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Integrating intelligent machine vision techniques to advance precision manufacturing: a comprehensive survey in the context of mechatronics and beyond

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Abstract

Measuring machining parameters is essential for influencing the quality and precision of the finished product in the manufacturing and machining industries. Machine vision systems, which provide an in-depth investigation of these parameters, are essential resources for this purpose. To evaluate machining parameters such as tool wear, surface roughness, and defects, this article investigates machine vision and its techniques. It also explores tool condition monitoring (TCM), a subject that is becoming more and more important. To achieve precision, high-resolution cameras with CCD or CMOS sensors in conjunction with deliberate illumination are essential. Area, compactness, and perimeter metrics are essential for assessing machining parameters because they offer insightful information about a variety of situations and enhance tool performance. By effectively utilizing these techniques, machinery can be converted into intelligent systems that improve safety, reliability, and product quality by preventing tool failure and optimizing cutting feed rates. A thorough review of the literature highlights the advantages of combining the direct and indirect TCM measurement methods, improving measurement accuracy. Additionally, the detection of tool wear problems like chip**, crater wear, and fractures is significantly aided by the integration of digital image processing techniques.

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References

  1. Santos, E., Xavier, W. B., Rodrigues, R. N., Botelho, S., Werhli, A.: Vision-based measurement applied to industrial instrumentation. Undefined. (2017) Accessed: Oct. 18, 2021. [Online]. Available: https://www.semanticscholar.org/paper/Vision-Based-Measurementapplied-to-Industrial-Santos-Xavier/3441c577f3e13d348fa8c0420bb1788da40681a3

  2. Chethan, Y.D., Ravindra, H.V., Krishnegowda, Y.T.: Optimisation of machining parameters in turning Nimonic-75 using machine vision and acoustic emission signals by Taguchi technique. Measurement 144, 144–154 (2019). https://doi.org/10.1016/j.measurement.2019.05.035

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Kim, J.H., Moon, D.K., Lee, D.W., Kim, J.S., Kang, M.C., Kim, K.H.: Tool wear measuring technique on the machine using CCD and exclusive jig. J. Mater. Process. Technol. 130, 668–674 (2002). https://doi.org/10.1016/S0924-0136(02)00733-1

    Article  Google Scholar 

  5. Ayub, M.A., Mohamed, A.B., Esa, A.H.: In-line inspection of roundness using machine vision. Procedia Technol. 15, 807–816 (2014)

    Article  Google Scholar 

  6. Benbarrad, T., Salhaoui, M., Kenitar, S.B., Arioua, M.: Intelligent machine vision model for defective product inspection based on machine learning. J. Sens. Actuator Netw.Netw. (2021). https://doi.org/10.3390/jsan10010007

    Article  Google Scholar 

  7. Eshkevari, M., JahangoshaiRezaee, M., Zarinbal, M., Izadbakhsh, H.: Automatic dimensional defect detection for glass vials based on machine vision A heuristic segmentation method. J. Manuf. Process. 68, 973–989 (2021). https://doi.org/10.1016/j.jmapro.2021.06.018

    Article  Google Scholar 

  8. Bradley, C., Wong, Y.S.: Surface texture indicators of tool wear - a machine vision approach. Int. J. Adv. Manuf. Technol. 17(6), 435–443 (2001). https://doi.org/10.1007/s001700170161

    Article  Google Scholar 

  9. Bagga, P.J., Makhesana, M.A., Patel, K., Patel, K.M.: Tool wear monitoring in turning using image processing techniques. Mater. Today: Proc. 44, 771–775 (2021). https://doi.org/10.1016/j.matpr.2020.10.680

    Article  Google Scholar 

  10. Selvaraj, T., Balasubramani, C., Vignesh, S.H., Prabakaran, M.P.: Tool wear monitoring by image processing. Int. J. Eng. Res. 2(8), 10 (2013)

    Google Scholar 

  11. Moldovan, O., Dzitac, S., Moga, I., Vesselenyi, T., Dzitac, I.: Tool-wear analysis using image processing of the tool flank. Symmetry 9(12), 296 (2017). https://doi.org/10.3390/sym9120296

    Article  Google Scholar 

  12. Chethan, Y.D., Ravindra, H., Gowda, Y.T.K., Kumar, S.B.: Machine Vision for Tool Status Monitoring in Turning Inconel 718 using Blob Analysis. Undefined (2015). Accessed: Oct. 18, 2021. [Online]. Available: https://www.semanticscholar.org/paper/Machine-Visionfor-Tool-Status-Monitoring-in-718-Chethan-Ravindra/4fcaaae6248444d531e06f08971bf4bb354f7e80

  13. Žuvela, P., Lovrić, M., Yousefian-Jazi, A., Liu, J.J.: Ensemble learning approaches to data imbalance and competing objectives in design of an industrial machine vision system. Ind. Eng. Chem. Res. (2020). https://doi.org/10.1021/acs.iecr.9b05766

    Article  Google Scholar 

  14. Patel, D.R., Kiran, M.B., Vakharia, V.: Modeling and prediction of surface roughness using multiple regressions: a noncontact approach. Eng. Rep. 2(2), e12119 (2020). https://doi.org/10.1002/eng2.12119

    Article  Google Scholar 

  15. Dhanasekar, B., Ramamoorthy, B.: Restoration of blurred images for surface roughness evaluation using machine vision. Tribol. Int.. Int. 43(1–2), 268–276 (2010). https://doi.org/10.1016/j.triboint.2009.05.030

    Article  Google Scholar 

  16. Yuan, Y., et al.: Crack length measurement using convolutional neural networks and image processing. Sensors 21(17), 5894 (2021). https://doi.org/10.3390/s21175894

    Article  Google Scholar 

  17. Patel, D.R., Vakharia, V., Kiran, M.B.: Texture classification of machined surfaces using image processing and machine learning techniques. FME Trans. 47(4), 865–872 (2019). https://doi.org/10.5937/fmet1904865P

    Article  Google Scholar 

  18. Xu, L.M., Fan, F., Hu, Y.X., Zhang, Z., Hu, D.J.: A vision-based processing methodology for profile grinding of contour surfaces. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. (2019). https://doi.org/10.1177/0954405419857401

    Article  Google Scholar 

  19. Mook, W.K., Shahabi, H.H., Ratnam, M.M.: Measurement of nose radius wear in turning tools from a single 2D image using machine vision. Int. J. Adv. Manuf. Technol. 43(3), 217–225 (2009). https://doi.org/10.1007/s00170-008-1712-1

    Article  Google Scholar 

  20. Kumar, B.M., Ratnam, M.M.: Study on effect of tool nose radius wear on hybrid roughness parameters during turning using vision-based approach. IOP Conf. Ser. Mater. Sci. Eng. 530, 012009 (2019). https://doi.org/10.1088/1757-899X/530/1/012009

    Article  Google Scholar 

  21. Patel, D.R., Kiran, M.B.: Vision based prediction of surface roughness for end milling. Mater. Today Proc. 44, 792–796 (2021). https://doi.org/10.1016/j.matpr.2020.10.709

    Article  Google Scholar 

  22. Patel, D.R., Kiran, M.B.: A non-contact approach for surface roughness prediction in CNC turning using a linear regression model. Mater. Today Proc. 26, 350–355 (2020). https://doi.org/10.1016/j.matpr.2019.12.029

    Article  Google Scholar 

  23. Guardiola, B.A.: Machine vision systems: automated inspection and metrology. p. 88

  24. Shuxia, G., Jiancheng, Z., **aofeng, J., Yin, P., Lei, W.: Mini milling cutter measurement based on machine vision. Procedia Eng. 15, 1807–1811 (2011). https://doi.org/10.1016/j.proeng.2011.08.336

    Article  Google Scholar 

  25. Szydłowski, M., Powałka, B.: Chatter detection algorithm based on machine vision. Int. J. Adv. Manuf. Technol. 62(5–8), 517–528 (2012). https://doi.org/10.1007/s00170-011-3816-2

    Article  Google Scholar 

  26. Zhou, J., Yu, J.: Chisel edge wear measurement of high-speed steel twist drills based on machine vision. Comput. Ind.. Ind. 128, 103436 (2021). https://doi.org/10.1016/j.compind.2021.103436

    Article  Google Scholar 

  27. Shahabi, H.H., Ratnam, M.M.: Assessment of flank wear and nose radius wear from workpiece roughness profile in turning operation using machine vision. Int. J. Adv. Manuf. Technol. 43(1), 11–21 (2009). https://doi.org/10.1007/s00170-008-1688-x

    Article  Google Scholar 

  28. Mahapatra, P.K., Thareja, R., Kaur, M., Kumar, A.: A machine vision system for tool positioning and its verification. Meas. Control (2015). https://doi.org/10.1177/0020294015602499

    Article  Google Scholar 

  29. Li, B.: Research on geometric dimension measurement system of shaft parts based on machine vision. EURASIP J. Image Video Process (2018). https://doi.org/10.1186/s13640-018-0339-x

    Article  Google Scholar 

  30. Dutta, S., Pal, S. K., Mukhopadhyay, S., Sen, R.: Application of digital image processing in tool condition monitoring: a review. Undefined, 2013, Accessed: Oct. 18 2021. [Online]. Available: https://www.semanticscholar.org/paper/Application-of-digital-imageprocessing-in-tool-A-Dutta-Pal/bd53460cef65cb12f69bfab31bc66d82d30b1e29

  31. Verma, N., Vettivel, S.C., Rao, P.S., Zafar, S.: Processing, tool wear measurement using machine vision system and optimisation of machining parameters of boron carbide and rice husk ash reinforced. Mater. Res. Express 6(8), 86 (2019). https://doi.org/10.1088/2053-1591/ab2509

    Article  Google Scholar 

  32. Lins, R.G., de Araujo, P.R.M., Corazzim, M.: In-process machine vision monitoring of tool wear for cyber-physical production systems. Robot. Comput.-Integr. Manuf. 61, 101859 (2020). https://doi.org/10.1016/j.rcim.2019.101859

    Article  Google Scholar 

  33. Shao, F., Liu, Z., Wan, Y., Shi, Z.: Finite element simulation of machining of Ti-6Al-4V alloy with thermodynamical constitutive equation. Int. J. Adv. Manuf. Technol. 49(5–8), 431–439 (2010). https://doi.org/10.1007/s00170-009-2423-y

    Article  Google Scholar 

  34. Sortino, M.: Application of statistical filtering for optical detection of tool wear,” undefined. Accessed: Oct. 18, 2021. [Online]. (2003) Available: https://www.semanticscholar.org/paper/Application-of-statistical-filtering-for-optical-ofSortino/cfb523844d543730b40483a7379227acf1ae110b

  35. Castejón, M., Alegre, E., Barreiro, J., Hernández, L.: On-line tool wear monitoring using geometric descriptors from digital images. undefined, .Accessed: Oct. 18, 2021. [Online]. (2007). Available: https://www.semanticscholar.org/paper/On-line-tool-wear-monitoringusing-geometric-from-Castej%C3%B3n-Alegre/e4679549606817a97e11e74dcd84e1ba7850165c

  36. Danesh, M., Khalili, K.: Determination of tool wear in turning process using undecimated wavelet transform and textural features. Undefined. Accessed: Oct. 18, 2021. [Online] (2015) Available: https://www.semanticscholar.org/paper/Determination-of-Tool-Wear-inTurning-Process-Using-Danesh-Khalili/e720a235f30c8d0ad8240d654627cfda6c019dfd

  37. Yu, X., Lin, X., Dai, Y., Zhu, K.: Image edge detection based tool condition monitoring with morphological component analysis. Undefined (2017) Accessed: Oct. 18, 2021. [Online]. Available: https://www.semanticscholar.org/paper/Image-edge-detection-based-toolcondition-with-Yu-Lin/a9845773454d36ee77f4bb3cdb91c64d1a71080e

  38. D’Addona, D.M., Teti, R.: Image data processing via neural networks for tool wear prediction. Procedia CIRP 12, 252–257 (2013). https://doi.org/10.1016/j.procir.2013.09.044

    Article  Google Scholar 

  39. Yu, J., Cheng, X., Lu, L., Wu, B.: A machine vision method for measurement of machining tool wear. Measurement 182, 109683 (2021). https://doi.org/10.1016/j.measurement.2021.109683

    Article  Google Scholar 

  40. Dawson, T.G., Kurfess, T.: Quantification of tool wear using white light interferometry and three-dimensional computational metrology. Undefined, 2005, Accessed: Oct. 18, 2021. [Online]. Available: https://www.semanticscholar.org/paper/Quantification-of-toolwear-using-white-light-and-Dawson-Kurfess/eacc9a87f117bd8a7b9c36dc50cea28cb580cf90

  41. **ong, G., Liu, J., Avila, A.: Cutting tool wear measurement by using active contour model based image processing. In: 2011 IEEE International Conference on Mechatronics and Automation, Bei**g, China, pp. 670–675 (2011). https://doi.org/10.1109/ICMA.2011.5985741

  42. Prasad, K. N., Ramamoorthy, B.: Tool wear evaluation by stereo vision and prediction by artificial neural network. Undefined, 2001. Accessed: Oct. 18, 2021. [Online]. Available: https://www.semanticscholar.org/paper/Tool-wear-evaluation-by-stereo-vision-and-byneural-Prasad-Ramamoorthy/35359076d898f2d10219cb805828be893653a4ca

  43. Iliyas Ahmad, M., Yusof, Y., Daud, M.E., Latiff, K., AbdulKadir, A.Z., Saif, Y.: Machine monitoring system: a decade in review. Int. J. Adv. Manuf. Technol. 108(11), 3645–3659 (2020). https://doi.org/10.1007/s00170-020-05620-3

    Article  Google Scholar 

  44. Teti, R., Jemielniak, K., O’Donnell, G., Dornfeld, D.: Advanced monitoring of machining operations. CIRP Ann. 59(2), 717–739 (2010)

    Article  Google Scholar 

  45. Thakre, A.A., Lad, A.V., Mala, K.: Measurements of tool wear parameters using machine vision system. Model. Simul. Eng. (2019). https://doi.org/10.1155/2019/1876489

    Article  Google Scholar 

  46. Wang, Y., Jia, X., Li, X., Yang, S., Zhao, H., Lee, J.: A machine vision-based monitoring system for the LCD panel cutting wheel degradation. Procedia Manuf. 48, 49–53 (2020). https://doi.org/10.1016/j.promfg.2020.05.019

    Article  Google Scholar 

  47. Fernández-Robles, L., Azzopardi, G., Alegre, E., Petkov, N.: Machine-vision-based identification of broken inserts in edge profile milling heads. Robot. Comput.-Integrated Manuf. 44, 276–283 (2017). https://doi.org/10.1016/j.rcim.2016.10.004

    Article  Google Scholar 

  48. Kurada, S., Bradley, C.: A review of machine vision sensors for tool condition monitoring. Comput. Ind.. Ind. 34(1), 55–72 (1997). https://doi.org/10.1016/S0166-3615(96)00075-9

    Article  Google Scholar 

  49. Choudhary, A. K. , AhmadKhan D.: Introduction to conditioning monitoring of mechanical systems. Soft Comput. Cond. Monit. Diagn. Electr. Mech. Syst. (2020) https://doi.org/10.1007/978-981-15-1532-3_9

  50. Liu, W., Li, X., Jia, Z., Yan, H., Ma, X.: A three-dimensional triangular vision-based contouring error detection system and method for machine tools. Precis. Eng. 50, 85–98 (2017). https://doi.org/10.1016/j.precisioneng.2017.04.016

    Article  Google Scholar 

  51. Nath, C.: Integrated tool condition monitoring systems and their applications: a comprehensive review. Procedia Manuf. 48, 852–863 (2020). https://doi.org/10.1016/j.promfg.2020.05.123

    Article  Google Scholar 

  52. Zhang, C., Zhang, J.: On-line tool wear measurement for ball-end milling cutter based on machine vision. Comput. Ind.. Ind. 64(6), 708–719 (2013). https://doi.org/10.1016/j.compind.2013.03.010

    Article  Google Scholar 

  53. Ge, L., Dan, D., Li, H.: An accurate and robust monitoring method of full-bridge traffic load distribution based on YOLO-v3 machine vision. Struct. Control. Health Monit.. Control. Health Monit. 27(12), e2636 (2020). https://doi.org/10.1002/stc.2636

    Article  Google Scholar 

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

    Article  Google Scholar 

  55. Peng, R., Liu, J., Fu, X., Liu, C., Zhao, L.: Application of machine vision method in tool wear monitoring. Int. J. Adv. Manuf. Technol. 116(3), 1357–1372 (2021). https://doi.org/10.1007/s00170-021-07522-4

    Article  Google Scholar 

  56. Ambadekar, P.K., Choudhari, C.M.: CNN based tool monitoring system to predict life of cutting tool. SN Appl. Sci. 2(5), 1–11 (2020). https://doi.org/10.1007/s42452-020-2598-2

    Article  Google Scholar 

  57. Wong, S.Y., Chuah, J.H., Yap, H.J.: Technical data-driven tool condition monitoring challenges for CNC milling: a review. Int. J. Adv. Manuf. Technol. 107(11), 4837–4857 (2020). https://doi.org/10.1007/s00170-020-05303-z

    Article  Google Scholar 

  58. Hou, Q., Sun, J., Huang, P.: A novel algorithm for tool wear on-line inspection based on machine vision. Int. J. Adv. Manuf. Technol. 101(9), 2415–2423 (2019). https://doi.org/10.1007/s00170-018-3080-9

    Article  Google Scholar 

  59. Chen, W., Teng, X., Huo, D., Wang, Q.: An improved cutting force model for micro milling considering machining dynamics. Int. J. Adv. Manuf. Technol. 93(9), 3005–3016 (2017). https://doi.org/10.1007/s00170-017-0706-2

    Article  Google Scholar 

  60. Peng, R., Pang, H., Jiang, H., Hu, Y.: Study of tool wear monitoring using machine vision. Autom. Control. Comput. Sci.. Control. Comput. Sci. 54(3), 259–270 (2020). https://doi.org/10.3103/S0146411620030062

    Article  Google Scholar 

  61. García-Ordás, M.T., Alegre, E., González-Castro, V., Alaiz-Rodríguez, R.: A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques. Int. J. Adv. Manuf. Technol. 90(5), 1947–1961 (2017). https://doi.org/10.1007/s00170-016-9541-0

    Article  Google Scholar 

  62. Sun, W.-H., Yeh, S.-S.: Using the machine vision method to develop an on-machine insert condition monitoring system for computer numerical control turning machine tools. Materials (2018). https://doi.org/10.3390/ma11101977

    Article  Google Scholar 

  63. Szydłowski, M., Powałka, B., Matuszak, M., Kochmański, P.: Machine vision micro-milling tool wear inspection by image reconstruction and light reflectance. Precis. Eng. 44, 236–244 (2016). https://doi.org/10.1016/j.precisioneng.2016.01.003

    Article  Google Scholar 

  64. Čerče, L., Pušavec, F., Kopač, J.: 3D cutting tool-wear monitoring in the process. J. Mech. Sci. Technol. 29(9), 3885–3895 (2015). https://doi.org/10.1007/s12206-015-0834-2

    Article  Google Scholar 

  65. Wei, W., Yin, J., Zhang, J., Zhang, H., Lu, Z.: Wear and breakage detection of integral spiral end milling cutters based on machine vision. Materials (2021). https://doi.org/10.3390/ma14195690

    Article  Google Scholar 

  66. Loizou, J., Tian, W., Robertson, J., Camelio, J.: Automated wear characterization for broaching tools based on machine vision systems. J. Manuf. Syst. 37, 558–563 (2015). https://doi.org/10.1016/j.jmsy.2015.04.005

    Article  Google Scholar 

  67. Lee, W.K., Ratnam, M.M., Ahmad, Z.A.: Detection of fracture in ceramic cutting tools from workpiece profile signature using image processing and fast Fourier transform. Precis. Eng. 44, 131–142 (2016). https://doi.org/10.1016/j.precisioneng.2015.11.001

    Article  Google Scholar 

  68. Prabhu, S., Karthik Saran, S., Majumder, D., Siva Teja, P.V.: A review on applications of image processing in inspection of cutting tool surfaces. Appl. Mech. Mater. 766–767, 635–642 (2015). https://doi.org/10.4028/www.scientific.net/AMM.766-767.635

    Article  Google Scholar 

  69. Jywe, W.-Y., Hsieh, T.-H., Chen, P.-Y., Wang, M.-S., Lin, Y.-T.: Evaluation of tool scra** wear conditions by image pattern recognition system. Int. J. Adv. Manuf. Technol. 105(1), 1791–1799 (2019). https://doi.org/10.1007/s00170-019-04360-3

    Article  Google Scholar 

  70. García-Ordás, M.T., Alegre-Gutiérrez, E., González-Castro, V., Alaiz-Rodríguez, R.: Combining shape and contour features to improve tool wear monitoring in milling processes. Int. J. Prod. Res. (2018). https://doi.org/10.1080/00207543.2018.1435919

    Article  Google Scholar 

  71. Kalil, J., Schueller, K., Pinto, F. de A. C., Villibor, G. P.: Monitoring of flank wear and damage on turning cutting tools by image processing. J. Eng. Exact Sci. (2020) https://doi.org/10.18540/jcecvl6iss2pp0098-0106.

  72. Zawawi, M. A. M. , Teoh, S. S., Abdullah, N. B., Mohd Sazali, M. I. S. (Eds.) In: 10th International Conference on Robotics, Vision, Signal Processing and Power Applications: Enabling Research and Innovation Towards Sustainability, vol. 547. Singapore: Springer Singapore (2019). https://doi.org/10.1007/978-981-13-6447-1.

  73. Ho, S.-Y., Lee, K., Chen, S.-S., Ho, S.-J.: Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Undefined (2002). Accessed: Oct. 19, 2021. [Online]. Available: https://www.semanticscholar.org/paper/Accurate-modeling-and-prediction-of-surface-by-in-HoLee/7d476bb348e8a0819605ec31b46f9e2a9afce96d

  74. Kapłonek, W., Nadolny, K.: Laser methods based on an analysis of scattered light for automated, in-process inspection of machined surfaces: a review. Optik 126(20), 2764–2770 (2015). https://doi.org/10.1016/j.ijleo.2015.07.009

    Article  Google Scholar 

  75. Sun, T.-H., Tien, F.-C., Tien, F.-C., Kuo, R.-J.: Automated thermal fuse inspection using machine vision and artificial neural networks. J. Intell. Manuf.Intell. Manuf. 27(3), 639–651 (2016). https://doi.org/10.1007/s10845-014-0902-y

    Article  Google Scholar 

  76. Wang, J., Qian, J., Ferraris, E., Reynaerts, D.: In-situ process monitoring and adaptive control for precision micro-EDM cavity milling. Precis. Eng. 47, 261–275 (2017). https://doi.org/10.1016/j.precisioneng.2016.09.001

    Article  Google Scholar 

  77. Priya, P., Ramamoorthy, B.: The influence of component inclination on surface finish evaluation using digital image processing. Undefined, 2007, Accessed: Oct. 19, 2021. [Online]. Available: https://www.semanticscholar.org/paper/The-influence-of-componentinclination-on-surface-Priya-Ramamoorthy/e79433a0f6e0fe63dfb01f82822f36cbdc8f93c9

  78. Gadelmawla, E.S., Eladawi, A.E., Abouelatta, O.B., Elewa, I.M.: Investigation of the cutting conditions in milling operations using image texture features. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 222(11), 1395–1404 (2008). https://doi.org/10.1243/09544054JEM1173

    Article  Google Scholar 

  79. Tian, H., Wang, D., Lin, J., Chen, Q., Liu, Z.: Surface defects detection of stam** and grinding flat parts based on machine vision. Sensors 20(16), 4531 (2020). https://doi.org/10.3390/s20164531

    Article  Google Scholar 

  80. Fekri-Ershad, S.: Texture image analysis and texture classification methods—a review. vol. 2, p. 29 (2019).

  81. “Image Processing with NI Vision Development Module.” https://www.ni.com/en-lb/innovations/white-papers/06/image-processing-withni-vision-development-module.html (accessed Oct. 19, 2021).

  82. Baaziz, N., Abahmane, O., Missaoui, R.: Texture feature extraction in the spatial-frequency domain for content-based image retrieval. p. 19

  83. Wang, W., Wong, Y.S., Hong, G.S.: Flank wear measurement by successive image analysis. Comput. Ind.. Ind. (2005). https://doi.org/10.5555/1672858.1672934

    Article  Google Scholar 

  84. Ong, P., Lee, W.K., Lau, R.J.H.: Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision. Int. J. Adv. Manuf. Technol. 104(1), 1369–1379 (2019). https://doi.org/10.1007/s00170-019-04020-6

    Article  Google Scholar 

  85. Ambadekar, P.K., Choudhari, D.C.M.: Application of gray level co-occurrence matrix as a feature extraction technique to monitor wear of cutting tool. p. 9 (2018)

  86. Jurkovic, J., Korosec, M., Kopac, J.: New approach in tool wear measuring technique using CCD vision system. Int. J. Mach. Tools Manuf 45(9), 1023–1030 (2005). https://doi.org/10.1016/j.ijmachtools.2004.11.030

    Article  Google Scholar 

  87. Lutz, B., Kisskalt, D., Regulin, D., Reisch, R., Schiffler, A., Franke, J.: Evaluation of deep learning for semantic image segmentation in tool condition monitoring. In: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), Boca Raton, FL, USA, 2019, pp. 2008–2013. https://doi.org/10.1109/ICMLA.2019.00321.

  88. Li, X.: A brief review: acoustic emission method for tool wear monitoring during turning. Int. J. Mach. Tools Manuf 42(2), 157–165 (2002). https://doi.org/10.1016/S0890-6955(01)00108-0

    Article  Google Scholar 

  89. Zhang, Y., Qi, X., Wang, T., He, Y.: Tool wear condition monitoring method based on deep learning with force signals. Sensors. 23(10), 4595 (2023). https://doi.org/10.3390/s23104595

    Article  Google Scholar 

  90. Machikhin, A., Poroykov, A., Bardakov, V., Marchenkov, A., Zhgut, D., Sharikova, M., Barat, V., Meleshko, N., Kren, A.: Combined acoustic emission and digital image correlation for early detection and measurement of fatigue cracks in rails and train parts under dynamic loading. Sensors. 22(23), 9256 (2022). https://doi.org/10.3390/s22239256

    Article  Google Scholar 

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Authors and Affiliations

  1. Indus University, Ahmedabad, Gujarat, 382115, India

    Dhiren R. Patel

  2. Department of Mechanical Engineering, Parul University, Vadodara, Gujarat, 391760, India

    Ankit D. Oza

  3. Department of Mechanical Engineering, ABES Engineering College, Ghaziabad, U.P., 201009, India

    Manoj Kumar

Authors
  1. Dhiren R. Patel
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  2. Ankit D. Oza
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  3. Manoj Kumar
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Contributions

Conceptualization: DRP; Methodology: ADO; Formal analysis and investigation: MK; Writing—original draft preparation: DRP; Writing—DRP and ADO; Supervision: DRP.

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Correspondence to Dhiren R. Patel.

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Patel, D.R., Oza, A.D. & Kumar, M. Integrating intelligent machine vision techniques to advance precision manufacturing: a comprehensive survey in the context of mechatronics and beyond. Int J Interact Des Manuf (2023). https://doi.org/10.1007/s12008-023-01635-8

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