A Real-time Video Measurement System for Quality Control Applications

Yıl 2022, Cilt: 11 Sayı: 2, 22 - 26, 29.06.2022

Fatih Akkoyun

https://doi.org/10.46810/tdfd.1086012

Öz

Quality control is extremely important for manufacturing compatible parts to supply products that meet production requirements. It provides to track and control the stages of the process and minimizes waste by supporting high levels of productivity. Most of the manufacturers prefer a video measurement system (VMS), which offers non-contact high accurate measurement devices, for evaluating machined parts and products. However, due to the advanced technology and low competition the cost of the non-contact measurement devices is high. Besides some facilities and some research laboratories couldn’t reach these high-cost devices. Today, with the help of evolving technology and open-source image processing libraries, it is possible to offer cost-effective and accurate non-contact measurement systems. This study aims to put forward a VMS to measure parts/products in two dimensions with swift and accurate results. The proposed system has an error below 1% and the linear regression coefficient (r2) was found over 0.95. It works in real-time and minimum frequency was found 10 Hz for repetitive measurements, real-time measurement applications. The proposed cost-effective device can be adapted into various quality control applications in industrial manufacturing

Anahtar Kelimeler

Video measurement system, non-contact measuring, quality control, image processing, computer vision

Teşekkür

We thank Sena Sezen and Mert Hatipoglu for his suggestions and recommendations.

Kalite kontrol uygulamaları için gerçek zamanlı bir video ölçüm sistemi

Öz

Quality control is extremely important for manufacturing compatible parts to supply products that meet production requirements. It provides to track and control the stages of the process and minimizes waste by supporting high levels of productivity. Most of the manufacturers prefer a video measurement system (VMS), which offers non-contact high accurate measurement devices, for evaluating machined parts and products. However, due to the advanced technology and low competition the cost of the non-contact measurement devices is high. Besides some facilities and some research laboratories couldn’t reach these high-cost devices. Today, with the help of evolving technology and open-source image processing libraries, it is possible to offer cost-effective and accurate non-contact measurement systems. This study aims to put forward a VMS to measure parts/products in two dimensions with swift and accurate results. The proposed system has an error below 1% and the linear regression coefficient (r2) was found over 0.95. It works in real-time and minimum frequency was found 10 Hz for repetitive measurements, real-time measurement applications. The proposed cost-effective device can be adapted into various quality control applications in industrial manufacturing.

Anahtar Kelimeler

Video measurement system, non-contact measuring, quality control, image processing, computer vision

Kaynakça

• [1] Akkoyun F, Ozcelik A. Rapid Characterization of Cell and Bacteria Counts using Computer Vision. Tr J Nat Sci 2021;10:269–74. https://doi.org/10.46810/tdfd.902441.
• [2] Ozcelik A, Aslan Z. A simple acoustofluidic device for on-chip fabrication of PLGA nanoparticles. Biomicrofluidics 2022;16:014103. https://doi.org/10.1063/5.0081769.
• [3] Akkoyun F, Ercetin A. Automated Grain Counting for the Microstructure of Mg Alloys Using an Image Processing Method. J Mater Eng Perform 2021. https://doi.org/10.1007/s11665-021-06436-2.
• [4] Erçetin A, Aslantaş K, Perçin M. Micro milling of tungsten-copper composite materials produced through powder metallurgy method: Effect of composition and sintering temperature. J Fac Eng Archit Gazi Univ 2018;33:1369–81. https://doi.org/10.17341/gummfd.43569.
• [5] Dwivedi SK, Vishwakarma M, Soni PA. Advances and Researches on Non Destructive Testing: A Review. Mater Today Proc 2018;5:3690–8. https://doi.org/10.1016/j.matpr.2017.11.620.
• [6] Kilic K, Boyaci IH, Koksel H, Kusmenoglu I, Kılıç K, Boyacı İH, et al. A classification system for beans using computer vision system and artificial neural networks. J Food Eng 2007;78:897–904. https://doi.org/10.1016/j.jfoodeng.2005.11.030.
• [7] Seifi M, Gorelik M, Waller J, Hrabe N, Shamsaei N, Daniewicz S, et al. Progress Towards Metal Additive Manufacturing Standardization to Support Qualification and Certification. JOM 2017;69:439–55. https://doi.org/10.1007/s11837-017-2265-2.
• [8] Teagle PR. The quality control and non-destructive evaluation of composite aerospace components. Composites 1983;14:115–28. https://doi.org/10.1016/S0010-4361(83)80007-X.
• [9] Xie H, Tian YQ, Granillo JA, Keller GR. Suitable remote sensing method and data for mapping and measuring active crop fields. Int J Remote Sens 2007;28:395–411. https://doi.org/10.1080/01431160600702673.
• [10] Paap A, Askraba S, Alameh K, Rowe J. Photonic-based spectral reflectance sensor for ground-based plant detection and weed discrimination. Opt Express 2008;16:1051. https://doi.org/10.1364/oe.16.001051.
• [11] Arimoto H, Egawa M. Non-contact skin moisture measurement based on near-infrared spectroscopy. Appl Spectrosc 2004;58:1439–46. https://doi.org/10.1366/0003702042641218.
• [12] El Masri Y, Rakha T. A scoping review of non-destructive testing (NDT) techniques in building performance diagnostic inspections. Constr Build Mater 2020;265:120542. https://doi.org/10.1016/j.conbuildmat.2020.120542.
• [13] Hawkins SA, Jones DR. Prediction modelling of storage time and quality measurements using visible-near infrared spectra of pasteurized shell eggs. J Food Meas Charact 2013;7:101–6. https://doi.org/10.1007/s11694-013-9144-5.
• [14] Cho C, Kim J, Kim J, Lee SJ, Kim KJ. Detecting for high speed flying object using image processing on target place. Cluster Comput 2016;19:285–92. https://doi.org/10.1007/s10586-015-0525-x.
• [15] Kouche A El, Hassanein HS. Ultrasonic Non-Destructive Testing (NDT) Using Wireless Sensor Networks. Procedia Comput Sci 2012;10:136–43. https://doi.org/10.1016/j.procs.2012.06.021.
• [16] Medeossi F, Sorgato M, Bruschi S, Savio E. Novel method for burrs quantitative evaluation in micro-milling. Precis Eng 2018;54:379–87. https://doi.org/10.1016/j.precisioneng.2018.07.007.
• [17] Yaqoob M, Sharma S, Aggarwal P. Imaging techniques in Agro-industry and their applications, a review. J Food Meas Charact 2021;15:2329–43. https://doi.org/10.1007/s11694-021-00809-w.
• [18] Khoyutanov EA, Gavrilov VL. Coal Quality Control in Mining Complex-Structure Deposits. J Min Sci 2019;55:399–406. https://doi.org/10.1134/S1062739119035721.
• [19] Herakovic N, Simic M, Trdic F, Skvarc J. A machine-vision system for automated quality control of welded rings. Mach Vis Appl 2011;22:967–81. https://doi.org/10.1007/s00138-010-0293-9.
• [20] Ghaderi M, Banakar A, Masoudi AA. Using dielectric properties and intelligent methods in separating of hatching eggs during incubation. Measurement 2018;114:191–4. https://doi.org/10.1016/j.measurement.2017.09.038.
• [21] Nyalala I, Okinda C, Nyalala L, Makange N, Chao Q, Chao L, et al. Tomato volume and mass estimation using computer vision and machine learning algorithms: Cherry tomato model. J Food Eng 2019;263:288–98. https://doi.org/10.1016/j.jfoodeng.2019.07.012.
• [22] Prijatna D, Muhaemin M, Wulandari RP, Herwanto T, Saukat M, Sugandi WK. A Study of Light Level Effect on the Accuracy of Image Processing-based Tomato Grading. IOP Conf Ser Earth Environ Sci 2018;147:012005. https://doi.org/10.1088/1755-1315/147/1/012005.
• [23] Çevik ZA, Özsoy K, Erçetin A. The Effect of Machining Processes on the Physical and Surface Morphology of Ti6al4v Specimens Produced Through Powder Bed Fusion Additive Manufacturing. Int J 3D Print Technol Digit Ind 2021;5:187–94. https://doi.org/10.46519/ij3dptdi.947650.
• [24] Erçetin A, Aslantaş K. The effect of different cutting parameters on cutting force, tool wear and burr formation in micro milling WCu composite material fabricated via powder metallurgy. Turkish J Nat Sci 2016;5:1–5.
• [25] Akkoyun F, Ercetin A, Aslantas K, Pimenov DY, Giasin K, Lakshmikanthan A, et al. Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements in Micro-Milling. Sensors 2021;21:4432. https://doi.org/10.3390/s21134432.
• [26] Ercetin A, Akkoyun F, Şimşir E, Pimenov DY, Giasin K, Gowdru Chandrashekarappa MP, et al. Image Processing of Mg-Al-Sn Alloy Microstructures for Determining Phase Ratios and Grain Size and Correction with Manual Measurement. Materials (Basel) 2021;14:5095. https://doi.org/10.3390/ma14175095.
• [27] Akkoyun F, Gucluer S, Ozcelik A. Potential of the acoustic micromanipulation technologies for biomedical research. Biomicrofluidics 2021;15:061301. https://doi.org/10.1063/5.0073596.
• [28] Bradski G, Kaehler A. Learning OpenCV, Computer Vision with OpenCV Library. 2008. https://doi.org/10.1109/MRA.2009.933612.
• [29] Akkoyun F. Inexpensive multispectral imaging device. Instrum Sci Technol 2022:1–17. https://doi.org/10.1080/10739149.2022.2047061.
• [30] Stroustrup B. The C++ Programming Language 3rd Edition. 1989.
• [31] Laliberte AS, Goforth MA, Steele CM, Rango A. Multispectral remote sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments. Remote Sens 2011;3:2529–51. https://doi.org/10.3390/rs3112529.