Temperature prediction for forging a large S355NL steel flange for offshore wind turbines using neural network and numerical simulation - Yu Chen; Jinfu Zhao; Ling Kong; Jianjun Zhang; Zhixin Huang; Dayong Li; Yuhui Wang (Results in Engineering)
This paper investigates the temperature detection and prediction in S355NL large ring forgings during the forging process. Initially, the process was modeled using a high-precision finite element model. The contact heat transfer coefficient between S355NL low alloy steel and 5CrNiMo die steel was determined through experimental measurements using the steady-state heat flow method. Finite element numerical simulations subsequently mapped the temperature field of large ring forgings across different forging scenarios at varying die speeds. The analysis identified optimal die speeds for the first upsetting, second upsetting, stretching, and punching processes (100–40 mm/s, 100–5 mm/s, 100 mm/s and 100 mm/s, respectively). By applying back propagation (BP) neural network algorithms to finite element simulation data, the study achieved precise modeling of temperature trends in the forging process, with a maximum error below 1 %, enabling online temperature predictions. Experimental validation, conducted using infrared thermal imaging, confirmed that the discrepancy between numerical simulations and actual measurements was <2 %.