Abstract
It is of utmost important to maintain perfect condition of complex welded structures such as pressure vessels, load bearing structural members and power plants. The commonly used approach is non-destructive evaluation (NDE) of such welded structures. This paper presents an application of artificial neural networks (ANN) for weld data, extracted from reported radiographic images. Linear Vector Quantization based supervised neural network classifier is implemented in Parallel Processing Environment on PARAM 10000. Single Architecture Single Processor and Single Architecture Multiple Processor based parallel neuro classifier are developed for the weld defect classification. The results obtained for various statistical evaluation methods showed promising future of Single Architecture Single Processor based parallel neuro classifier in the problem domain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, P. (2004), Development of Parallel Neuro Classifier for Weld Defect Classification Problem, B.Tech Thesis, Department of Civil Engineering, IIT Kharagpur, India.
Barai S. V., and Reich, Y. (2002), “Weld classification in radiographic images: data mining approach” Proceedings of National Seminar on Non Destructive Evaluation, held at Chennai during 5–7 December 2002, and organized by Indian Society for Non Destructive Testing hapters in Chennai, Kalpakkam and Sriharikota.
Berger, H. (1977), Nondestructive Testing Standards – A Review, STP 624, ASTM.
Bray, D. E. and Stanley, R. K. (1989), Nondestructive evaluation -A tool for Design, Manufacturing and Service, McGrawH-Hill Book Company
Fayyad, U. M., Piatetsky-Shapiro, G.,Smyth, P. and Uthursamy, R. (1996), Advances in knowledge discovery and data mining, AAAI press/The MIT Press, Cambridge, MA.
Haykin, S. (1994), Neural networks – A comprehensive Foundation, Macmillan College Publishing Company, New York, USA.
Kohonen, T. (1995), Self-Organizing Maps, Berlin: Springer-Verlag.
Labonte, G. and Quintin, M.(1999), “Network Parallel Computing for SOM Neural Networks”, (Royal Military College of Canada), http://www.rmc.ca/academic/math_cs/labonte/HPCS99.pdf
Liao, T. W. and Li, D. (1997), “ Two manufacturing applications of the fuzzy k-NN algorithm”, Fuzzy Sets and Systems, Vol. 92, pp: 289–303.
Liao, T. W., Li, D. M. and Li, Y. M. (1999), “ Detection of welding flaws from radiographic images with fuzzy clustering methods”, Fuzzy Sets and Systems, Vol. 108, pp: 145–158.
Liao, T. W. and Li, Y. (1998), “ An automated radiographic NDT system for weld inspection: Part II – Flow detection”, NDT&E International, Vol. 31, No. 3, pp: 183–192.
Liao,T. W. and Ni, J. (1996), “ An automated radiographic NDT system for weld inspection:Part I – Weld Extraction”, NDT&E International, Vol. 29, No.3, pp: 157–162.
Liao, T. W. and Tang, K. (1997), “ Automated extraction of welds from digitized radiographic images based on MLP neural networks”, Applied Artificial Intelligence, Vol. 11, pp: 197–218.
Liao, T. W., Wang, G., Triantaphyllou, Chang, P. C. (2001), “A data mining study of weld quality models constructed with MLP neural networks from stratified samples data”, Industrial Engineering Research Conference, Dallas, TX, May 20–23, 2001, http://cda4.imse.lsu.edu/PROCEEDINGS_Papers/IERC2001paper.PDF
Lippman, R. P. (1987), “An introduction to computing with neural nets”, IEEE ASSP Magazine, 4, 6–22.
Nafaa, N. Redouane, D. and Amar, B. (2000), “Weld defect extraction and classification in radiographic testing based artificial neural networks”, 15 th WCNDT, Roma 2000, http://www.ndt.net/article/wcndt00/papers/ idn575/idn575.htm
Rao, B. P. C., Raj, B. and Kalyansundaram, P. (2002), “An artificial neural networks for eddy current testing of austenitic stainless steel welds”, NDT & E International, Vol. 35, pp: 393–398
Reich, Y. (1997), Machine learning techniques for civil engineering problems, Microcomputers in Civil Engineering, 12, 4, 307–322.
Reich, Y. and Barai, S. V. (1999), Evaluating machine learning models for engineering problems, Artificial Intelligence in Engineering, 13, 257– 272.
Reich, Y. and Barai, S. V. (2000), A methodology for building neural networks model from empirical engineering data, Engineering Applications of Artificial Intelligence, 13, 685–694.
Seiffert, U.(2002), “Artificial Neural Networks on Massively Parallel Computer Hardware”, (University of Magdeburg, Germany). Proceedings of European Symposium on Artificial Neural Networks Bruges (Belgium), 24–26 April 2002.
Stepinski, T. and Lingvall, F. (2000), “Automatic defect characterization in ultrasonic NDT”, 15 th WCNDT, Roma 2000, http://www.ndt.net/article/wcndt00/papers/idn393/idn393.htm
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this paper
Cite this paper
Barai, S., Agrawal, P. (2006). Parallel Neuro Classifier for Weld Defect Classification. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_3
Download citation
DOI: https://doi.org/10.1007/3-540-31662-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31649-7
Online ISBN: 978-3-540-31662-6
eBook Packages: EngineeringEngineering (R0)