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Erschienen in:
Evolution of Manufacturing and Its Journey Towards Digital Twin Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

4. Image Processing for Digital Twin

verfasst von : Surjya Kanta Pal, Debasish Mishra, Arpan Pal, Samik Dutta, Debashish Chakravarty, Srikanta Pal

Erschienen in: Digital Twin – Fundamental Concepts to Applications in Advanced Manufacturing

Verlag: Springer International Publishing

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Abstract

Improvement of product quality by reducing downtime is the ultimate aim in manufacturing. The product quality can be monitored from the process signals or images acquired at the time of manufacturing. For example, in machining, damage or wear of cutting tool causes degradation in product quality. This damage or wear can be monitored from the process signals such as force, acoustic emission, power, vibration etc. acquired during machining.

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Vorheriges Kapitel Signal Processing for Digital Twin
Nächstes Kapitel Data Communication-Edge, Fog, and Cloud Computing
Literatur
1.
Xu LM, Fan F, Zhang Z, Chen Y, Hu DJ, Shi L (2019) Methodology and implementation of a vision-oriented open CNC system for profile grinding. Int J Adv Manuf Technol 100(5):2123–2131 CrossRef
2.
Yan MT, Lin SS (2011) Process planning and electrode wear compensation for 3D micro-EDM. Int J Adv Manuf Technol 100(5):2123–2131
3.
Malayath G, Katta S, Sidpara AM, Deb S (2019) Length-wise tool wear compensation for micro electric discharge drilling of blind holes. Measurement 134:888–896 CrossRef
4.
Guo C, Qian J, Reynaerts D (2018) Deterministic removal strategy for machine vision assisted scanning micro electrochemical flow cell. J Manuf Process 34:167–178 CrossRef
5.
Jia G, Dong X, Huo Q, Wang K, Mei X (2018) Positioning and navigation system based on machine vision intended for laser-electrochemical micro-hole processing. Int J Adv Manuf Technol 94(1):1397–1410 CrossRef
6.
Rodriguez-Araujo J, Rodriguez-Andina JJ, Farina J, Vidal F, Mato JL, Montealegre MA (2012) Industrial laser cladding systems: FPGA-based adaptive control. IEEE Ind Electron Mag 6(4):35–46 CrossRef
7.
Hofman JT, Pathiraj B, Van Dijk J, De Lange DF, Meijer J (2012) A camera based feedback control strategy for the laser cladding process. J Mater Process Technol 212(11):2455–2462 CrossRef
8.
Moralejo S, Penaranda X, Nieto S, Barrios A, Arrizubieta I, Tabernero I, Figueras J (2017) A feedforward controller for tuning laser cladding melt pool geometry in real time. Int J Adv Manuf Technol 89(1–4):821–831 CrossRef
9.
Farshidianfar MH, Khajepour A, Gerlich A (2016) Real-time control of microstructure in laser additive manufacturing. Int J Adv Manuf Technol 82(5–8):1173–1186 CrossRef
10.
Wang JJ, Lin T, Chen SB (2005) Obtaining weld pool vision information during aluminium alloy TIG welding. Int J Adv Manuf Technol 26(3):219–227 CrossRef
11.
Ma H, Wei S, Lin T, Chen S, Li L (2010) Binocular vision system for both weld pool and root gap in robot welding process. Sens Rev
12.
Zhou L, Lin T, Chen SB (2006) Autonomous acquisition of seam coordinates for arc welding robot based on visual servoing. J Intell Rob Syst 47(3):239–255 CrossRef
13.
Na X, Zhang Y, Liu Y, Walcott B (2009) Nonlinear identification of laser welding process. IEEE Trans Control Syst Technol 18(4):927–934 CrossRef
14.
Ma H, Wei S, Sheng Z, Lin T, Chen S (2010) Robot welding seam tracking method based on passive vision for thin plate closed-gap butt welding. Int J Adv Manuf Technol 48(9–12):945–953 CrossRef
15.
Shen H, Lin T, Chen S, Li L (2010) Real-time seam tracking technology of welding robot with visual sensing. J Intell Rob Syst 59(3–4):283–298 MATHCrossRef
16.
Ye Z, Fang G, Chen S, Zou JJ (2013) Passive vision based seam tracking system for pulse-MAG welding. Int J Adv Manuf Technol 67(9–12):1987–1996 CrossRef
17.
Chen H, Liu K, Xing G, Dong Y, Sun H, Lin W (2014) A robust visual servo control system for narrow seam double head welding robot. Int J Adv Manuf Technol 71(9–12):1849–1860 CrossRef
18.
Pan E, Ye L, Shi J, Chang TS (2009) On-line bleeds detection in continuous casting processes using engineering-driven rule-based algorithm. J Manuf Sci Eng 131(6)
19.
Liu W, Jia Z, Wang F, Ma X, Wang W, Jia X, Song D (2012) An improved online dimensional measurement method of large hot cylindrical forging. Measurement 45(8):2041–2051 CrossRef
20.
Bračun D, Škulj G, Kadiš M (2017) Spectral selective and difference imaging laser triangulation measurement system for on line measurement of large hot workpieces in precision open die forging. Int J Adv Manuf Technol 90(1):917–926 CrossRef
21.
Agarwal K, Shivpuri R, Zhu Y, Chang TS, Huang H (2011) Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling. Expert Syst Appl 38(6):7251–7262 CrossRef
22.
Song K, Yan Y (2013) A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl Surf Sci 285:858–864 CrossRef
23.
Luo Q, He Y (2016) A cost-effective and automatic surface defect inspection system for hot-rolled flat steel. Robot Comput-Integr Manuf 38:16–30 CrossRef
24.
Zhao YJ, Yan YH, Song KC (2017) Vision-based automatic detection of steel surface defects in the cold rolling process: considering the influence of industrial liquids and surface textures. Int J Adv Manuf Technol 90(5–8):1665–1678 CrossRef
25.
Fischer A, Stöbener D (2019) In-process roughness quality inspection for metal sheet rolling. CIRP Ann 68(1):523–526 CrossRef
26.
ISO 3685: 1993(E), Tool life testing with single point turning tools
27.
Dutta S, Pal SK, Mukhopadhyay S, Sen R (2013) Application of digital image processing in tool condition monitoring: a review. CIRP J Manuf Sci Technol 6:212–232 CrossRef
28.
Dutta S, Pal SK, Sen R (2014) Digital image processing in machining. In: Davim JP (ed) modern mechanical engineering—Research, development and education. Springer, Berlin, pp 369–412
29.
Fernández-Robles L, Azzopardi G, Alegre E, Petkov N (2015) Cutting edge localisation in an edge profile milling head. In: International conference on computer analysis of images and patterns. Springer, Cham, pp 336–347
30.
Fernández-Robles L, Azzopardi G, Alegre E, Petkov N (2017) Machine-vision-based identification of broken inserts in edge profile milling heads. Robot Comput-Integr Manuf 44:276–283 CrossRef
31.
García-Ordás MT, Alegre-Gutiérrez E, Alaiz-Rodríguez R, González-Castro V (2018) Tool wear monitoring using an online, automatic and low cost system based on local texture. Mech Syst Signal Process 112:98–112 CrossRef
32.
You Z, Gao H, Guo L, Liu Y, Li J (2020) On-line milling cutter wear monitoring in a wide field-of-view camera. Wear, 460:203479
33.
Duan G, Chen YW, Sukegawa T (2010) Automatic optical flank wear measurement of microdrills using level set for cutting plane segmentation. Mach Vis Appl 21:667–676 CrossRef
34.
Lins RG, de Araujo PRM, Corazzim M (2020) In-process machine vision monitoring of tool wear for cyber-physical production systems. Robot Comput-Integr Manuf 61:101859
35.
Kassim AA, Mannan MA, Jing M (2000) Machine tool condition monitoring using workpiece surface texture analysis. Mach Vis Appl 11:257–263 CrossRef
36.
Kassim AA, Mian Z, Mannan MA (2006) Tool condition classification using Hidden Markov model based on fractal analysis of machined surface textures. Mach Vis Appl 17:327–336 CrossRef
37.
Bhat NN, Dutta S, Vashisth T, Pal S, Pal SK, Sen R (2016) Tool condition monitoring by SVM classification of machined surface images in turning. Int J Adv Manuf Technol 83:1487–1502 CrossRef
38.
Bhat NN, Dutta S, Pal SK, Pal S (2016) Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images. Measurement 90:500–509 CrossRef
39.
Kassim AA, Mian Z, Mannan MA (2004) Connectivity oriented fast Hough transform for tool wear monitoring. Pattern Recogn 37:1925–1933 CrossRef
40.
Kassim AA, Mannan MA, Mian Z (2007) Texture analysis methods for tool condition monitoring. Image Vis Comput 25:1080–1090 CrossRef
41.
Dutta S, Datta A, Chakladar ND, Pal SK, Mukhopadhyay S, Sen R (2012) Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique. Precis Eng 36:458–466 CrossRef
42.
Gadelmawla ES, Al-Mufadi FA, Al-Aboodi AS (2013) Calculation of the machining time of cutting tools from captured images of machined parts using image texture features. Proc Inst Mech Eng Part B: J Eng Manuf 228:203–214 CrossRef
43.
Datta A, Dutta S, Pal SK, Sen R (2013) Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method. J Mater Process Technol 213:2339–2349 CrossRef
44.
Dutta S, Kanwat A, Pal SK, Sen R (2013) Correlation study of tool flank wear with machined surface texture in end milling. Measurement 46:4249–4260 CrossRef
45.
Dutta S, Pal SK, Sen R (2016) On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression. Precis Eng 43:34–42 CrossRef
46.
Dutta S, Pal SK, Sen R (2016) Tool condition monitoring in turning by applying machine vision. J Manuf Sci Eng 138
47.
Dutta S, Pal SK, Sen R (2018) Progressive tool condition monitoring of end milling from machined surface images. Proc Inst Mech Eng Part B J EngManuf 232:251–326 CrossRef
48.
Ren L, Zhang G, Wang Y, Zhang Q, Wang F, Huang Y (2019) A new in-process material removal rate monitoring approach in abrasive belt grinding. Int J Adv Manuf Technol 104(5–8):2715–2726 CrossRef
49.
Dhanasekar B, Ramamoorthy B (2010) Restoration of blurred images for surface roughness evaluation using machine vision. Tribol Int 43:268–276 CrossRef
50.
Al-Kindi G, Zughaer H (2012) An approach to improved CNC machining using vision-based system. Mater Manuf Process 27:765–774 CrossRef
51.
Bamberger H, Ramachandran S, Hong E, Katz R (2011) Identification of machining chatter marks on surfaces of automotive valve seats. Trans ASME-J Manuf Sci Eng 133:0410031–0410037 CrossRef
52.
Samtaş G (2014) Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network. Int J Adv Manuf Technol 73:353–364 CrossRef
53.
Tootooni MS, Liu C, Roberson D, Donovan R, Rao PK, Kong ZJ, Bukkapatnam ST (2016) Online non-contact surface finish measurement in machining using graph theory-based image analysis. J Manuf Syst 41:266–276 CrossRef
54.
Sun H, Gao D, Zhao Z, Tang X (2017) An approach to in-process surface texture condition monitoring. Robot Comput-Integr Manuf 48:254–262 CrossRef
55.
Diao S, Chen X, Luo J (2018) Development and experimental evaluation of a 3D vision system for grinding robot. Sensors 18(9):3078 CrossRef
56.
Mei B, Zhu W, Ke Y (2018) Positioning variation analysis and control for automated drilling in aircraft manufacturing. Assem Autom
57.
Yu L, Bi Q, Ji Y, Fan Y, Huang N, Wang Y (2019) Vision based in-process inspection for countersink in automated drilling and riveting. Precis Eng 58:35–46 CrossRef
58.
Shao J, Du D, Chang B, Shi H (2012) Automatic weld defect detection based on potential defect tracking in real-time radiographic image sequence. NDT and E Int 46:14–21 CrossRef
59.
Zou Y, Du D, Chang B, Ji L, Pan J (2015) Automatic weld defect detection method based on Kalman filtering for real-time radiographic inspection of spiral pipe. NDT and E Int 72:1–9 CrossRef
60.
Chu HH, Wang ZY (2016) A vision-based system for post-welding quality measurement and defect detection. Int J Adv Manuf Technol 86(9–12):3007–3014 CrossRef
61.
Li Y, Li YF, Wang QL, Xu D, Tan M (2009) Measurement and defect detection of the weld bead based on online vision inspection. IEEE Trans Instrum Meas 59(7):1841–1849
62.
Alfaro SCA, Vargas JAR, de Carvalho GC, de Souza GG (2015) Characterization of “humping” in the GTA welding process using infrared images. J Mater Process Technol 223:216–224 CrossRef
63.
Chu HH, Wang ZY (2017) A study on welding quality inspection system for shell-tube heat exchanger based on machine vision. Int J Precis Eng Manuf 18(6):825–834 CrossRef
64.
Sassi P, Tripicchio P, Avizzano CA (2019) A smart monitoring system for automatic welding defect detection. IEEE Trans Industr Electron 66(12):9641–9650 CrossRef
65.
Bhat NN, Kumari K, Dutta S, Pal SK, Pal S (2015) Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images. J Manuf Process 20:274–281 CrossRef
66.
Sudhagar S, Sakthivel M, Ganeshkumar P (2019) Monitoring of friction stir welding based on vision system coupled with Machine learning algorithm. Measurement 144:135–143 CrossRef
67.
Ranjan R, Khan AR, Parikh C, Jain R, Mahto RP, Pal S, Pal SK, Chakravarty D (2016) Classification and identification of surface defects in friction stir welding: an image processing approach. J Manuf Process 22:237–253 CrossRef
68.
Parikh C, Ranjan R, Khan AR, Jain R, Mahto RP, Chakravarty D, Pal S, Pal SK (2017) Volumetric defect analysis in friction stir welding based on three dimensional reconstructed images. J Manuf Process 29:96–112 CrossRef
69.
Sun J, Li C, Wu XJ, Palade V, Fang W (2019) An effective method of weld defect detection and classification based on machine vision. IEEE Trans Industr Inf 15(12):6322–6333 CrossRef
70.
Kim CH, Ahn DC (2012) Coaxial monitoring of keyhole during Yb: YAG laser welding. Opt Laser Technol 44(6):1874–1880 CrossRef
71.
Luo M, Shin YC (2015) Estimation of keyhole geometry and prediction of welding defects during laser welding based on a vision system and a radial basis function neural network. Int J Adv Manuf Technol 81(1):263–276 CrossRef
72.
Židek K, Piteľ J, Adámek M, Lazorík P, Hošovský A (2020) Digital twin of experimental smart manufacturing assembly system for Industry 4.0 concept. Sustainability 12(9):3658
73.
Chauhan V, Surgenor B (2017) Fault detection and classification in automated assembly machines using machine vision. Int J Adv Manuf Technol 90(9–12):2491–2512 CrossRef
74.
Xu Y, Lv N, Fang G, Du S, Zhao W, Ye Z, Chen S (2017) Welding seam tracking in robotic gas metal arc welding. J Mater Process Technol 248:18–30 CrossRef
75.
Eckhart M, Ekelhart A (2019) Digital twins for cyber-physical systems security: State of the art and outlook. In: Security and quality in cyber-physical systems engineering. Springer, Cham, pp 383–412
76.
77.
78.
Burke MW (1996) Image acquisition: handbook of machine vision engineering. Chapman and Hall, London
79.
Saba MH, Mukherjee S, Dutta S, Mallisetty PK, Murmu NC (2020) Electrohydrodynamic jet printing for desired print diameter. Mater Today: Proc
80.
Gonzalez RC, Woods RE (2002) Digital image processing. Prentice-Hall, NJ, USA
81.
Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, Romeny BH, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vis Grap Image Process 39(3):355–368 CrossRef
82.
Lee BY, Yu SF, Juan H (2004) The model of surface roughness inspection by vision system in turning. Mechatronics 14(1):129–141 CrossRef
83.
Petrosian AA, Meyer FG (2001) Wavelets in signal and image analysis: from theory to practice. Kluwer Academic, Boston
84.
Petrou M, Sevilla PG (2006) Image processing dealing with texture. Wiley, UK
85.
Mallat S (1999) A wavelet tour of signal processing. Academic Press, London, UK
Metadaten
Titel
Image Processing for Digital Twin
verfasst von
Surjya Kanta Pal
Debasish Mishra
Arpan Pal
Samik Dutta
Debashish Chakravarty
Srikanta Pal
Copyright-Jahr
2022
Buch
Digital Twin – Fundamental Concepts to Applications in Advanced Manufacturing

Print ISBN: 978-3-030-81814-2

Electronic ISBN: 978-3-030-81815-9

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-81815-9_4

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