摘要
Abstract
This study explores the optimization of welding processes for replacing the middle section of ultra-thick wall large-diameter headers in thermal power plants.Using a neural network-based optimization approach,detailed adjustments and optimizations were made to the welding parameters,welding sequence,and real-time monitoring feedback system.Through comparative experiments with traditional welding processes,the results show that the optimized process exhibits significant advantages in welding quality,weld performance,and work efficiency.Specifically,the quality of welded joints under the optimized process has been significantly improved,with a substantial reduction in internal weld defects and enhanced mechanical properties.Additionally,the welding cycle has been shortened,and material utilization has improved.Furthermore,the optimized process effectively reduces the heat-affected zone,controls welding deformation,and lowers residual stress.Overall,the optimization method adopted in this study significantly improves the welding process for replacing the middle section of ultra-thick wall large-diameter headers in thermal power plants,providing solid technical support for equipment maintenance in the power industry.关键词
火电厂/超厚壁大口径集箱/焊接技术/工艺优化/神经网络Key words
thermal power plant/ultra-thick wall large-diameter header/welding technology/process optimization/neural network分类
矿业与冶金
