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2022 | OriginalPaper | Chapter

8. Machine Vision Based Smart Machining System Monitoring

Author : Kunpeng Zhu

Published in: Smart Machining Systems

Publisher: Springer International Publishing

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Abstract

Machine vision based monitoring system refers to the monitoring system that captures the target attributes (pixel distribution, brightness, colour, etc.) using visual devices, and transmit and process the digital image to carry out a variety of detection and control operations for equipment action. The advantage of machine vision is that with a proper setup it can reaches high precision non-destructive monitoring in the machining process, and improves the flexibility and automation of production.

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previous chapter Tool Condition Monitoring with Sparse Decomposition

next chapter Tool Wear Monitoring with Hidden Markov Models

Davies ER (2012) Computer and machine vision: theory, algorithms, practicalities, 5th edn. Academic Press

Beyerer J, León FP, Frese C (2015) Machine vision: automated visual inspection: theory, practice and applications. Springer

Li Q, Ren S (2012) A real-time visual inspection system for discrete surface defects of rail heads. IEEE Trans Instrum Meas

Luo Q, Fang X, Liu L, Yang C, Sun Y (2020) Automated visual defect detection for flat steel surface: a survey. IEEE Trans Instrum Meas 69(3):626–644CrossRef

Ren R, Hung T, Tan K (2018) A generic deep-learning-based approach for automated surface inspection. IEEE Trans Cybern 48(3):929–940CrossRef

Scime L, Beuth J (2018) Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit Manufact

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):2505–2523CrossRef

Chauhan V, Surgenor B (2017) Fault detection and classification in automated assembly machines using machine vision. Int J Adv Manufact Technol

Huang SH, Pan YC (2015) Automated visual inspection in the semiconductor industry: a survey. Comput Ind 66:1–10CrossRef

10.

Cheng Y, Jafari MA (2008) Vision-based online process control in manufacturing applications. IEEE Trans Automat Sci Eng 5(1):140–153

11.

Castejón M, Alegre E, Barreiro J, Hernandez LK (2007) On-line tool wear monitoring using geometric descriptors from digital images. Int J Mach Tools Manuf 47(12–13):1847–1853CrossRef

12.

Lins RG, De Araujo PR, Corazzim M (2020) In-process machine vision monitoring of tool wear for cyber-physical production systems. Robot Comput-Integrat Manufact 61:101859

13.

Zhang C, Zhang J (2013) On-line tool wear measurement for ball-end milling cutter based on machine vision. Comput Ind 64(6):708–718CrossRef

14.

Zhu K, Yu X (2017) The monitoring of micro milling tool wear conditions by wear area estimation. Mech Syst Signal Process 93:80–91CrossRef

15.

García-Ordás MT, Alegre-Gutiérrez E, Alaiz-Rodríguez R et al (2018) Tool wear monitoring using an online, automatic and low cost system based on local texture. Mech Syst Signal Process 112:98–112CrossRef

16.

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 43:34–42CrossRef

17.

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 44:276–283CrossRef

18.

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(3):212–232CrossRef

19.

Wang W, Wong YS, Hong GS (2005) Flank wear measurement by successive image analysis. Comput Ind 56:816–830CrossRef

20.

Prasad KN, Ramamoorthy B (2001) Tool wear evaluation by stereo vision and prediction by artificial neural network. J Mater Process Technol 112:43–52.

21.

Devillez A, Lesko S, Mozer W (2004) Cutting tool crater wear measurement with white light interferometry. Wear 256:56–65CrossRef

22.

Wang WH, Wong YS, Hong GS (2006) 3D measurement of crater wear by phase shifting method. Wear 261:164–171CrossRef

23.

Kassim AA, Mian Z, Mannan MA (2004) Connectivity oriented fast Hough transform for tool wear monitoring. Pattern Recognit 37:1925–1933

24.

Tönshoff HK, Inasaki I (eds) (2001) Sensors in manufacturing. Wiley-VCH

25.

Demant C, Streicher-Abel B, Garnica C (2013) Industrial image processing, 2nd edn. Springer

26.

Gonzalez RC, Woods RE (2018) Digital image processing, 4th ed. New York

27.

Chae J, Park SS, Freiheit T (2006) Investigation of micro-cutting operations. Int J Mach Tools Manuf 46:313–332CrossRef

28.

Patra K, Jha AK, Szalay T, Ranjan J, Monostori L (2017) Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals. Precis Eng 48:279–291CrossRef

29.

Tansel I, Nedbouyan A, Trujillo M, Tansel B (1998) Micro-end-milling—II. Extending tool life with a smart workpiece holder (SWH). Int J Mach Tools Manufact 38:1437–1448

30.

Aramcharoen A, Mativenga PT (2009) Size effect and tool geometry in micromilling of tool steel. Precis Eng 33:402–407CrossRef

31.

Tansel IN, Arkan TT, Bao WY, Mahendrakar N, Shisler B, McCool D (2000) Tool wear estimation in micro-machining. Part I: tool usage–cutting force relationship. Int J Mach Tools Manufact 40:599–608

32.

Lee HC, Chang YC, Huang YS (2014) A reliable wireless sensor system for monitoring mechanical wear-out of parts. IEEE Trans Instrum Meas 63:2488–2497CrossRef

33.

Tansel I, Trujillo M, Nedbouyan A, Velez C, Bao WY (1998) Micro-end-milling—III. Wear estimation and tool breakage detection using acoustic emission signals. Int J Mach Tools Manufact 38:1449–1466

34.

Szydłowski M, Powałkaa B, Matuszakb M, Kochmańskia P (2016) Machine vision micro-milling tool wear inspection by image reconstruction and light reflectance. Precis Eng 44:236–244CrossRef

35.

Astakhov VP (2004) The assessment of cutting tool wear. Int J Mach Tools Manuf 44:637–647CrossRef

36.

Castejón M, Alegre E, Barreiro J, Hernández LK (2007) On-line tool wear monitoring using geometric descriptors from digital images. Int J Mach Tools Manuf 47:1847–1853CrossRef

37.

Dutta S, Kanwat A, Pal SK, Sen R (2013) Correlation study of tool flank wear with machined surface texture in end milling. Measurement 46:4249–4260CrossRef

38.

Jackson MJ, Robinson GM, Hyde J, Rhodes R (2006) Neural image processing of the wear of cutting tools coated with thin films. J Mater Eng Perform 15(2):223–229CrossRef

39.

Shen J, Castan S (1992) An optimal linear operator for step edge detection. CVGIP: Graph Models Image Proces 54:112–133

40.

Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: IEEE international conference on computer vision (ICCV), Bombay, India, Jan 7

41.

He KM, Sun J, Tang XO (2010) Guided image filtering. In: European conference on computer vision (ECCV). Berlin, Germany, Sept 5–11

42.

Jain P, Tyagi V (2016) A survey of edge-preserving image denoising methods. Inf Syst Front 18:159–170CrossRef

43.

Zhu KP, Mei T, Ye DS (2015) Online condition monitoring in micromilling: a force waveform shape analysis approach. IEEE Trans Industr Electron 62:3806–3813

44.

Barreiroa J, Castejóna M, Alegrea E, Hernándezb LK (2008) Use of descriptors based on moments from digital images for tool wear monitoring. Int J Mach Tools Manuf 48:1005–1013CrossRef

45.

Liu TI (1990) A computer vision approach for drill wear measurements. J Mater Shaping Technol 8:11–16CrossRef

46.

Malekian M, Park SS, Jun MBG (2009) Tool wear monitoring of micro-milling operations. J Mater Process Technol 209:4903–4914CrossRef

47.

Fleischer J, Deuchert M, Ruhs C, Kühlewein C, Halvadjiysky G, Schmidt C (2008) Design and manufacturing of micro milling tools. Microsyst Technol 14:1771–1775CrossRef

48.

Su JC, Huang CK, Tarng YS (2006) An automated flank wear measurement of microdrills using machine vision. J Mater Process Technol 180(1–3):328–335CrossRef

49.

Lee KH, Dornfeld DA (2005) Micro-burr formation and minimization through process control. Precis Eng 29:246–252CrossRef

50.

Attanasio A (2017) Tool run-out measurement in micro milling. Micromachines 8(7):221CrossRef

51.

Castro HFF (2008) A method for evaluating spindle rotation errors of machine tools using a laser interferometer. Measurement 41(5):526–537CrossRef

52.

Li KX, Zhu KP, Mei T (2016) A generic instantaneous undeformed chip thickness model for the cutting force modeling in micromilling. Int J Mach Tools Manuf 105:23–31CrossRef

Title: Machine Vision Based Smart Machining System Monitoring
Author: Kunpeng Zhu
Publisher: Springer International Publishing
Book: Smart Machining Systems
Print ISBN: 978-3-030-87877-1

Electronic ISBN: 978-3-030-87878-8

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-87878-8_8

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