Article Sidebar
Main Article Content
Computer vision-based inspection has become widely used in manufacturing industries for part identification, dimensional inspection, and guiding material handling systems. Defect-free production cannot be achieved with sampling inspection methods; therefore, a 100 percentage inspection approach is mandatory to meet the zero-defect goals of manufacturing industries. Achieving this is possible with advanced technologies, such as vision-based inspection systems. In this study, a vision-based inspection system is proposed for part identification, defect detection, and dimensional measurement. The system is validated using machined parts, including a Druck plate, Pressure plate, and Retainer. A part identification algorithm is developed based on a geometry search approach. The inspection algorithm classifies parts based on edge relationships, utilizing edge detection techniques to identify each part's geometric features. Surface defects are identified by analyzing the pixel intensity gradients within defective regions. The system measures part dimensions using a vision system, with results comparable to those obtained from a coordinate measuring machine.
Article Details
D. Aswar. Measuring the dimensions of mechanical component using image processing techniques. International Journal of Advanced Research in Computer Science and Software Engineering 7(9):65. 2017. https://doi.org/10.23956/ijarcsse.v7i9.363. (Crossref)
G. Di Leo, C. Liguori, A. Pietrosanto, and P. Sommella. A vision system for the online quality monitoring of industrial manufacturing. Optics and Lasers in Engineering 89:162-168. 2017. https://doi.org/10.1016/j.optlaseng.2016.05.007. (Crossref)
W. A. Ezat, M. M. Dessouky, and N. A. Ismail. Multi-class image classification using deep learning algorithm. Journal of Physics: Conference Series 1447(1):012021. 2019. https://doi.org/10.1088/1742-6596/1447/1/012021. Fourth International Conference on Advanced Technology and Applied Sciences (ICaTAS 2019). (Crossref)
A. Hossain and S. Sajib. Classification of images using convolutional neural networks (CNN). Global Journal of Computer Science and Technology 19(2):13-18. 2019. https://computerresearch.org/index.php/computer/article/view/1821. (Crossref)
S. Huang and Y. Pan. Automated visual inspection in the semiconductor industry: A survey. Computers in Industry 66(1):1-10. 2015. https://doi.org/10.1016/j.compind.2014.10.006. (Crossref)
M. Javaid, A. Haleem, R. Pratap Singh, S. Rab, and R. Suman. Exploring impact and features of machine vision for progressive industry 4.0 culture. Sensors International 3(100132). 2022. https://doi.org/10.1016/j.sintl.2021.100132. (Crossref)
M. Karimi and D. Asemani. Surface defect detection in tiling industries using digital image processing methods: Analysis and evaluation. ISA Transactions 53(3):834–844. 2014. https://doi.org/10.1016/j.isatra.2013.11.015. (Crossref)
B. Li. Application of machine vision technology in geometric dimension measurement of small parts. EURASIP Journal on Image and Video Processing 2018(1):127. 2018. https://doi.org/10.1186/s13640-018-0364-9. (Crossref)
Y. Liu, L. Guo, H. Gao, Z. You, Y. Ye, et al. Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review. Mechanical Systems and Signal Processing 164:108068. 2022. https://doi.org/10.1016/j.ymssp.2021.108068. (Crossref)
Mitutoyo, Inc. CRYSTA-Apex S544. 2016. https://www.mitutoyo.com/webfoo/wp-content/uploads/CRYSTA_ApexS-_2202.pdf.
D. Moru and D. Borro. A machine vision algorithm for quality control inspection of gears. International Journal of Advanced Manufacturing Technology 106:105-123. 2020. https://doi.org/10.1007/s00170-019-04426-2. (Crossref)
J. K. Park, B. K. Kwon, J. H. Park, and D.-J. Kang. Machine learning-based imaging system for surface defect inspection. International Journal of Precision Engineering and Manufacturing-Green Technology 3:303-310. 2016. https://doi.org/10.1007/s40684-016-0039-x. (Crossref)
M. Ramprasath and M. V. Anand. Image classification using convolutional neural networks. International Journal of Pure and Applied Mathematics 119(17):1307-1319. 2018. https://www.acadpubl.eu/hub/2018-119-17/4/419.pdf.
Z. Ren, F. Fang, N. Yan, and Y. Wu. State of the art in defect detection based on machine vision. International Journal of Precision Engineering and Manufacturing-Green Technology 9:661-691. 2022. https://doi.org/10.1007/s40684-021-00343-6. (Crossref)
V. Shah and N. Sajnani. Multi-class image classification using cnn and tflite. International Journal of Research in Engineering, Science and Management 3(11):65-68. 2020. https://doi.org/10.47607/ijresm.2020.375. (Crossref)
H. Shen, S. Li, D. Gu, and H. Chang. Bearing defect inspection based on machine vision. Measurement 45(4):719-733. 2012. https://doi.org/10.1016/j.measurement.2011.12.018. (Crossref)
K. Sills, G. Bone, and D. Capson. Defect identification on specular machined surfaces. Machine Vision and Applications 25:377-388. 2014. https://doi.org/10.1007/s00138-013-0590-1. (Crossref)
Teledyne Dalsa. PC2-Vision Teledyne Dalsa Analog Frame Grabber. 2008. https://www.teledynevisionsolutions.com/categories/legacy-products/. [Accessed: 19 Apr 2009]. Legacy product, https://www.artisantg.com/TestMeasurement/55133-1/Teledyne-Dalsa-PC2-Vision-Analog-Frame-Grabber.
Teledyne Vision Solutions. Sherlock. 2008. https://www.teledynevisionsolutions.com/products/sherlock/. [Accessed: 19 Apr 2009].
UNIQ VISION, Inc. UM-200, UM-201 EIA/CCIR CCD Camera. 2008. http://uniqvision.com/Products/UM200. [Accessed: 19 Apr 2009].