Abstract
The aim of this study is to develop predictive Artificial Neural Network (ANN) models for welding process control of a strategic product (155 mm. artillery ammunition) in armed forces’ inventories. The critical process about the production of product is the welding process. In this process, a rotating band is welded to the body of ammunition. This is a multi-input, multi-output process. In order to tackle problems in the welding process 2 different ANN models have been developed in this study. Model 1 is a Backpropagation Neural Network (BPNN) application used for classification of defective and defect-free products. Model 2 is a reverse BPNN application used for predicting input parameters given output values. In addition, with the help of models developed mean values of best values of some input parameters are found for a defect-free weld operation.
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References
Benyounis, K.Y., Olabi, A.G.: Optimization of different welding processes using statistical and numerical approaches – A reference guide. Advances in Eng. Software 39, 483–496 (2008)
Öztemel, E.: Yapay Sinir Ağları, İstanbul; Papatya Yayıncılık (2006)
Sha, W., Edwards, K.L.: The use of artificial neural networks in materials science based research. Materials and Design 28, 1747–1752 (2007)
Chertov, D.A.: Use of Artificial Intelligence Systems in the Metallurgical Industry (Survey). Metallurgist 47, 7–8 (2003)
Tay, K.M., Butler, C.: Modeling and Optimizing of a Mig Welding Process-A Case Study Using Experimental Designs and Neural Networks. Quality and Rel. Eng. Int. 13, 61–70 (1997)
Luo, H., Zenga, H., Hub, L., Hub, X., Zhoub, Z.: Application of artificial neural network in laser welding defect diagnosis. Journal of Materials Processing Technology 170, 403–411 (2005)
Oscar, M., Manuel, L., Fernando, M.: Artificial neural networks for quality control by ultrasonic testing in resistance spot welding. J. of Mat. Processing Technology 183, 226–233 (2007)
Kim, S., Sona, J.S., Leeb, S.H., Yarlagaddac, P.: Optimal design of neural networks for control in robotic arc welding. Robotics and Computer-Integrated Manufacturing 20, 57–63 (2004)
Özerdem, M.S., Sedat, K.: Artificial neural network approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys. Materials and Design 30, 764–769 (2009)
Oscar, M., Pilar De, T., Manuel, L.: Artificial neural networks for pitting potential prediction of resistance spot welding joints of AISI 304 austenitic stainless steel. Corrosion Science 52, 2397–2402 (2010)
Mirapeix, J., García-Allende, P.B., Cobo, A., Conde, O.M., López-Higuera, J.M.: Real-time arc-welding defect detection and classification with principal component analysis and artificial neural Networks. NDT&E International 40, 315–323 (2007)
Pal, S., Pal, K.S., Samantaray, K.: Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals. Journal of Materials Processing Technology 202, 464–474 (2008)
Ateş, H.: Prediction of gas metal arc welding parameters based on artificial neural Networks. Materials and Design 28, 2015–2023 (2007)
Su, T., Jhang, J., Hou, C.: A Hybrid Artificial Neural Network and Particle Swarm Optimization for Function Approximation. International Journal of Innovative Computing 4(9) (2008)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. P. Hall, Inc. (2003)
Nong, Y., Vilbert, S., Chen, Q.: Computer intrusion detection through EWMA for auto correlated and uncorrelated data. IEEE Trans. Reliability 52, 75–82 (2003)
Li, C., Li, S., Zhang, D., Chen, G.: Cryptanalysis of a Chaotic Neural Network Based Multimedia Encryption Scheme. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004, Part III. LNCS, vol. 3333, pp. 418–425. Springer, Heidelberg (2004)
Shihab, K.: A Backpropagation Neural Network for Computer Network Security. Journal of Computer Science 2(9), 710–715 (2006)
Hardalac, F., Barisci, N., Ergun, U.: Classification of aorta insufficiency and stenosis using MLP neural network and neuro-fuzzy system. Physica Medica XX(4) (2004)
Fausett, L.: Fundamentals of neural Networks, architectures, algorithms and applications. Prentice Hall (1994) ISBN: 0-13-334186-0
Marquardt, D.: An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM Journal on Applied Mathematics 11, 431–441 (1963)
Khashei, M., Bijari, M.: An Artificial Neural Network Model (p,d,q) for Timeseries Forecasting. Expert Systems with Applications 37, 479–489 (2010)
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Aktepe, A., Ersöz, S., Lüy, M. (2012). Backpropagation Neural Network Applications for a Welding Process Control Problem. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_18
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DOI: https://doi.org/10.1007/978-3-642-32909-8_18
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