Welding process optimization for blast furnace shell by numerical simulation and experimental study

At cold temperatures, the stress after welding a blast furnace shell is not completely released because of the fast cooling speed, which results in cracks. To solve this problem, an integrated method combining gray relational analysis, back propagation (BP) neural network, and genetic algorithm-BP (GA-BP) was proposed in this study. Its aim was to analyze the dominant mechanism of residual stress in multi-layer and multi-pass welding process under the heating of ceramic sheet and achieve super-objective collaborative optimization of residual stress. GRA indicated that through the correlation analysis of welding parameters, residual stress, maximum temperature of characteristic point, and deformation, welding speed was determined to be an all-important factor affecting welding quality. A BP prediction model of residual stress was established. The predicted value was in good agreement with the experimental value, and the maximum error was 8.75%. The influence of each factor on the quality of welding seam is not a single, but a complex mechanism of mutual influence. GA-BP determined the combination of low level welding speed 28 mm/min, medium level welding current 210A and medium and low level welding voltage 21V as the optimal solution of low residual stress. The maximum prediction error of residual stress was 13.76%. The experimental results show that the maximum prediction error of residual stress is 4.4%, and the optimized weld has better macro and micro structure, which proves the feasibility of the multi-objective optimization method. Most importantly, the proposed method can effectively identify the optimal welding parameters, which can provide a reliable theoretical basis for engineering practice.& COPY; 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).