Abstract
The purpose of this study is to develop an automated visual inspection system for analysis of the surface appearance of ring varistors based on an adaptive neuro-fuzzy inference system (ANFIS). Known image patterns of the six types of ring varistors are used in a training process to establish Sugeno FIS rules, and the input-output data are then set to train the ANFIS to tune the membership function. Feature extraction reduces image complexity using two-dimensional edge detection, calculated within divided rectangular region. The ANFIS combines the neural network adaptive capabilities and fuzzy logic qualitative to train a classification system for six different types of components. The performance of the ANFIS is evaluated in terms of training performance and classification accuracy. The results confirm that the proposed ANFIS is capable of classifying the six types of ring varistors with an accuracy of 98.67%.
Similar content being viewed by others
References
Gordon GG (1996) Automated glass fragmentation analysis. Machine vision applications in industrial inspection IV. Proc SPIE, San Jose, CA, pp 2665–2675
Caron J, Duvieubourg L, Postaire JG (1997) A hyperbolic filter for defect detection in packaging industry. In: Int conf quality control and artificial vision. Le Creusot, France, pp 207–211
Brzakovic D, Vujovic N (1992) Designing defect classification system: a cause study. Pattern Recogn 29:1401–1419
Fernandze C, Platero C, Campany P, Aracil R (1993) Vision system for online surface inspection in aluminum casting process. Proc IEEE International Conference on Industrial Electrics, Control, Instrumentation and Automation (IECON’93), pp 1854–1859
Caron J, Duvieubourg LJ, Orteu J, Revolte JG (1997) Automatic inspection system for strip of preweathered zinc. In: Int Conf applications of photonic technology. Montréal, Canada, pp 571–576
Torres T et al (1998) Automated real-time visual inspection system for high-resolution superimposed printings. Image Vis Comput 16:947–958
Guerra E, Villalobos JR (2001) A three-dimensional automated visual inspection system for SMT assembly. Comput Ind Eng 40:175–190
Chou PB, Rao AR, Wu FY (1997) Automatic defect classification for semiconductor manufacturing. Mach Vis Appl 4:201–214
Wilson D, Greig A, Gilby J, Smith R (1997) Using uncertainty techniques to aid defect classification in an automated visual inspection system. Industrial Inspection. IEE Colloquium 2/1–2/10
Kashitani A, Takanashi N, Tagawa N (1993) A solder joint inspection system for surface mounted pin grid arrays. In: Proc IEEE International conference on industrial electronics and instrumentation (IECON) ’93, Maui, HA, pp 1865–1870
Li H, Lin JC (1994) Using fuzzy logic to detect dimple defects of polished wafer surfaces. IEEE Trans Ind Appl 30:1530–1543
Sarigul E, Abbott AL, Schmoldt DL (2003) Rule-driven defect detection in CT images of hardwood logs. Comput Electron Agric 41:101–119
Chang J, Han G, Valverde JM, Griswold NC, Duque-Carrillo JF, Cork SE (1997) Quality classification system using a unified image processing and fuzzy-neural network methodology. IEEE Trans Neural Netw 8:964–973
Sarkodie-Gyan T, Lam CW, Hong D, Campbell AW (1997) An efficient object recognition scheme for a prototype component inspection. Mechatronics 7:185–197
Chen YH (1995) Computer vision for general purpose visual inspection: a fuzzy logic approach. Opt Lasers Eng 22:181–192
Jang JSR (1993) ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Sonka M, Hlavac V, Boyle R (1999) Image processing, analysis, and machine vision. PWS Publishing, New York
Van GL, Wambacq P, Oostterlinck A (1991) Intelligence robotic vision systems. In: Intelligent robotic systems. Marcel Dekker, New York, pp 457–507
Morii F (2004) Distortion analysis on discrete Laplacian operators by introducing random images, image and graphics. Proc Third Int Conf, pp 80–83
Gunn SR (1999) On the discrete representation of the Laplacian of Gaussian. Pattern Recogn 32(8):1463–1472
Schwartz WH (1986) Vision system for PC board inspection. Assembly Eng 29(8):8–21
Moganti M, Ercal F (1995) Automatic PCB inspection system. IEEE Potentials 14(3):6–10
Chou PB, Rao AR, Sturzenbecker MC, Wu FY, Brecher VH (1997) Automatic defect classification for semiconductor manufacturing. Mach Vis Appl 9:201–214
Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Upper Saddle River, NJ
Demant G et al (1999) Industrial image processing–visual quality control in manufacturing. Springer, Berlin Heidelberg New York
Author information
Authors and Affiliations
Corresponding author
Additional information
This paper has not been published elsewhere nor has it been submitted for publication elsewhere.
Rights and permissions
About this article
Cite this article
Su, J.C., Tarng, Y.S. Automated visual inspection for surface appearance defects of varistors using an adaptive neuro-fuzzy inference system. Int J Adv Manuf Technol 35, 789–802 (2008). https://doi.org/10.1007/s00170-006-0756-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-006-0756-3