Using neural networks to predict the low curves and processing maps of TNM-B1

• Autorzy: Stendal Johan Andreas , Emdadi Aliakbar , Sizova Irina , Bambach Markus
• Języki publikacji: EN

Abstrakty

EN

The ability to predict the behavior of a material is vital in both science and engineering. Traditionally, this task has been carried out using physics-based mathematical modeling. However, material behavior is dependent on a wide range of interconnected phenomena, properties and conditions. During deformation processes, work hardening, softening, microstructure evolution and generation of heat all occur simultaneously, and can either cooperate or compete. In addition, they can vary with the deformation temperature, applied force and process speed. As the complete picture of material behavior from the macroscopic scale to the atomic scale is not yet fully understood, deformation processes such as hot forging can be difficult to handle using physics-based modeling. Usually, modeling the high temperature deformation behavior of metals consists of extracting characteristic points from the experimental flow curve data, and use them to fit the model equations through regression analysis. This is called phenomenological modeling, as it is based on the observations of a phenomena rather than being derived from fundamental theory. Alternatively, the data obtained from experiments could be used for a data-driven or machine learning (ML) approach to model the material behavior. An ML model would require no knowledge of the underlying physical phenomena governing a deformation process, as it can learn a mapping function which connects input to output based purely on the experimental data. In this work, the application of machine learning to modeling the flow curves of two different states of the titanium aluminide (TiAl) TNM-B1; hot isostatically pressed (HIPed) and heat treated, is investigated. Neural networks were used to learn a mapping function which predicted flow stress based on the inputs temperature, strain and strain rate. In addition, strain rate sensitivity maps and processing maps based on the experimental and the predicted data are analysed and compared. The results revealed that the neural networks were able to produce realistic and accurate flow curves, which fitted to the underlying behavior of the experimental data rather than the noise. The strain rate sensitivity and processing maps showed conflicting results. Good correlation was found for the HIPed material state between the ones based on experimental data and the ones based on predicted values, while there was a significant difference for the heat treated state.

Słowa kluczowe

EN

machine learning; processing maps; titanium aluminide; TNM-B1

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Methods in Materials Science
• Rocznik: 2018
• Tom: Vol. 18, No. 4
• Strony: 134--142
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Stendal Johan Andreas

• Panta Rhei Gebäude, Konrad-Wachsmann-Allee 17, 03046 Cottbus, Germany

autor

Emdadi Aliakbar

• Panta Rhei Gebäude, Konrad-Wachsmann-Allee 17, 03046 Cottbus, Germany

autor

Sizova Irina

• Panta Rhei Gebäude, Konrad-Wachsmann-Allee 17, 03046 Cottbus, Germany

autor

Bambach Markus

• Panta Rhei Gebäude, Konrad-Wachsmann-Allee 17, 03046 Cottbus, Germany

Bibliografia

• Anderson, J.A., 1995, An Introduction to Neural Networks, A Bradford Book, The MIT Press.
• Bambach, M., Sizova, I., Bolz, S. et al., 2016, Devising Strain Hardening Models Using Kocks–Mecking Plots—A Comparison of Model Development for Titanium Aluminides and Case Hardening Steel, Metals, 6, 204.
• Cheng, L., Xue, X., Tang, B. et al., 2014, Flow characteristics and constitutive modeling for elevated temperature deformation of a high Nb containing TiAl alloy, Intermetallics, 49, 23–28.
• Cingara, A., McQueen, H.J., 1992, New formula for calculating flow curves from high temperature constitutive data for 300 austenitic steels, Journal of Materials Processing Technology, 36, 31–42.
• Inui, H., Kishida, K., Misak, M. et al., 1995, Temperature dependence of yield stress, tensile elongation and deformation structures in polysynthetically twinned crystals of Ti-Al, Philosophical Magazine A, 72, 1609-1631.
• Levenberg, K., 1944, A Method for the solution of certain nonlinear problems in least squares, Quarterly of Applied Mathematics, 2, 164-168.
• Lin, Y.C., Zhang, J., Zhong, J., 2008, Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel, Computational Materials Science, 43, 752-758.
• Liu, B., Liu, Y., Qiu, C. et al., 2015, Design of low-cost titanium aluminide intermetallics, Journal of Alloys and Compounds, 650, 298-304.
• Liu, Y., Zhao, T., Ju, W. et al., 2017, Materials discovery and design using machine learning, Computational Materials Science, 3, 159-177.
• Marquardt, D., 1963, An algorithm for least-squares estimation of nonlinear parameters, SIAM Journal on Applied Mathematics, 11, 431–441.
• Masahashi, N., Mizuhara, Y., Matsuo, M. et al., 1991, High temperature behavior of titanium-aluminide based gamma plus beta microduplex alloy, ISIJ International, 31, 728-737.
• Pilania, G., Wang, C., Jiang, X. et al., 2013, Accelerating materials property predictions using machine learning, Scientific Reports, 3.
• Prasad, Y.V.R.K., Sasidhara, S., 1997, Hot working guide: a compendium of processing maps, ASM International, Materials Park.
• Prasad, Y.V.R.K., Seshacharyulu, T., 1998, Processing maps for hot working of titanium alloys, 243, 82-88.
• Rojas, R., 1996, Neural Networks: A Systematic Introduction, Springer.
• Sheikh, H., Serajzadeh, S., 2008, Estimation of flow stress behavior of AA5083 using artificial neural networks with regard to dynamic strain ageing effect, Journal of Materials Processing Technology, 196, 115-119.
• Schloffer, M., Iqbal, F., Gabrisch, H. et al., 2012, Microstructure development and hardness of a powder metallurgical multi phase γ-TiAl based alloy, Intermetallics, 22, 231-240.
• Yu, W., Li, M.Q., Luo, J. et al., 2010, Prediction of the mechanical properties of the post-forged Ti–6Al–4V alloy using fuzzy neural network, Materials & Design, 31, 3282-3288.
• Zhu, Q., Abbod, M.F., Talamantes-Silva, J., Sellars, C.M. et al., 2003, Hybrid modelling of aluminium–magnesium alloys during thermomechanical processing in terms of physically-based, neuro-fuzzy and finite element models, Acta Materialia,51, 5051-5062.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019)