INSPECTION AND QUALITY CHECKING OF CERAMIC CUP USING MACHINE VISION TECHNIQUE: DESIGN AND ANALYSIS
DOI:
https://doi.org/10.11113/jt.v79.11280Keywords:
Inspection, quality checking, machine vision, defect detection, image analysisAbstract
The development of inspection and quality checking using machine vision technique are discussed where the design of the algorithm mainly to detect the sign of defect when a sample product is used for inspection purposes. There are several constraints that a machine need to be improved based on technology used in vision application. CMOS image sensor as well as programming language and open source computer vision library were used in designing the inspection method. Experimental set-up was conducted to test the proposed technique for evaluate the effectiveness process. The experimental results were obtained and represented in graphical and image processing form. Besides, analysis and discussion were made according to obtained results. The proposed technique is able to perform the inspection process using good and defect ceramic cup based on detection technique. Moreover, based on the analysis gathered, the proposed technique able to differentiate between good and defect ceramic cup. The result shows that there is a difference frequency by 236 which is 2% of total value in pixels frequency. The frequency indicated as pixel frequency of image using histogram method based on scaled value of image.
References
T. Brosnan and D. W. Sun. 2004. Improving Quality Inspection of Food Products by Computer Vision-A Review. J. Food Eng. 61(1): 3-16.
C. L. Phillips. 1993. Eliminating Traditional Inspection from the Electronics Manufacturing Process. Proc. Eighth Annu. Appl. Power Electron. Conf. Expo. 244-248.
T. Muraoka. S. Uchimura and H. Ikeda. 2016. Evaluating Visual Fatigue to Keep Eyes Healthy within Circadian Change during Continuous Display Operations. Journal of Display Technology. 12: 1464.
B. C. Jiang, C. C. Wang, and H. C. Liu. 2005. Liquid Crystal Display Surface Uniformity Defect Inspection Using Analysis of Variance and Exponentially Weighted Moving Average Techniques. Int. J. Prod. Res. 43(1): 67-80.
D. Litwiller. 2001. CCD vs. CMOS. Photonics Spectra. 35(1): 154-158.
N. M. Ali, M. S. Karis, A. F. Z. Abidin, B. Bakri, N. R. Abd Razif. 2015. Traffic Sign Detection and Recognition: Review and Analysis. Jurnal Teknologi. 77(20): 107-113.
N. M. Ali, Y. M. Mustafah, N. K. A. Md Rashid. 2013. Performance Analysis of Robust Road Sign Identification. IOP Conference Series: Materials Science and Engineering. 53(1): 012017.
N. M. Ali, M. S. Karis, J. Safei. 2014. Hidden Nodes of Neural Network: Useful Application in Traffic Sign Recognition. IEEE Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). 1-4.
N. M. Ali, N. K. A. Md Rashid, Y. M. Mustafah. 2013. Performance Comparison between RGB and HSV Color Segmentations for Road Signs Detection. Applied Mechanics and Materials. 393: 550-555.
M. S. Karis, N. Hasim, N. M. Ali, M. F. Baharom, A. Ahmad, N. Abas, M. Z. Mohamed, Z. Mokhtar. 2014. Comparative Study for Bitumen Containment Strategy using Different Radar Types for Level Detection. International Conference on Electronic Design (ICED). 161-166.
N. M. Ali, Y. M. Mustafah, M. Rashid, N. K. Alang. 2013. Performance Comparison between ANN and PCA Techniques for Road Signs Recognition. Applied Mechanics and Materials. 393: 611-616.
N. M. Ali, N. M. Sobran, S. A. A. Shukor, M. M. Ghazaly, A. F. T. Ibrahim. 2013. Individual Processing Speed Analysis for Traffic Sign Detetion and Recognition. IEEE International Cofenrence on Smart Instrumentation, Measurement and Application (ICSIMA 2013). 1-5.
N. M. Ali. 2014. Automatic Road Sign Identification System with Robustness to Partial Occlusion. Master Science Thesis. International Islamic University Malaysia (IIUM).
A. H. A. Eissa and A. a. A. Khalik. 2012. Understanding Color Image Processing by Machine Vision for Biological Materials. Struct. Funct. Food Eng. 227-274.
O. Marques. 2011. Histogram Processing. Practical Image and Video Processing Using MATLAB. Florida, John Wiley. 176-180.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
