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Artificial Neural Network Application for Parameter Prediction of Heat Induced Distortion

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Trends in Applied Knowledge-Based Systems and Data Science (IEA/AIE 2016)

Abstract

Heat induced distortion has been widely studied over the years, in order to provide reliable results, thermal elastic-plastic FEM analysis have been used to estimate the distortions produced by the heat source. However this type of analysis often involves long computational time and requires high degree of technical knowledge by the user, moreover it’s mainly performed to specific regions that limit the scope of the analysis. In order to provide a tool for the prediction of the line heating phenomena, an artificial neural network (ANN) is used. ANN is a powerful tool to predict complex phenomena, and in addition, it is very attractive because of the relatively modest hardware requirements and fast computational time. In this paper, parameter prediction for the heat induced distortion as an inverse problem is performed by ANN, using, the inherent deformation from a gas heating FEM analysis and their heating conditions as the training data. Exploratory analysis of the data and the model were performed to accurately predict the heating conditions. The prediction of the necessary heating conditions to generate an arbitrary deformation in the plate is a step forward in the automation of the line heating forming process. The possibility of predicting arbitrary heat induced distortion problem by an ANN model is shown.

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References

  1. Tango, Y., Ishiyama, M., Suzuki, H.: IHIMU a fully automated steel plate bending system for shipbuilding. IHI Eng. Rev. 44(1), 6–11 (2011)

    Google Scholar 

  2. Osawa, N., Hashimoto, K., Sawamura, J., Kikuchi, J., Deguchi, Y., Yamaura, T.: Development of heat input estimation technique for simulation of shell forming by line-heating. J. Comput. Model. Eng. Sci. (CMES) 20(1), 45–53 (2007)

    MATH  Google Scholar 

  3. Tango, Y., Ishiyama, M., Nagahara, S., Nagashima, T., Kobayashi, J.: Automated line heating for plate forming by IHI-ALPHA system and its application to construction of actual vessels-system outline and application record to date. J. Soc. Naval Archit. Jpn. 193, 85–95 (2003)

    Article  Google Scholar 

  4. Vega, A.: Development of inherent deformation database for automatic forming of thick steel plates by line heating considering complex heating patterns, Doctoral Dissertation Thesis, Osaka University (2009)

    Google Scholar 

  5. Pinzon, C., Plazaola, C., Banfield, I., Fong, A., Vega, A.: Development of a neural network model to predict distortion during the metal forming process by line heating, ship science & technology, vol 6, no. 12 (2012)

    Google Scholar 

  6. Rashwan, H.: Computer aided planning system for plate bending by line heating. Doctoral Dissertation Thesis, Osaka University (1994)

    Google Scholar 

  7. Blandon, J., Osawa, N., Masanori, S., Rashed, S. and Murakawa, H.: Numerical study on heat straightening process for welding distortion of a stiffened panel structure. In: Proceedings of the Twenty-Fifth International Offshore and Polar Engineering Conference, Kona, Hawaii (2015)

    Google Scholar 

  8. Tarantola, A.: Inverse problem theory and methods for model parameter estimation. In: Society of Industrial and Applied Mathematics (2005)

    Google Scholar 

  9. Haykin, S.: Neural Networks and Learning Machines. Prentice-Hall Inc., Upper Saddle River (1999)

    MATH  Google Scholar 

  10. Fausett, L.: Fundamentals of Neural Networks Architectures: Algorithms and Applications. Prentice-Hall Inc., Upper Saddle River (1994)

    MATH  Google Scholar 

  11. Beale, M., Hagan, M., Demuth, H.: Neural Network Toolbox. The Math Works Inc., Natick (2015)

    Google Scholar 

  12. Hagan, M.T., Menhaj, M.: Training feed-forward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1994)

    Article  Google Scholar 

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Correspondence to Cesar Pinzon .

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© 2016 Springer International Publishing Switzerland

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Pinzon, C., Hasewaga, K., Murakawa, H. (2016). Artificial Neural Network Application for Parameter Prediction of Heat Induced Distortion. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_52

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  • DOI: https://doi.org/10.1007/978-3-319-42007-3_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42006-6

  • Online ISBN: 978-3-319-42007-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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