
dc.identifier.eissn.none.fl_str_mv	2745-0120
url	https://hdl.handle.net/20.500.12585/13505 https://doi.org/10.32397/tesea.vol3.n2.490
identifier_str_mv	10.32397/tesea.vol3.n2.490 2745-0120
dc.language.iso.eng.fl_str_mv	eng
language	eng
dc.relation.references.eng.fl_str_mv	Arkadeep Kumar. Methods and materials for smart manufacturing: additive manufacturing, internet of things, flexible sensors and soft robotics. Manufacturing Letters, 15:122–125, 2018. [2] Yuhang Yang, Zhiqiao Dong, Yuquan Meng, and Chenhui Shao. Data-driven intelligent 3d surface measurement in smart manufacturing: review and outlook. Machines, 9(1):13, 2021. [3] Longyu Zhang, Haiwei Dong, and Abdulmotaleb El Saddik. From 3d sensing to printing: A survey. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 12(2):1–23, 2015. [4] QMJ Qu, MF Ricky Lee, and Clarence W de Silva. Intelligent 3d sensing in automated manufacturing processes. In 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, volume 1, pp. 366–370. IEEE, 2001. [5] Kevin Harding. Handbook of optical dimensional metrology. CRC Press, 2013. [6] Toru Yoshizawa. Handbook of optical metrology: Principles and Applications. CRC press, 2009. [7] W Gao, H Haitjema, FZ Fang, RK Leach, CF Cheung, E Savio, and Jean-Marc Linares. On-machine and in-process surface metrology for precision manufacturing. CIRP Annals, 68(2):843–866, 2019. [8] Sarah K Everton, Matthias Hirsch, Petros Stravroulakis, Richard K Leach, and Adam T Clare. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Materials & Design, 95:431–445, 2016. [9] Sai Siva Gorthi and Pramod Rastogi. Fringe projection techniques: whither we are? Optics and Lasers in Engineering, 48(2):133–140, 2010. [10] Jason Geng. Structured-light 3d surface imaging: a tutorial. Advances in Optics and Photonics, 3(2):128–160, 2011. [11] Andres G Marrugo, Feng Gao, and Song Zhang. State-of-the-art active optical techniques for three-dimensional surface metrology: a review [invited]. Journal of the Optical Society of America A, 37(9):B60–B77, 2020. [12] Samuel Tolansky. An introduction to interferometry. Longman, 1973. [13] James C Wyant. White light interferometry. In Holography: A Tribute to Yuri Denisyuk and Emmett Leith, volume 4737, pp. 98–107. SPIE, 2002. [14] James C Wyant. Interferometric optical metrology: Basic principles and new systems. Laser Focus, USA, 18(5):65–71, 1982. [15] Andreas Kolb, Erhardt Barth, Reinhard Koch, and Rasmus Larsen. Time-of-flight cameras in computer graphics. Computer Graphics Forum, 29(1):141–159, 2010. [16] Filiberto Chiabrando, Roberto Chiabrando, Dario Piatti, and Fulvio Rinaudo. Sensors for 3d imaging: Metric evaluation and calibration of a ccd/cmos time-of-flight camera. Sensors, 9(12):10080–10096, 2009. [17] Robert Lange and Peter Seitz. Solid-state time-of-flight range camera. IEEE Journal of Quantum Electronics, 37(3):390–397, 2001. [18] Yan Cui, Sebastian Schuon, Derek Chan, Sebastian Thrun, and Christian Theobalt. 3d shape scanning with a time-of-flight camera. In Proc. IEEE on CVPR, pp. 1173–1180. IEEE, 2010. [19] Carlo Dal Mutto, Pietro Zanuttigh, and Guido M Cortelazzo. Time-of-flight cameras and Microsoft Kinect (TM). Springer Science & Business Media, 2012. [20] S Parthasarathy, J Birk, and J Dessimoz. Laser rangefinder for robot control and inspection. In Robot Vision, volume 336, pp. 2–11. SPIE, 1982. [21] G Bickel, G Hausler, and M Maul. Triangulation with expanded range of depth. Optical Engineering, 24(6):246975, 1985. [22] João Guilherme DM França, Mário A Gazziro, Alessandro Noriaki Ide, and José Hiroki Saito. A 3d scanning system based on laser triangulation and variable field of view. In IEEE International Conference on Image Processing 2005, volume 1, pp. I–425. IEEE, 2005. [23] Rainer G Dorsch, Gerd Häusler, and Jürgen M Herrmann. Laser triangulation: fundamental uncertainty in distance measurement. Applied Optics, 33(7):1306–1314, 1994. [24] Umesh R Dhond and Jake K Aggarwal. Structure from stereo-a review. IEEE transactions on systems, man, and cybernetics, 19(6):1489–1510, 1989. [25] Daniel Scharstein and Richard Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1):7–42, 2002. [26] Joaquim Salvi, Jordi Pages, and Joan Batlle. Pattern codification strategies in structured light systems. Pattern Recognition, 37(4):827–849, 2004. [27] Song Zhang. Recent progresses on real-time 3-d shape measurement using digital fringe projection techniques. Optics and Lasers in Engineering, 48(2):149–158, 2010. [28] Sam Van der Jeught, Joris AM Soons, and Joris JJ Dirckx. Real-time microscopic phase-shifting profilometry. Applied Optics, 54(15):4953–4959, 2015. [29] Nikolaus Karpinsky, Morgan Hoke, Vincent Chen, and Song Zhang. High-resolution, real-time three-dimensional shape measurement on graphics processing unit. Optical Engineering, 53(2):024105, 2014. [30] Song Zhang. High-speed 3D imaging with digital fringe projection techniques. CRC Press, 2018. [31] Mitsuo Takeda, Hideki Ina, and Seiji Kobayashi. Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry. Journal of the Optical Society of America A, 72(1):156–160, 1982. [32] Daniel Malacara. Optical shop testing, volume 59. John Wiley & Sons, 2007. [33] Song Zhang. Absolute phase retrieval methods for digital fringe projection profilometry: A review. Optics and Lasers in Engineering, 107:28–37, 2018. [34] Chao Zuo, Lei Huang, Minliang Zhang, Qian Chen, and Anand Asundi. Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review. Optics and Lasers in Engineering, 85:84–103, 2016. [35] Beiwen Li, Nikolaus Karpinsky, and Song Zhang. Novel calibration method for structured-light system with an out-of-focus projector. Applied optics, 53(16):3415–3426, 2014. [36] Shuangyan Lei and Song Zhang. Flexible 3-d shape measurement using projector defocusing. Optics Letters, 34(20):3080–3082, 2009. [37] Shijie Feng, Liang Zhang, Chao Zuo, Tianyang Tao, Qian Chen, and Guohua Gu. High dynamic range 3d measurements with fringe projection profilometry: a review. Measurement Science and Technology, 29(12):122001, 2018. [38] Hui Lin, Jian Gao, Guanjin Zhang, Xin Chen, Yunbo He, and Yan Liu. Review and comparison of high-dynamic range three-dimensional shape measurement techniques. Journal of Sensors, 2017, 2017. [39] Yue Liu, Liam Blunt, Feng Gao, and Xiangqian Jiang. High-dynamic-range 3d measurement for e-beam fusion additive manufacturing based on svm intelligent fringe projection system. Surface Topography: Metrology and Properties, 9(3):034002, 2021. [40] Liang Zhang, Qian Chen, Chao Zuo, and Shijie Feng. High-speed high dynamic range 3d shape measurement based on deep learning. Optics and Lasers in Engineering, 134:106245, 2020. [41] Bin Zhang, John Ziegert, Faramarz Farahi, and Angela Davies. In situ surface topography of laser powder bed fusion using fringe projection. Additive Manufacturing, 12:100–107, 2016. [42] Andrew Dickins, Taufiq Widjanarko, Danny Sims-Waterhouse, Adam Thompson, Simon Lawes, Nicola Senin, and Richard Leach. Multi-view fringe projection system for surface topography measurement during metal powder bed fusion. Journal of the Optical Society of America A, 37(9):B93–B105, 2020. [43] Haolin Zhang, Chaitanya Krishna Prasad Vallabh, Yubo Xiong, and Xiayun Zhao. A systematic study and framework of fringe projection profilometry with improved measurement performance for in-situ lpbf process monitoring. Measurement, 191:110796, 2022. [44] Zhongwei Li, Xingjian Liu, Shifeng Wen, Piyao He, Kai Zhong, Qingsong Wei, Yusheng Shi, and Sheng Liu. In situ 3d monitoring of geometric signatures in the powder-bed-fusion additive manufacturing process via vision sensing methods. Sensors, 18(4):1180, 2018. [45] Xiao Zhang, Weijun Shen, Vignesh Suresh, Jakob Hamilton, Li-Hsin Yeh, Xuepeng Jiang, Zhan Zhang, Qing Li, Beiwen Li, Iris V Rivero, et al. In situ monitoring of direct energy deposition via structured light system and its application in remanufacturing industry. The International Journal of Advanced Manufacturing Technology, 116(3):959–974, 2021. [46] Giovanna Sansoni, Matteo Carocci, and Roberto Rodella. Three-dimensional vision based on a combination of gray-code and phase-shift light projection: analysis and compensation of the systematic errors. Applied Optics, 38(31):6565–6573, 1999. [47] Beiwen Li and Song Zhang. Flexible calibration method for microscopic structured light system using telecentric lens. Optics Express, 23(20):25795–25803, 2015. [48] Kwangwoo Wi, Vignesh Suresh, Kejin Wang, Beiwen Li, and Hantang Qin. Quantifying quality of 3d printed clay objects using a 3d structured light scanning system. Additive Manufacturing, 32:100987, 2020.
dc.relation.ispartofjournal.eng.fl_str_mv	Transactions on Energy Systems and Engineering Applications
dc.relation.citationvolume.eng.fl_str_mv	3
dc.relation.citationstartpage.none.fl_str_mv	1
dc.relation.citationendpage.none.fl_str_mv	12
dc.relation.bitstream.none.fl_str_mv	https://revistas.utb.edu.co/tesea/article/download/490/371
dc.relation.citationedition.eng.fl_str_mv	Núm. 2 , Año 2022 : Transactions on Energy Systems and Engineering Applications
dc.relation.citationissue.eng.fl_str_mv	2
dc.rights.eng.fl_str_mv	Beiwen Li - 2022
dc.rights.uri.eng.fl_str_mv	https://creativecommons.org/licenses/by/4.0
dc.rights.accessrights.eng.fl_str_mv	info:eu-repo/semantics/openAccess
dc.rights.creativecommons.eng.fl_str_mv	This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.coar.eng.fl_str_mv	http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv	Beiwen Li - 2022 https://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.mimetype.eng.fl_str_mv	application/pdf
dc.publisher.eng.fl_str_mv	Universidad Tecnológica de Bolívar
dc.source.eng.fl_str_mv	https://revistas.utb.edu.co/tesea/article/view/490
institution	Universidad Tecnológica de Bolívar
repository.name.fl_str_mv	Repositorio Digital Universidad Tecnológica de Bolívar
repository.mail.fl_str_mv	bdigital@metabiblioteca.com
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spelling	Li, Beiwen2022-07-28 16:45:262025-05-21T19:15:45Z2022-07-28 16:45:262022-07-28https://hdl.handle.net/20.500.12585/13505https://doi.org/10.32397/tesea.vol3.n2.49010.32397/tesea.vol3.n2.4902745-0120This paper presents examples of high-speed 3D optical sensing for research and applications in the manufacturing community. Specifically, this paper will focus on the fringe projection technique as a special technology that can be extremely beneficial to manufacturing applications, given its merits of simultaneous high-speed and high-accuracy 3D surface measurements. This paper will introduce the basic principles of 3D optical sensing based on the fringe projection technique as well as the enabled manufacturing research applications, including both in-situ/in-process monitoring and post-process quality assurance.application/pdfengUniversidad Tecnológica de BolívarBeiwen Li - 2022https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/490High-speed 3D optical sensingfringe projectionmanufacturingin-situ monitoringpost-process quality evaluationHigh-speed 3D optical sensing for manufacturing research and industrial sensing applicationsHigh-speed 3D optical sensing for manufacturing research and industrial sensing applicationsArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Arkadeep Kumar. Methods and materials for smart manufacturing: additive manufacturing, internet of things, flexible sensors and soft robotics. Manufacturing Letters, 15:122–125, 2018. [2] Yuhang Yang, Zhiqiao Dong, Yuquan Meng, and Chenhui Shao. Data-driven intelligent 3d surface measurement in smart manufacturing: review and outlook. Machines, 9(1):13, 2021. [3] Longyu Zhang, Haiwei Dong, and Abdulmotaleb El Saddik. From 3d sensing to printing: A survey. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 12(2):1–23, 2015. [4] QMJ Qu, MF Ricky Lee, and Clarence W de Silva. Intelligent 3d sensing in automated manufacturing processes. In 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, volume 1, pp. 366–370. IEEE, 2001. [5] Kevin Harding. Handbook of optical dimensional metrology. CRC Press, 2013. [6] Toru Yoshizawa. Handbook of optical metrology: Principles and Applications. CRC press, 2009. [7] W Gao, H Haitjema, FZ Fang, RK Leach, CF Cheung, E Savio, and Jean-Marc Linares. On-machine and in-process surface metrology for precision manufacturing. CIRP Annals, 68(2):843–866, 2019. [8] Sarah K Everton, Matthias Hirsch, Petros Stravroulakis, Richard K Leach, and Adam T Clare. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Materials & Design, 95:431–445, 2016. [9] Sai Siva Gorthi and Pramod Rastogi. Fringe projection techniques: whither we are? Optics and Lasers in Engineering, 48(2):133–140, 2010. [10] Jason Geng. Structured-light 3d surface imaging: a tutorial. Advances in Optics and Photonics, 3(2):128–160, 2011. [11] Andres G Marrugo, Feng Gao, and Song Zhang. State-of-the-art active optical techniques for three-dimensional surface metrology: a review [invited]. Journal of the Optical Society of America A, 37(9):B60–B77, 2020. [12] Samuel Tolansky. An introduction to interferometry. Longman, 1973. [13] James C Wyant. White light interferometry. In Holography: A Tribute to Yuri Denisyuk and Emmett Leith, volume 4737, pp. 98–107. SPIE, 2002. [14] James C Wyant. Interferometric optical metrology: Basic principles and new systems. Laser Focus, USA, 18(5):65–71, 1982. [15] Andreas Kolb, Erhardt Barth, Reinhard Koch, and Rasmus Larsen. Time-of-flight cameras in computer graphics. Computer Graphics Forum, 29(1):141–159, 2010. [16] Filiberto Chiabrando, Roberto Chiabrando, Dario Piatti, and Fulvio Rinaudo. Sensors for 3d imaging: Metric evaluation and calibration of a ccd/cmos time-of-flight camera. Sensors, 9(12):10080–10096, 2009. [17] Robert Lange and Peter Seitz. Solid-state time-of-flight range camera. IEEE Journal of Quantum Electronics, 37(3):390–397, 2001. [18] Yan Cui, Sebastian Schuon, Derek Chan, Sebastian Thrun, and Christian Theobalt. 3d shape scanning with a time-of-flight camera. In Proc. IEEE on CVPR, pp. 1173–1180. IEEE, 2010. [19] Carlo Dal Mutto, Pietro Zanuttigh, and Guido M Cortelazzo. Time-of-flight cameras and Microsoft Kinect (TM). Springer Science & Business Media, 2012. [20] S Parthasarathy, J Birk, and J Dessimoz. Laser rangefinder for robot control and inspection. In Robot Vision, volume 336, pp. 2–11. SPIE, 1982. [21] G Bickel, G Hausler, and M Maul. Triangulation with expanded range of depth. Optical Engineering, 24(6):246975, 1985. [22] João Guilherme DM França, Mário A Gazziro, Alessandro Noriaki Ide, and José Hiroki Saito. A 3d scanning system based on laser triangulation and variable field of view. In IEEE International Conference on Image Processing 2005, volume 1, pp. I–425. IEEE, 2005. [23] Rainer G Dorsch, Gerd Häusler, and Jürgen M Herrmann. Laser triangulation: fundamental uncertainty in distance measurement. Applied Optics, 33(7):1306–1314, 1994. [24] Umesh R Dhond and Jake K Aggarwal. Structure from stereo-a review. IEEE transactions on systems, man, and cybernetics, 19(6):1489–1510, 1989. [25] Daniel Scharstein and Richard Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1):7–42, 2002. [26] Joaquim Salvi, Jordi Pages, and Joan Batlle. Pattern codification strategies in structured light systems. Pattern Recognition, 37(4):827–849, 2004. [27] Song Zhang. Recent progresses on real-time 3-d shape measurement using digital fringe projection techniques. Optics and Lasers in Engineering, 48(2):149–158, 2010. [28] Sam Van der Jeught, Joris AM Soons, and Joris JJ Dirckx. Real-time microscopic phase-shifting profilometry. Applied Optics, 54(15):4953–4959, 2015. [29] Nikolaus Karpinsky, Morgan Hoke, Vincent Chen, and Song Zhang. High-resolution, real-time three-dimensional shape measurement on graphics processing unit. Optical Engineering, 53(2):024105, 2014. [30] Song Zhang. High-speed 3D imaging with digital fringe projection techniques. CRC Press, 2018. [31] Mitsuo Takeda, Hideki Ina, and Seiji Kobayashi. Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry. Journal of the Optical Society of America A, 72(1):156–160, 1982. [32] Daniel Malacara. Optical shop testing, volume 59. John Wiley & Sons, 2007. [33] Song Zhang. Absolute phase retrieval methods for digital fringe projection profilometry: A review. Optics and Lasers in Engineering, 107:28–37, 2018. [34] Chao Zuo, Lei Huang, Minliang Zhang, Qian Chen, and Anand Asundi. Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review. Optics and Lasers in Engineering, 85:84–103, 2016. [35] Beiwen Li, Nikolaus Karpinsky, and Song Zhang. Novel calibration method for structured-light system with an out-of-focus projector. Applied optics, 53(16):3415–3426, 2014. [36] Shuangyan Lei and Song Zhang. Flexible 3-d shape measurement using projector defocusing. Optics Letters, 34(20):3080–3082, 2009. [37] Shijie Feng, Liang Zhang, Chao Zuo, Tianyang Tao, Qian Chen, and Guohua Gu. High dynamic range 3d measurements with fringe projection profilometry: a review. Measurement Science and Technology, 29(12):122001, 2018. [38] Hui Lin, Jian Gao, Guanjin Zhang, Xin Chen, Yunbo He, and Yan Liu. Review and comparison of high-dynamic range three-dimensional shape measurement techniques. Journal of Sensors, 2017, 2017. [39] Yue Liu, Liam Blunt, Feng Gao, and Xiangqian Jiang. High-dynamic-range 3d measurement for e-beam fusion additive manufacturing based on svm intelligent fringe projection system. Surface Topography: Metrology and Properties, 9(3):034002, 2021. [40] Liang Zhang, Qian Chen, Chao Zuo, and Shijie Feng. High-speed high dynamic range 3d shape measurement based on deep learning. Optics and Lasers in Engineering, 134:106245, 2020. [41] Bin Zhang, John Ziegert, Faramarz Farahi, and Angela Davies. In situ surface topography of laser powder bed fusion using fringe projection. Additive Manufacturing, 12:100–107, 2016. [42] Andrew Dickins, Taufiq Widjanarko, Danny Sims-Waterhouse, Adam Thompson, Simon Lawes, Nicola Senin, and Richard Leach. Multi-view fringe projection system for surface topography measurement during metal powder bed fusion. Journal of the Optical Society of America A, 37(9):B93–B105, 2020. [43] Haolin Zhang, Chaitanya Krishna Prasad Vallabh, Yubo Xiong, and Xiayun Zhao. A systematic study and framework of fringe projection profilometry with improved measurement performance for in-situ lpbf process monitoring. Measurement, 191:110796, 2022. [44] Zhongwei Li, Xingjian Liu, Shifeng Wen, Piyao He, Kai Zhong, Qingsong Wei, Yusheng Shi, and Sheng Liu. In situ 3d monitoring of geometric signatures in the powder-bed-fusion additive manufacturing process via vision sensing methods. Sensors, 18(4):1180, 2018. [45] Xiao Zhang, Weijun Shen, Vignesh Suresh, Jakob Hamilton, Li-Hsin Yeh, Xuepeng Jiang, Zhan Zhang, Qing Li, Beiwen Li, Iris V Rivero, et al. In situ monitoring of direct energy deposition via structured light system and its application in remanufacturing industry. The International Journal of Advanced Manufacturing Technology, 116(3):959–974, 2021. [46] Giovanna Sansoni, Matteo Carocci, and Roberto Rodella. Three-dimensional vision based on a combination of gray-code and phase-shift light projection: analysis and compensation of the systematic errors. Applied Optics, 38(31):6565–6573, 1999. [47] Beiwen Li and Song Zhang. Flexible calibration method for microscopic structured light system using telecentric lens. Optics Express, 23(20):25795–25803, 2015. [48] Kwangwoo Wi, Vignesh Suresh, Kejin Wang, Beiwen Li, and Hantang Qin. Quantifying quality of 3d printed clay objects using a 3d structured light scanning system. Additive Manufacturing, 32:100987, 2020.Transactions on Energy Systems and Engineering Applications3112https://revistas.utb.edu.co/tesea/article/download/490/371Núm. 2 , Año 2022 : Transactions on Energy Systems and Engineering Applications220.500.12585/13505oai:repositorio.utb.edu.co:20.500.12585/135052025-05-21 14:15:45.746https://creativecommons.org/licenses/by/4.0Beiwen Li - 2022metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com

High-speed 3D optical sensing for manufacturing research and industrial sensing applications

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