Issue 53

Y. Lu et alii, Frattura ed Integrità Strutturale, 53 (2020) 325-336; DOI: 10.3221/IGF-ESIS.53.25 336 [5] Deng, D., Kiyoshima, S., Ogawa, K., Yanagida, N. and Saito, K. (2011). Predicting welding residual stresses in a dissimilar metal girth welded pipe using 3d finite element model with a simplified heat source, Nuclear Engineering and Design, 241(1), pp. 46-54. [6] Xia, J. and Jin, H. (2016). Numerical study of welding simulation and residual stress on butt welding of dissimilar thickness of austenitic stainless steel, International Journal of Advanced Manufacturing Technology, 91(1-4), pp. 1-9. [7] Wei, L., Liang, Z. and Xiaolu, S. (2017). Prediction of welding deformation of ultra-thin plates by inherent strain method, Transactions of The China Welding Institution, 38 (3), pp. 103-106. [8] Jin, Z., Baohua, C., Ye, Z. and Dong, D. (2010). Prediction of welding deformation of aluminum alloy by inherent strain method, Welding Technology, 39(6), pp. 6-10. [9] Liming, L., Guoli, L., Yujun, L., Zan, Z., Chonghua, Z. and Peisheng, L. (2002). Analysis and Prediction of Welding Deformation of Ship High Strength Steel Based on Artificial Neural Network, Transactions of The China Welding Institution, (01), pp. 27-29+33-3. [10] Li, Z., Yan, X., Yuan, C., Peng, Z. and Li, L. (2011). Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method, Mechanical Systems and Signal Processing, 25(7), pp. 2589-2607. [11] Baştanlar Y . and Ozuysal, M. (2012). Introduction to machine learning, An Introduction to Machine Learning. Springer Publishing Company, Incorporated. [12] Chen, B. and Feng, J. (2014). Modeling of underwater wet welding process based on visual and arc sensor, Industrial Robot: An International Journal, 41(3), pp. 311-317. [13] Jiangqi, L., Fengchong, L., Jiqing, C. and Ping, A. Y. (2009). Mechanical properties prediction of the mechanical clinching joints based on genetic algorithm and bp neural network, Chinese Journal of Mechanical Engineering, 22(1), pp. 36-41. [14] Liu, J., Xu, G., Ren, L., Qian, Z. and Ren, L. (2016). Defect intelligent identification in resistance spot welding ultrasonic detection based on wavelet packet and neural network, International Journal of Advanced Manufacturing Technology, 90(9-12), pp. 1-8. [15] Haisheng, L., Long, L., Li, C., Cheng, L. and Qiang, C. (2015). The prediction in computer color matching of dentistry based on ga+bp neural network, Computational and Mathematical Methods in Medicine, pp. 1-7. [16] Qian, L., Huan, T., Jing, W., Qin, Z., Jie, C. and Zhe, Q. W. (2018). Warfarin maintenance dose prediction for patients undergoing heart valve replacement — a hybrid model with genetic algorithm and back-propagation neural network, Scientific Reports, 8(1), pp. 9712 [17] Niu, H., Yu, J. and Huang, Z. (2017). Gray correlation analysis and chebyshev prediction of air gap discharge voltage, International Conference on Electrical Materials & Power Equipment. IEEE. [18] Jian, C., Jingmin, F. and Chenguang, A. (2010). Application of grey correlation analysis in chromatograph peak identification of transformer oil, Power System Technology, 34(7), pp. 206-210. [19] Yanbin, L., Xinyi ， Y. and Zhijie, W. (2013). Risk assessment on photovoltaic power generation project by grey correlation analysis and TOPSIS method, Power System Technology, 37(6), pp. 1514-1519. [20] Xian, S. (2014). The principle and control of the anti-deformation method of weldment in engineering application, Modern Welding Technology, 12, pp. 38-42. [21] Hailaing, X., Xingye, G., Yongping, L., Jian, L., Hanguang, F., Rongshi, X. (2019). Welding deformation of ultra-thin 316 stainless steel plate using pulsed laser welding process, Optics And Laser Technology, 119.