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Computer vision in manufacturing: a bibliometric analysis and future research propositions

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Abstract

Computer vision for the past two decades has been used to simulate human capabilities and automate tasks, and in the process, has benefited all of us. Specifically, its application within the manufacturing context has garnered ample attention and interest from both academics and practitioners. Due to its large-scale applicability and adoption potential, extensive research has been conducted to understand and appreciate it is working. However, extant research in this domain is rather disjointed, thereby delimiting the otherwise vast scope and knowledge boundaries. Thus, this study utilizes bibliometric analysis to synthesize extant literature within this field to address this lacuna. We analyzed 897 articles from Scopus, entailing contributions from 309 journals, 108 countries, 2138 authors, and 1334 organizations from 1981 to 2022. Additionally, we analyzed citation and co-authorship networks to acknowledge prominent authors, organizations, and countries within this domain. The thematic classification of extant literature through bibliographic coupling identified five major thematic areas: automated visual inspection, object tracking and process controlling, real-time monitoring, roughness inspection, and profile projection. Importantly, we used both knowledge and insights from our findings, and propose scope for future research.

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References

  1. Aghajan HK, Kailath T (1993) Sensor array processing techniques for super resolution multi-line-fitting and straight edge detection. IEEE Trans Image Process 2(4):454–465. https://doi.org/10.1109/83.242355

    Article  Google Scholar 

  2. Aminzadeh M, Kurfess TR (2019) Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images. J Intell Manuf 30(6):2505–2523. https://doi.org/10.1007/s10845-018-1412-0

    Article  Google Scholar 

  3. Arents J, Greitans M (2022) Smart industrial robot control trends, challenges and opportunities within manufacturing. Appl Sci 12(2):937. https://doi.org/10.3390/app12020937

    Article  Google Scholar 

  4. Badmos O, Kopp A, Bernthaler T, Schneider G (2020) Image-based defect detection in lithium-ion battery electrode using convolutional neural networks. J Intell Manuf 31(4):885–897. https://doi.org/10.1007/s10845-019-01484-x

    Article  Google Scholar 

  5. Barua S, Liou F, Newkirk J, Sparks T (2014) Vision-based defect detection in laser metal deposition process. Rapid Prototyp J 20(1):77–86. https://doi.org/10.1108/RPJ-04-2012-0036

    Article  Google Scholar 

  6. Benkő P, Martin RR, Várady T (2001) Algorithms for reverse engineering boundary representation models. Comput Aided Des 33(11):839–851. https://doi.org/10.1016/S0010-4485(01)00100-2

    Article  Google Scholar 

  7. Bhat NN, Kumari K, Dutta S, Pal SK, Pal S (2015) Friction stirs weld classification by applying wavelet analysis and support vector machine on weld surface images. J Manuf Process 20:274–281. https://doi.org/10.1016/j.jmapro.2015.07.002

    Article  Google Scholar 

  8. Bhatt Y, Ghuman K, Dhir A (2020) Sustainable manufacturing. Bibliometrics and content analysis. J Clean Prod 260:120988. https://doi.org/10.1016/j.jclepro.2020.120988

    Article  Google Scholar 

  9. Bhuyan A, Sanguri K, Sharma H (2021) Improving the keyword co-occurrence analysis: an integrated semantic similarity approach. IEEE International Conference on Industrial Engineering and Engineering Management, pp 482–487. https://doi.org/10.1109/IEEM50564.2021.9673030

  10. Bi ZM, Kang B (2014) Sensing and responding to the changes of geometric surfaces in flexible manufacturing and assembly. Enterp Inf Syst 8(2):225–245. https://doi.org/10.1080/17517575.2012.654826

    Article  Google Scholar 

  11. Brosnan T, Sun DW (2004) Improving quality inspection of food products by computer vision––a review. J Food Eng 61(1):3–16. https://doi.org/10.1016/S0260-8774(03)00183-3

    Article  Google Scholar 

  12. Carbone V, Carocci M, Savio E, Sansoni G, De Chiffre L (2001) Combination of a vision system and a coordinate measuring machine for the reverse engineering of freeform surfaces. Int J Adv Manuf Technol 17(4):263–271. https://doi.org/10.1007/s001700170179

    Article  Google Scholar 

  13. Castillo-Vergara M, Alvarez-Marin A, Placencio-Hidalgo D (2018) A bibliometric analysis of creativity in the field of business economics. J Bus Res 85:1–9. https://doi.org/10.1016/j.jbusres.2017.12.011

    Article  Google Scholar 

  14. Caviggioli F, Ughetto E (2019) A bibliometric analysis of the research dealing with the impact of additive manufacturing on industry, business and society. Int J Prod Econ 208:254–268. https://doi.org/10.1016/j.ijpe.2018.11.022

    Article  Google Scholar 

  15. Chan VH, Bradley C, Vickers GW (2001) A multi-sensor approach to automating co-ordinate measuring machine-based reverse engineering. Comput Ind 44(2):105–115. https://doi.org/10.1016/S0166-3615(00)00087-7

    Article  Google Scholar 

  16. Chen F, Selvaggio M, Member S, Caldwell DG (2018) Dexterous grasping with manipulability selection using an industrial mobile manipulator with visual guidance. IEEE Trans Ind Inf 3203(c):1–9

    Google Scholar 

  17. Chen MC (2002) Roundness measurements for discontinuous perimeters via machine visions. Comput Ind 47(2):185–197. https://doi.org/10.1016/S0166-3615(01)00143-9

    Article  Google Scholar 

  18. Chen H, Pang Y, Hu Q, Liu K (2020) Solar cell surface defect inspection based on multispectral convolutional neural network. J Intell Manuf 31(2):453–468. https://doi.org/10.1007/s10845-018-1458-z

    Article  Google Scholar 

  19. Cheng Y, Jafari MA (2008) Vision-based online process control in manufacturing applications. IEEE Trans Autom Sci Eng 5(1):140–153. https://doi.org/10.1109/TASE.2007.912058

    Article  Google Scholar 

  20. Chiu V, Liu Q, Muehlmann B, Baldwin AA (2019) A bibliometric analysis of accounting information systems journals and their emerging technologies contributions. Int J Account Inf Syst 32:24–43. https://doi.org/10.1016/j.accinf.2018.11.003

    Article  Google Scholar 

  21. Chou PB, Rao AR, Sturzenbecker MC, Wu FY, Brecher VH (1997) Automatic defect classification for semiconductor manufacturing. Mach Vis Appl 9(4):201–214. https://doi.org/10.1007/s001380050041

    Article  Google Scholar 

  22. Cicirello VA, Regli WC (2002) An approach to a feature-based comparison of solid models of machined parts. Artif Intell Eng Des Anal Manuf: AIEDAM 16(5):385–399. https://doi.org/10.1017/S0890060402165048

    Article  Google Scholar 

  23. Colosimo BM, Grasso M (2018) Spatially weighted PCA for monitoring video image data with application to additive manufacturing. J Qual Technol 50(4):391–417. https://doi.org/10.1080/00224065.2018.1507563

    Article  Google Scholar 

  24. Conci A, Proença CB (1998) A fractal image analysis system for fabric inspection based on a box-counting method. Comput Netw ISDN Syst 30(20–21):1887–1895. https://doi.org/10.1016/S0169-7552(98)00211-6

    Article  Google Scholar 

  25. Couto J (2020) An Introductory Guide to Computer Vision | Tryolabs Resources. https://tryolabs.com/resources/introductory-guide-computer-vision/. Accessed 18 March 2022

  26. Danvila-del-Valle I, Estévez-Mendoza C, Lara FJ (2019) Human resources training: a bibliometric analysis. J Bus Res 101:627–636. https://doi.org/10.1016/j.jbusres.2019.02.026

    Article  Google Scholar 

  27. Dom BE, Brecher V (1995) Recent advances in the automatic inspection of integrated circuits for pattern defects. Mach Vis Appl 8(1):5–19. https://doi.org/10.1007/BF01213634

    Article  Google Scholar 

  28. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res 133:285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

    Article  Google Scholar 

  29. Eck NJV, Waltman L (2014) Visualizing bibliometric networks. In: Measuring scholarly impact. Springer, Cham, pp 285–320

  30. Edinbarough I, Balderas R, Bose S (2005) A vision and robot based on-line inspection monitoring system for electronic manufacturing. Comput Ind 56(8–9):986–996. https://doi.org/10.1016/j.compind.2005.05.022

    Article  Google Scholar 

  31. Ekanayake B, Wong JKW, Fini AAF, Smith P (2021) Computer vision-based interior construction progress monitoring: a literature review and future research directions. Autom Constr 127:103705. https://doi.org/10.1016/j.autcon.2021.103705

    Article  Google Scholar 

  32. Fahimnia B, Sarkis J, Davarzani H (2015) Green supply chain management: a review and bibliometric analysis. Int J Prod Econ 162:101–114. https://doi.org/10.1016/j.ijpe.2015.01.003

    Article  Google Scholar 

  33. Faugeras O, Robert L, Laveau S, Csurka G, Zeller C, Gauclin C, Zoghlami I (1998) 3-d reconstruction of urban scenes from image sequences. Comput Vis Image Underst 69(3):292–309. https://doi.org/10.1006/cviu.1998.0665

    Article  Google Scholar 

  34. Feng C, Xiao Y, Willette A, McGee W, Kamat VR (2015) Vision guided autonomous robotic assembly and as-built scanning on unstructured construction sites. Autom Constr 59:128–138. https://doi.org/10.1016/j.autcon.2015.06.002

    Article  Google Scholar 

  35. Ferreira FA (2018) Mapping the field of arts-based management: bibliographic coupling and co-citation analyses. J Bus Res 85:348–357. https://doi.org/10.1016/j.jbusres.2017.03.026

    Article  Google Scholar 

  36. Franceschini F, Maisano D, Mastrogiacomo L (2016) Empirical analysis and classification of database errors in Scopus and Web of Science. J Informet 10(4):933–953. https://doi.org/10.1016/j.joi.2016.07.003

    Article  Google Scholar 

  37. Ghosal S, Mehrotra R (1993) Segmentation of range images: an orthogonal moment-based integrated approach. IEEE Trans Robot Autom 9(4):385–399. https://doi.org/10.1109/70.246050

    Article  Google Scholar 

  38. Grasso M, Laguzza V, Semeraro Q, Colosimo BM (2017) In-process monitoring of selective laser melting: spatial detection of defects via image data analysis. J Manuf Sci Eng 139(5). https://doi.org/10.1115/1.4034715

  39. Guerra E, Villalobos JR (2001) A three-dimensional automated visual inspection system for SMT assembly. Comput Ind Eng 40(1–2):175–190. https://doi.org/10.1016/S0360-8352(01)00016-X

    Article  Google Scholar 

  40. Gurzki H, Woisetschläger DM (2017) Mapping the luxury research landscape: a bibliometric citation analysis. J Bus Res 77:147–166. https://doi.org/10.1016/j.jbusres.2016.11.009

    Article  Google Scholar 

  41. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  42. He K, Zhang Q, Hong Y (2019) Profile monitoring-based quality control method for fused deposition modeling process. J Intell Manuf 30(2):947–958. https://doi.org/10.1007/s10845-018-1424-9

    Article  Google Scholar 

  43. Ho SY, Lee KC, Chen SS, Ho SJ (2002) Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Int J Mach Tools Manuf 42(13):1441–1446. https://doi.org/10.1016/S0890-6955(02)00078-0

    Article  Google Scholar 

  44. Hoang K, Wen W, Nachimuthu A, Jiang XL (1997) Achieving automation in leather surface inspection. Comput Ind 34(1):43–54. https://doi.org/10.1016/S0166-3615(97)00019-5

    Article  Google Scholar 

  45. Huang R, Gu J, Sun X, Hou Y, Uddin S (2019) A rapid recognition method for electronic components based on the improved YOLO-V3 network. Electronics 8(8):825. https://doi.org/10.3390/electronics8080825

    Article  Google Scholar 

  46. Iglesias C, Martínez J, Taboada J (2018) Automated vision system for quality inspection of slate slabs. Comput Ind 99:119–129. https://doi.org/10.1016/j.compind.2018.03.030

    Article  Google Scholar 

  47. Iowa State University library, Scopus: Comparison 2022. https://instr.iastate.libguides.com/comparisons. Accessed 18 May 2022

  48. Javaid M, Haleem A, Singh RP, Rab S, Suman R (2022) Exploring impact and features of machine vision for progressive industry 4.0 culture. Sensors Int 3:100132. https://doi.org/10.1016/j.sintl.2021.100132

    Article  Google Scholar 

  49. Jian C, Gao J, Ao Y (2017) Automatic surface defect detection for mobile phone screen glass based on machine vision. Appl Soft Comput 52:348–358. https://doi.org/10.1016/j.asoc.2016.10.030

    Article  Google Scholar 

  50. Jin Z, Zhang Z, Gu GX (2019) “Autonomous in-situ correction of fused deposition modelling printers using computer vision and deep learning. Manuf Lett 22:11–15. https://doi.org/10.1016/j.mfglet.2019.09.005

    Article  Google Scholar 

  51. Kamguem R, Tahan SA, Songmene V (2013) Evaluation of machined part surface roughness using image texture gradient factor. Int J Precis Eng Manuf 14(2):183–190. https://doi.org/10.1007/s12541-013-0026-x

    Article  Google Scholar 

  52. Kerr D, Pengilley J, Garwood R (2006) Assessment and visualisation of machine tool wear using computer vision. Int J Adv Manuf Technol 28(7):781–791. https://doi.org/10.1007/s00170-004-2420-0

    Article  Google Scholar 

  53. Kessler MM (1963) Bibliographic coupling between scientific papers. Am Doc 14(1):10–25. https://doi.org/10.1002/asi.5090140103

    Article  Google Scholar 

  54. Khalaj BH, Aghajan HK, Kailath T (1994) Patterned wafer inspection by high resolution spectral estimation techniques. Mach Vis Appl 7(3):178–185. https://doi.org/10.1007/BF01211662

    Article  Google Scholar 

  55. Khanra S, Dhir A, Mäntymäki M (2020) Big data analytics and enterprises: a bibliometric synthesis of the literature. Enterp Inf Syst 14(6):737–768. https://doi.org/10.1080/17517575.2020.1734241

    Article  Google Scholar 

  56. Khanra S, Dhir A, Kaur P, Mäntymäki M (2021) Bibliometric analysis and literature review of ecotourism: toward sustainable development. Tour Manag Perspect 37:100777. https://doi.org/10.1016/j.tmp.2020.100777

    Article  Google Scholar 

  57. Khanra S, Dhir A, Parida V, Kohtamäki M (2021) Servitization research: a review and bibliometric analysis of past achievements and future promises. J Bus Res 131:151–166. https://doi.org/10.1016/j.jbusres.2021.03.056

    Article  Google Scholar 

  58. Kim B, Kang H, Kim DH, Park JO (2006) A flexible microassembly system based on hybrid manipulation scheme for manufacturing photonics components. Int J Adv Manuf Technol 28(3):379–386. https://doi.org/10.1007/s00170-004-2360-8

    Article  Google Scholar 

  59. Krishnan B, Vijayan V, Parameshwaran Pillai T, Sathish T (2019) Influence of surface roughness in turning process—an analysis using artificial neural network. Trans Can Soc Mech Eng 43(4):509–514. https://doi.org/10.1139/tcsme-2018-0255

    Article  Google Scholar 

  60. Lee JY, Yoo SI (2004) Automatic detection of region-mura defect in TFT-LCD. IEICE Trans Inf Syst 87(10):2371–2378

    Google Scholar 

  61. Lee BY, Yu SF, Juan H (2004) The model of surface roughness inspection by vision system in turning. Mechatronics 14(1):129–141. https://doi.org/10.1016/S0957-4158(02)00096-X

    Article  Google Scholar 

  62. Lee KC, Ho SJ, Ho SY (2005) Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system. Precis Eng 29(1):95–100. https://doi.org/10.1016/j.precisioneng.2004.05.002

    Article  Google Scholar 

  63. Li D, Liang LQ, Zhang WJ (2014) Defect inspection and extraction of the mobile phone cover glass based on the principal components analysis. Int J Adv Manuf Technol 73(9):1605–1614. https://doi.org/10.1007/s00170-014-5871-y

    Article  Google Scholar 

  64. Lian J, Jia W, Zareapoor M, Zheng Y, Luo R, Jain DK, Kumar N (2019) “Deep-learning-based small surface defect detection via an exaggerated local variation-based generative adversarial network. IEEE Trans Industr Inf 16(2):1343–1351. https://doi.org/10.1109/TII.2019.2945403

    Article  Google Scholar 

  65. Liu JJ, MacGregor JF (2006) Estimation and monitoring of product aesthetics: application to manufacturing of “engineered stone” countertops. Mach Vis Appl 16(6):374–383. https://doi.org/10.1007/s00138-005-0009-8

    Article  Google Scholar 

  66. Liu Y, Huang S, Zhang Z, Gao N, Gao F, Jiang X (2017) Full-field 3D shape measurement of discontinuous specular objects by direct phase measuring deflectometry. Sci Rep 7(1):1–8. https://doi.org/10.1038/s41598-017-11014-5

    Article  Google Scholar 

  67. Market and Market (2022) Machine vision market by compotent (hardware, Software), deployment (general, robotic cells), product (PC-based machine vision system, smart camera-based machine vision system), end-user industry, region -2030. https://www.marketsandmarkets.com/Market-Reports/industrial-machine-vision-market-234246734.html. Accessed 17 June 2022

  68. Martinez P, Ahmad R, Al-Hussein M (2019) A vision-based system for pre-inspection of steel frame manufacturing. Autom Constr 97:151–163. https://doi.org/10.1016/j.autcon.2018.10.021

    Article  Google Scholar 

  69. Martinez P, Al-Hussein M, Ahmad R (2019) A scientometric analysis and critical review of computer vision applications for construction. Autom Constr 107:102947. https://doi.org/10.1016/j.autcon.2019.102947

    Article  Google Scholar 

  70. Martyn J (1964) Bibliographic coupling. J Document 20(4):236–236. https://doi.org/10.1108/eb026352

    Article  Google Scholar 

  71. Medina R, Gayubo F, González-Rodrigo LM, Olmedo D, Gómez-García-Bermejo J, Zalama E, Perán JR (2011) Automated visual classification of frequent defects in flat steel coils. Int J Adv Manuf Technol 57(9):1087–1097. https://doi.org/10.1007/s00170-011-3352-0

    Article  Google Scholar 

  72. Merigó JM, Muller C, Modak NM, Laengle S (2019) Research in production and operations management: a university-based bibliometric analysis. Glob J Flex Syst Manag 20(1):1–29. https://doi.org/10.1007/s40171-018-0201-0

    Article  Google Scholar 

  73. Milovanovic B, Djekic I, Djordjevic V, Tomovic V, Barba F, Tomasevic I, Lorenzo JM (2019) Pros and cons of using a computer vision system for color evaluation of meat and meat products. IOP Conference Series. Earth and Environmental Science 333(1):012008

    Google Scholar 

  74. Moganti M, Ercal F, Dagli CH, Tsunekawa S (1996) Automatic PCB inspection algorithms: a survey. Comput Vis Image Underst 63(2):287–313. https://doi.org/10.1006/cviu.1996.0020

    Article  Google Scholar 

  75. Molleda J, Usamentiaga R, García DF, Bulnes FG, Espina A, Dieye B, Smith LN (2013) An improved 3D imaging system for dimensional quality inspection of rolled products in the metal industry. Comput Ind 64(9):1186–1200. https://doi.org/10.1016/j.compind.2013.05.002

    Article  Google Scholar 

  76. Molleda J, Usamentiaga R, Millara ÁF, García DF, Manso P, Suárez CM, García I (2016) A profile measurement system for rail quality assessment during manufacturing. IEEE Trans Ind Appl 52(3):2684–2692. https://doi.org/10.1109/TIA.2016.2524459

    Article  Google Scholar 

  77. Mongeon P, Paul-Hus A (2016) The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106(1):213–228. https://doi.org/10.1007/s11192-015-1765-5

    Article  Google Scholar 

  78. Monostori L, Kádár B, Bauernhansl T, Kondoh S, Kumara S, Reinhart G, Ueda K (2016) Cyber-physical systems in manufacturing. CIRP Ann 65(2):621–641. https://doi.org/10.1016/j.cirp.2016.06.005

    Article  Google Scholar 

  79. Mostafa K, Hegazy T (2021) Review of image-based analysis and applications in construction. Autom Constr 122:103516. https://doi.org/10.1016/j.autcon.2020.103516

    Article  Google Scholar 

  80. Niñerola A, Sánchez-Rebull MV, Hernández-Lara AB (2019) Tourism research on sustainability: a bibliometric analysis. Sustainability 11(5):1377. https://doi.org/10.3390/su11051377

    Article  Google Scholar 

  81. Nobanee H, Al Hamadi FY, Abdulaziz FA, Abukarsh LS, Alqahtani AF, AlSubaey SK, Almansoori HA (2021) A bibliometric analysis of sustainability and risk management. Sustainability 13(6):3277. https://doi.org/10.3390/su13063277

    Article  Google Scholar 

  82. Palani S, Natarajan U (2011) Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform. Int J Adv Manuf Technol 54(9):1033–1042. https://doi.org/10.1007/s00170-010-3018-3

    Article  Google Scholar 

  83. Penumuru DP, Muthuswamy S, Karumbu P (2020) Identification and classification of materials using machine vision and machine learning in the context of industry 4.0. J Intell Manuf 31(5):1229–1241. https://doi.org/10.1007/s10845-019-01508-6

    Article  Google Scholar 

  84. Peres RS, Jia X, Lee J, Sun K, Colombo AW, Barata J (2020) Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE Access 8:220121–220139

    Article  Google Scholar 

  85. Pottmann H, Leopoldseder S, Hofer M, Steiner T, Wang W (2005) Industrial geometry: recent advances and applications in CAD. Comput Aided Des 37(7):751–766. https://doi.org/10.1016/j.cad.2004.08.013

    Article  Google Scholar 

  86. Rao AR (1996) Future directions in industrial machine vision: a case study of semiconductor manufacturing applications. Image Vis Comput 14(1):3–19. https://doi.org/10.1016/0262-8856(95)01035-1

    Article  Google Scholar 

  87. Schwarz S, Sjöström M, Olsson R (2013) A weighted optimization approach to time-of-flight sensor fusion. IEEE Trans Image Process 23(1):214–225. https://doi.org/10.1109/TIP.2013.2287613

    Article  MathSciNet  MATH  Google Scholar 

  88. Smith ML (1999) The analysis of surface texture using photometric stereo acquisition and gradient space domain mapping. Image Vis Comput 17(14):1009–1019. https://doi.org/10.1016/S0262-8856(99)00003-7

    Article  Google Scholar 

  89. Smith LN, Smith ML (2005) Automatic machine vision calibration using statistical and neural network methods. Image Vis Comput 23(10):887–899. https://doi.org/10.1016/j.imavis.2005.03.009

    Article  Google Scholar 

  90. Soosaraei M, Khasseh AA, Fakhar M, Hezarjaribi HZ (2018) A decade bibliometric analysis of global research on leishmaniasis in Web of Science database. Ann Med Surg 26:30–37. https://doi.org/10.1016/j.amsu.2017.12.014

    Article  Google Scholar 

  91. Steger C, Ulrich M, Wiedemann C (2018) Machine vision algorithms and applications. Wiley

    Google Scholar 

  92. Szeliski R (2010) Computer vision: algorithms and applications, Springer Science & Business Media

  93. Tandon A, Kaur P, Mäntymäki M, Dhir A (2021) Blockchain applications in management: a bibliometric analysis and literature review. Technol Forecast Soc Chang 166:120649. https://doi.org/10.1016/j.techfore.2021.120649

    Article  Google Scholar 

  94. Taylor P, Sivakumar S, Dhanalakshmi V (2015) International Journal of Computer Integrated Manufacturing An approach towards the integration of CAD / CAM / CAI through STEP file using feature extraction for cylindrical parts, No. March 2015, pp 37–41

  95. Thompson WB, Owen JC, Germain HDS, Stark SR, Henderson TC (1999) Feature-based reverse engineering of mechanical parts. IEEE Trans Robot Autom 15(1):57–66. https://doi.org/10.1109/70.744602

    Article  Google Scholar 

  96. Tian X, Geng Y, Zhong S, Wilson J, Gao C, Chen W, Hao H (2018) A bibliometric analysis on trends and characters of carbon emissions from transport sector. Transp Res Part D: Transp Environ 59:1–10. https://doi.org/10.1016/j.trd.2017.12.009

    Article  Google Scholar 

  97. Tsai DM, Tsai HY (2011) Low-contrast surface inspection of mura defects in liquid crystal displays using optical flow-based motion analysis. Mach Vis Appl 22(4):629–649. https://doi.org/10.1007/s00138-010-0256-1

    Article  Google Scholar 

  98. Tsai DM, Wu SK (2000) Automated surface inspection using Gabor filters. Int J Adv Manuf Technol 16(7):474–482. https://doi.org/10.1007/s001700070055

    Article  Google Scholar 

  99. Tsai DM, Chang CC, Chao SM (2010) Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion. Image Vis Comput 28(3):491–501. https://doi.org/10.1016/j.imavis.2009.08.001

    Article  Google Scholar 

  100. Tsang CS, Ngan HY, Pang GK (2016) Fabric inspection based on the Elo rating method. Patt Recogn 51:378–394. https://doi.org/10.1016/j.patcog.2015.09.022

    Article  Google Scholar 

  101. Tsarouchi P, Matthaiakis SA, Michalos G, Makris S, Chryssolouris G (2016) A method for detection of randomly placed objects for robotic handling. CIRP J Manuf Sci Technol 14:20–27. https://doi.org/10.1016/j.cirpj.2016.04.005

    Article  Google Scholar 

  102. Vedula SB, Agrawal RK (2023) Mapping spiritual leadership: a bibliometric analysis and synthesis of past milestones and future research agenda. J Bus Ethics 1–28. https://doi.org/10.1007/s10551-023-05346-8

  103. Vogel B, Reichard RJ, Batistič S, Černe M (2021) A bibliometric review of the leadership development field: how we got here, where we are, and where we are headed. Leader Q 32(5):101381. https://doi.org/10.1016/j.leaqua.2020.101381

    Article  Google Scholar 

  104. Wang M, Liu Y, Su D, Liao Y, Shi L, Xu J, Miro JV (2018) Accurate and real-time 3-D tracking for the following robots by fusing vision and ultrasonar information. IEEE/ASME Trans Mechatron 23(3):997–1006. https://doi.org/10.1109/TMECH.2018.2820172

    Article  Google Scholar 

  105. Wuest T, Weimer D, Irgens C, Thoben KD (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4(1):23–45. https://doi.org/10.1080/21693277.2016.1192517

    Article  Google Scholar 

  106. Xie WF, Li Z, Tu XW, Perron C (2009) Switching control of image-based visual servoing with laser pointer in robotic manufacturing systems. IEEE Trans Industr Electron 56(2):520–529. https://doi.org/10.1109/TIE.2008.2003217

    Article  Google Scholar 

  107. Xu X, Chen X, Jia F, Brown S, Gong Y, Xu Y (2018) Supply chain finance: a systematic literature review and bibliometric analysis. Int J Prod Econ 204:160–173. https://doi.org/10.1016/j.ijpe.2018.08.003

    Article  Google Scholar 

  108. Xuan Q, Chen Z, Liu Y, Huang H, Bao G, Zhang D (2018) Multiview generative adversarial network and its application in pearl classification. IEEE Trans Industr Electron 66(10):8244–8252. https://doi.org/10.1109/TIE.2018.2885684

    Article  Google Scholar 

  109. Yang G, Gaines JA, Nelson BJ (2003) A supervisory wafer-level 3D microassembly system for hybrid MEMS fabrication. J Intell Rob Syst 37(1):43–68. https://doi.org/10.1023/A:1023982907874

    Article  Google Scholar 

  110. Yang G, Gaines JA, Nelson BJ (2005) Optomechatronic design of microassembly systems for manufacturing hybrid microsystems. IEEE Trans Industr Electron 52(4):1013–1023. https://doi.org/10.1109/TIE.2005.851665

    Article  Google Scholar 

  111. Zhang JM, Lin RM, Wang MJJ (1999) The development of an automatic post-sawing inspection system using computer vision techniques. Comput Ind 40(1):51–60. https://doi.org/10.1016/S0166-3615(99)00009-3

    Article  Google Scholar 

  112. Zhang X, Prajapati M, Peden E (2011) A stochastic production planning model under uncertain seasonal demand and market growth. Int J Prod Res 49(7):1957–1975. https://doi.org/10.1080/00207541003690074

    Article  Google Scholar 

  113. Zheng P, Sang Z, Zhong RY, Liu Y, Liu C, Mubarok K, Xu X (2018) Smart manufacturing systems for Industry 4.0: conceptual framework, scenarios, and future perspectives. Front Mech Eng 13(2):137–150. https://doi.org/10.1007/s11465-018-0499-5

    Article  Google Scholar 

  114. Zhong RY, Xu X, Klotz E, Newman ST (2017) Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5):616–630. https://doi.org/10.1016/J.ENG.2017.05.015

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology and Systems, Indian Institute of Management (IIM), Kashipur, India

    Himanshu Sharma & Harish Kumar

  2. Department of Management Studies, Graphic Era (Deemed to Be University), Dehradun, India

    Ashulekha Gupta

  3. Department of Economics, Kebri Dehar University, Kebri Dehar, 250, Somali, Ethiopia

    Mohd Asif Shah

  4. School of Business, Woxsen University, Kamkole, Sadasivpet, Telangana, 502345, Hyderabad, India

    Mohd Asif Shah

  5. Division of Research and Development, Lovely Professional University, Punjab, 144001, Phagwara, India

    Mohd Asif Shah

  6. School of Engineering and Technology, Sharda University, 201310, Greater Noida, India

    Mohd Asif Shah

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  1. Himanshu Sharma
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  2. Harish Kumar
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Contributions

Himanshu Sharma: conceptualization, software, validation, investigation, resources, data curation, writing—original draft, writing—review and editing, visualization, supervision, and project administration. Harish Kumar: conceptualization, writing—original draft, and writing—review and editing. Ashulekha Gupta: formal analysis and writing—review and editing. Mohd Asif Shah: validation and writing—review and editing.

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Correspondence to Mohd Asif Shah.

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Sharma, H., Kumar, H., Gupta, A. et al. Computer vision in manufacturing: a bibliometric analysis and future research propositions. Int J Adv Manuf Technol 127, 5691–5710 (2023). https://doi.org/10.1007/s00170-023-11907-y

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