Prediction of the Charpy V-notch impact energy of low carbon steel using a shallow neural network and deep learning

Abstract

The impact energy prediction model of low carbon steel was investigated based on industrial data. A three-layer neural network, extreme learning machine, and deep neural network were compared with different activation functions, structure parameters, and training functions. Bayesian optimization was used to determine the optimal hyper-parameters of the deep neural network. The model with the best performance was applied to investigate the importance of process parameter variables on the impact energy of low carbon steel. The results show that the deep neural network obtains better prediction results than those of a shallow neural network because of the multiple hidden layers improving the learning ability of the model. Among the models, the Bayesian optimization deep neural network achieves the highest correlation coefficient of 0.9536, the lowest mean absolute relative error of 0.0843, and the lowest root mean square error of 17.34 J for predicting the impact energy of low carbon steel. Among the variables, the main factors affecting the impact energy of low carbon steel with a final thickness of 7.5 mm are the thickness of the original slab, the thickness of intermediate slab, and the rough rolling exit temperature from the specific hot rolling production line.

Keywords

prediction / shallow neural network / deep neural network / impact energy / low carbon steel

References

[1] Guo S, Yu JX, Liu XJ, Wang CP, Jiang QS. A predicting model for properties of steel using the industrial big data based on machine learning. Comput. Mater. Sci., 2019, 160, 95.

[2] Desu RK, Krishnamurthy HN, Balu A, Gupta AK, Singh SK. Mechanical properties of austenitic stainless steel 304L and 316L at elevated temperatures. J. Mater. Res. Technol., 2016, 5(1): 13.

[3] Lakshmi AA, Rao CS, Srikanth M, Faisal K, Fayaz K, Puspalatha Singh SK. Prediction of mechanical properties of ASS 304 in superplastic region using artificial neural networks. Mater. Today: Proc., 2018, 5(2): 3704.

[4] Kanumuri L, Pushpalatha DV, Naidu ASK, Singh SK. A hybrid neural network — Genetic algorithm for prediction of mechanical properties of ASS-304 at elevated temperatures. Mater. Today: Proc., 2017, 4(2): 746.

[5] Powar A, Date P. Modeling of microstructure and mechanical properties of heat treated components by using artificial neural network. Mater. Sci. Eng. A, 2015, 628, 89.

[6] Lalam S, Tiwari PK, Sahoo S, Dalal AK. Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks. Ironmaking Steelmaking, 2019, 46(1): 89.

[7] Thankachan T, Sooryaprakash K. Artificial neural network-based modeling for impact energy of cast duplex stainless steel. Arabian J. Sci. Eng., 2018, 43(3): 1335.

[8] Colas-Marquez R, Mahfouf M. Data mining and modelling of Charpy impact energy for alloy steels using fuzzy rough sets. IFAC-Papersonline, 2017, 50(1): 14970.

[9] Mahfouf M, Yang YY. A GA-optimised ensemble neural network model for Charpy impact energy predictions. IFAC Proc. Vol., 2010, 43(9): 62.

[10] Jimenez-Martinez M, Alfaro-Ponce M. Fatigue damage effect approach by artificial neural network. Int. J. Fatigue, 2019, 124, 42.

[11] Liu Y, Zhu JC, Cao Y. Modeling effects of alloying elements and heat treatment parameters on mechanical properties of hot die steel with back-propagation artificial neural network. J. Iron Steel Res. Int., 2017, 24(12): 1254.

[12] Xu ZW, Liu XM, Zhang K. Mechanical properties prediction for hot rolled alloy steel using convolutional neural network. IEEE Access, 2019, 7, 47068.

[13] Deng JF, Sun J, Peng W, Hu YH, Zhang DH. Application of neural networks for predicting hot-rolled strip crown. Appl. Soft Comput., 2019, 78, 119.

[14] Wu SW, Yang J, Zhang RH, Ono H. Prediction of endpoint sulfur content in KR desulfurization based on the hybrid algorithm combining artificial neural network with SAPSO. IEEE Access, 2020, 8, 33778.

[15] Wu SW, Ren JK, Zhou XG, Cao GM, Liu ZY, Yang J. Comparisons of different data-driven modeling techniques for predicting tensile strength of X70 pipeline steels. Trans. Indian Inst. Met., 2019, 72(5): 1277.

[16] Huang GB, Zhu QY, Siew CK. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1–3): 489.

[17] G.B. Huang, Q.Y. Zhu, and C.K. Siew, Extreme learning machine: A new learning scheme of feedforward neural networks, [in] 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), Vol. 2, Budapest, 2004, p. 985.

[18] Li MB, Huang GB, Saratchandran P, Sundararajan N. Fully complex extreme learning machine. Neurocomputing, 2005, 68, 306.

[19] Sui XY, Lv ZM. Prediction of the mechanical properties of hot rolling products by using attribute reduction ELM. Int. J. Adv. Manuf. Technol., 2016, 85(5–8): 1395.

[20] Su XL, Zhang S, Yin YX, Liu YN, Xiao WD. Data-driven prediction model for adjusting burden distribution matrix of blast furnace based on improved multilayer extreme learning machine. Soft Comput., 2018, 22(11): 3575.

[21] Su XL, Zhang S, Yin YX, Xiao WD. Prediction model of hot metal temperature for blast furnace based on improved multi-layer extreme learning machine. Int. J. Mach. Learn. Cybern., 2019, 10(10): 2739.

[22] Feng S, Zhou HY, Dong HB. Using deep neural network with small dataset to predict material defects. Mater. Des., 2019, 162, 300.

[23] D.P. Kingma and J.L. Ba, Adam: A method for stochastic optimization, [in] 3rd International Conference for Learning Representations, San Diego, 2015.

[24] Yoo Y. Hyperparameter optimization of deep neural network using univariate dynamic encoding algorithm for searches. Knowledge-Based Syst., 2019, 178, 74.

[25] X.Q. Zeng and G. Luo, Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection, Health Inf. Sci. Syst., 5(2017), No. 1, art. No. 2.

[26] J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, Algorithms for hyper-parameter optimization, [in] J. Shawe-Taylor, R.S. Zemel, P.L. Bartlett, F. Pereira, and K.Q. Weinberger, eds., Proceedings of the 24th International Conference on Neural Information Processing Systems, Granada, 2011, p. 2546.

[27] Wu J, Chen XY, Zhang H, Xiong LD, Lei H, Deng SH. Hyperparameter optimization for machine learning models based on Bayesian optimization. J. Electron. Sci. Technol., 2019, 17(1): 26.

[28] T.F. Awolusi, O.L. Oke, O.O. Akinkurolere, A.O. Sojobi, and O.G. Aluko, Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete, Heliyon, 5(2019), No. 1, art. No. e01115.

[29] Kim JS, Kim DY, Kim YT. Experiment on radial inflow turbines and performance prediction using deep neural network for the organic Rankine cycle. Appl. Therm. Eng., 2019, 149, 633.

[30] Pazhoohan J, Beiki H, Esfandyari M. Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank. Int. J. Miner. Metall. Mater., 2019, 26(5): 538.

[31] Elsheikh AH, Sharshir SW, Elaziz MA, Kabeel AE, Wang GL, Zhang HO. Modeling of solar energy systems using artificial neural network: A comprehensive review. Sol. Energy, 2019, 180, 622.

[32] Zhang KQ, Yin HQ, Jiang X, Liu XQ, He F, Deng ZH, Khan DF, Zheng QJ, Qu XH. A novel approach to predict green density by high-velocity compaction based on the materials informatics method. Int. J. Miner. Metall. Mater., 2019, 26(2): 194.

[33] C.C. Qi, A. Fourie, G.W. Ma, X.L. Tang, and X.H. Du, Comparative study of hybrid artificial intelligence approaches for predicting hangingwall stability, J. Comput. Civ. Eng., 32(2018), No. 2, art. No. 04017086.

[34] Wu SW, Liu ZY, Zhou XG, Shi NA. Prediction of mechanical properties and process parameters selection based on big data. J. Iron Steel Res., 2016, 28(12): 1.