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On-machine dimensional inspection: machine vision-based approach

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Abstract

The contemporary industry has witnessed a significant transformative development with the integration of artificial intelligence (AI) in various industrial systems, resulting in an enhanced automation for heightened productivity and efficiency. However, mastering this level of automation can be challenging for some applications, such as manufacturing inspection, which can be delicate while maintaining a precise cadence for an in-line manufacturing scale. In this paper, a systematic machine vision-based approach for on-machine inspection is proposed in order to automate and improve inspection process towards computer numerical control (CNC) machined parts. The approach incorporates remapping algorithm and image processing operations to accurately extract desired features. Subsequently, these features will undergo dimensional inspection based on their generated point clouds. Tests were applied on a sample part using a complementary metal–oxide–semiconductor (CMOS) camera mounted on the spindle of 5-axis CNC machining center. The paper explores numerous aspects related to different stages of the approach and their impact on the resulting inspected features evaluations. It also highlights significant findings regarding critical factors for conducting well-structured experiments at various stages. Promising results have shown the significance of the presented work regarding industrial automation technology, ultimately improving manufacturing efficiency throughout the production line.

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References

  1. Posada J, Toro C, Barandiaran I, Oyarzun D, Stricker D, De Amicis R, Pinto EB, Eisert P, Dollner J, Vallarino I (2015) Visual computing as a key enabling technology for Industrie 4.0 and industrial internet. IEEE Comput Grap Appl 35:26–40. https://doi.org/10.1109/MCG.2015.45

    Article  Google Scholar 

  2. Cheng J, Yang Y, Zou X, Zuo Y (2022) 5G in manufacturing: a literature review and future research. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-022-08990-y

    Article  Google Scholar 

  3. De Araujo PRM, Lins RG (2020) Computer vision system for workpiece referencing in three-axis machining centers. Int J Adv Manuf Technol 106:2007–2020. https://doi.org/10.1007/s00170-019-04626-w

    Article  Google Scholar 

  4. Davies ER (2012) Computer and machine vision: theory, algorithms, practicalities, 4th edn. Elsevier, Amsterdam, Boston

    Google Scholar 

  5. Fan L, Zhang F, Fan H, Zhang C (2019) Brief review of image denoising techniques. Vis Comput Ind Biomed Art 2:7. https://doi.org/10.1186/s42492-019-0016-7

    Article  PubMed  PubMed Central  Google Scholar 

  6. Mumuni A, Mumuni F (2021) CNN architectures for geometric transformation-invariant feature representation in computer vision: a review. SN Comput Sci 2:340. https://doi.org/10.1007/s42979-021-00735-0

    Article  Google Scholar 

  7. Sadaoui SE, Mehdi-Souzani C, Lartigue C (2022) Multisensor data processing in dimensional metrology for collaborative measurement of a laser plane sensor combined to a touch probe. Measurement 188:110395. https://doi.org/10.1016/j.measurement.2021.110395

    Article  Google Scholar 

  8. Azamfirei V, Psarommatis F, Lagrosen Y (2023) Application of automation for in-line quality inspection, a zero-defect manufacturing approach. J Manuf Syst 67:1–22. https://doi.org/10.1016/j.jmsy.2022.12.010

    Article  Google Scholar 

  9. Gao W, Haitjema H, Fang FZ, Leach RK, Cheung CF, Savio E, Linares JM (2019) On-machine and in-process surface metrology for precision manufacturing. CIRP Ann 68:843–866. https://doi.org/10.1016/j.cirp.2019.05.005

    Article  Google Scholar 

  10. Catalucci S, Thompson A, Piano S, Branson DT, Leach R (2022) Optical metrology for digital manufacturing: a review. Int J Adv Manuf Technol 120:4271–4290. https://doi.org/10.1007/s00170-022-09084-5

    Article  Google Scholar 

  11. Lartigue C, Contri A, Bourdet P (2002) Digitised point quality in relation with point exploitation. Measurement 32:193–203. https://doi.org/10.1016/S0263-2241(02)00008-8

    Article  ADS  Google Scholar 

  12. Li Y, Gu P (2004) Free-form surface inspection techniques state of the art review. Comput Aided Des 36:1395–1417. https://doi.org/10.1016/j.cad.2004.02.009

    Article  Google Scholar 

  13. Pérez L, Rodríguez Í, Rodríguez N, Usamentiaga R, García D (2016) Robot guidance using machine vision techniques in industrial environments: a comparative review. Sensors 16:335. https://doi.org/10.3390/s16030335

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  14. Sadaoui SE, Mehdi-Souzani C, Lartigue C, Brahim M (2022) Automatic path planning for high performance measurement by laser plane sensors. Opt Lasers Eng 159:107194. https://doi.org/10.1016/j.optlaseng.2022.107194

    Article  Google Scholar 

  15. Saif Y, Yusof Y, Latif K, Abdul Kadir AZ, Ahmad MBI, Adam A, Hatem N (2022) Development of a smart system based on STEP-NC for machine vision inspection with IoT environmental. Int J Adv Manuf Technol 118:4055–4072. https://doi.org/10.1007/s00170-021-08095-y

    Article  Google Scholar 

  16. Lins RG, Santos RED, Gaspar R (2023) Vision-based measurement for quality control inspection in the context of Industry 4.0: a comprehensive review and design challenges. J Braz Soc Mech Sci Eng 45:229. https://doi.org/10.1007/s40430-023-04050-y

    Article  Google Scholar 

  17. Alonso V, Dacal-Nieto A, Barreto L, Amaral A, Rivero E (2019) Industry 4.0 implications in machine vision metrology: an overview. Procedia Manuf 41:359–366. https://doi.org/10.1016/j.promfg.2019.09.020

    Article  Google Scholar 

  18. Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20:1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012

    Article  ADS  Google Scholar 

  19. Popov K, Dimov S, Ivanov A, Pham DT, Gandarias E (2010) New tool-workpiece setting up technology for micro-milling. Int J Adv Manuf Technol 47:21–27. https://doi.org/10.1007/s00170-009-2055-2

    Article  Google Scholar 

  20. Micali MK, Cashdollar HM, Gima ZT, Westwood MT (2016) One touch workpiece verification system for CNC machining using a low-cost computer vision approach. In: Volume 3: Joint MSEC-NAMRC Symposia, American Society of Mechanical Engineers, Blacksburg, Virginia, USA, pp V003T08A021. https://doi.org/10.1115/MSEC2016-8725

  21. Okarma K, Grudzinski M (2012) The 3D scanning system for the machine vision based positioning of workpieces on the CNC machine tools, in: 2012 17th International Conference on Methods & Models in Automation & Robotics (MMAR), IEEE, Miedzyzdroje, Poland, pp 85–90. https://doi.org/10.1109/MMAR.2012.6347906

  22. Ahmad R, Tichadou S, Hascoet J-Y (2012) New computer vision based Snakes and Ladders algorithm for the safe trajectory of two axis CNC machines. Comput Aided Des 44:355–366. https://doi.org/10.1016/j.cad.2011.12.008

    Article  Google Scholar 

  23. Tian X, Zhang X, Yamazaki K, Hansel A (2010) A study on three-dimensional vision system for machining setup verification. Robotics and Computer-Integrated Manufacturing 26:46–55. https://doi.org/10.1016/j.rcim.2009.02.002

    Article  Google Scholar 

  24. Stavropoulos P, Papacharalampopoulos A, Vasiliadis E, Chryssolouris G (2016) Tool wear predictability estimation in milling based on multi-sensorial data. Int J Adv Manuf Technol 82:509–521. https://doi.org/10.1007/s00170-015-7317-6

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Zhu K, Guo H, Li S, Lin X (2023) Online tool wear monitoring by super-resolution based machine vision. Comput Ind 144:103782. https://doi.org/10.1016/j.compind.2022.103782

    Article  Google Scholar 

  27. Mejia-Parra D, Sánchez J, Ruiz-Salguero O, Alonso M, Izaguirre A, Gil E, Palomar J, Posada J (2019) In-line dimensional inspection of warm-die forged revolution workpieces using 3D mesh reconstruction. Appl Sci 9:1069. https://doi.org/10.3390/app9061069

    Article  Google Scholar 

  28. Ho C-C, Zhang R-H (2021) Machine vision-based relative-angle measurement system between circular holes. Meas Control 54:647–657. https://doi.org/10.1177/00202940211013059

    Article  Google Scholar 

  29. Kshaurad K, Kiran MB, Shanmuganatan SP (2021) Minimum zone tolerance algorithm to detect roundness error for machined rods using vision system. Mater Today: Proc 46:5997–6003. https://doi.org/10.1016/j.matpr.2020.12.788

    Article  Google Scholar 

  30. Saif Y, Yusof Y, Latif K, Kadir AZA, Ahmed MBL, Adam A, Hatem N, Memon DA (2022) Roundness Holes’ measurement for milled workpiece using machine vision inspection system based on IoT structure: a case study. Measurement 195:111072. https://doi.org/10.1016/j.measurement.2022.111072

    Article  Google Scholar 

  31. Saif Y, Rus AZM, Yusof Y, Ahmed ML, Al-Alimi S, Didane DH, Adam A, Gu YH, Al-masni MA, Abdulrab HQA (2023) Advancements in roundness measurement parts for industrial automation using internet of things architecture-based computer vision and image processing techniques. Appl Sci 13:11419. https://doi.org/10.3390/app132011419

    Article  CAS  Google Scholar 

  32. Saif Y, Yusof Y, Rus AZM, Ghaleb AM, Mejjaouli S, Al-Alimi S, Didane DH, Latif K, Abdul Kadir AZ, Alshalabi H, Sadeq S (2023) Implementing circularity measurements in industry 4.0-based manufacturing metrology using MQTT protocol and Open CV: a case study. PLoS One 18:e0292814. https://doi.org/10.1371/journal.pone.0292814

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Yang F-S, Ho C-C, Chen L-C (2021) Automated optical inspection system for O-ring based on photometric stereo and machine vision. Appl Sci 11:2601. https://doi.org/10.3390/app11062601

    Article  CAS  Google Scholar 

  34. Jianming W, Biao G, Xiao Z, Xiaojie D, Xiuyan L (2015) Error correction for high-precision measurement of cylindrical objects diameter based on machine vision. 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). IEEE, Qingdao, China, pp 1113–1117. https://doi.org/10.1109/ICEMI.2015.7494414

    Chapter  Google Scholar 

  35. Sturm P (2014) Pinhole camera model. In: Ikeuchi K (ed) Computer Vision. Springer, US, Boston, MA, pp 610–613. https://doi.org/10.1007/978-0-387-31439-6_472

    Chapter  Google Scholar 

  36. Soria X, Riba E, Sappa A (2020) Dense extreme inception network: towards a robust CNN model for edge detection. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, Snowmass Village, CO, USA, pp 1912–1921. https://doi.org/10.1109/WACV45572.2020.9093290

    Chapter  Google Scholar 

  37. Fu KS, Mui JK (1981) A survey on image segmentation. Pattern Recogn 13:3–16. https://doi.org/10.1016/0031-3203(81)90028-5

    Article  MathSciNet  ADS  Google Scholar 

  38. Pratt WK (2007) Digital image processing: PIKS scientific inside, 1st edn. Wiley. https://doi.org/10.1002/0470097434

    Book  Google Scholar 

  39. Bradski GR, Kaehler A (2011) Learning OpenCV: computer vision with the OpenCV library, 1. ed., [Nachdr.], O’Reilly, Beijing

  40. Minichino J, Howse J (2015) Learning OpenCV 3 computer vision with Python: unleash the power of computer vision with Python using OpenCV, 2nd edn. Packt Publishing, Birmingham Mumbai

    Google Scholar 

  41. Lehmann TM, Gonner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Trans Med Imaging 18:1049–1075. https://doi.org/10.1109/42.816070

    Article  CAS  PubMed  Google Scholar 

  42. Bouhouche S, Ziani S, Mentouri Z, Bast J (2015) Uncertainty estimation of mechanical testing properties using sensitivity analysis and stochastic modelling. Measurement 62:149–154. https://doi.org/10.1016/j.measurement.2014.10.036

    Article  ADS  Google Scholar 

  43. Sadaoui SE, Mehdi-Souzani C, Lartigue C (2019) Computer-aided inspection planning: a multisensor high-level inspection planning strategy. J Comput Inf Sci Eng 19:021005. https://doi.org/10.1115/1.4041970

    Article  Google Scholar 

  44. Sadaoui SE, Mehdi-Souzani C, Lartigue C (2018) Combining a touch probe and a laser sensor for 3D part inspection on CMM. Procedia CIRP 67:398–403. https://doi.org/10.1016/j.procir.2017.12.232

    Article  Google Scholar 

  45. Phan NDM, Quinsat Y, Lartigue C (2019) Optimal scanning strategy for on-machine inspection with laser-plane sensor. Int J Adv Manuf Technol 103:4563–4576. https://doi.org/10.1007/s00170-019-03877-x

    Article  Google Scholar 

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Correspondence to Sif Eddine Sadaoui.

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Taatali, A., Sadaoui, S.E., Louar, M.A. et al. On-machine dimensional inspection: machine vision-based approach. Int J Adv Manuf Technol 131, 393–407 (2024). https://doi.org/10.1007/s00170-024-13081-1

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