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基于神经网络和PID控制技术,构建了钢管热处理工艺设计和控制模型,并进行了仿真试验和现场验证。结果表明,与产线现用工艺相比,当采用基于神经网络和PID控制的热处理工艺时,32CrMo4钢管的抗拉强度增加了13 MPa,屈服强度增加了19 MPa,断面收缩率增加了5.7%,96 h盐雾腐蚀的质量损失率减少了0.89%,钢管热处理后的性能得到显著提升。
Based on neural network and PID control technology, the steel pipe heat treatment process design and control model is constructed, and the simulation test and field verification are carried out. The results show that the tensile strength of 32CrMo4 steel pipe is increased by 13 MPa, the yield strength is increased by 19 MPa and the reduction of area is increased by 5.7% when the heat treatment process based on neural network and PID control is adopted. , The mass loss rate of salt spray corrosion decreased by 0.89% in 96 h, the performance of steel pipe after heat treatment was significantly improved.