摘要
对金属带材进行横向磁通感应加热(TFIH)时,通常会存在加热温度分布不均匀以及加热温度偏离目标值两个问题。该文研究了加热器结构参数与电源参数对45号钢带材回火热处理温度的影响,并对两种参数分别进行优化,使带材在加热器出口处的平均温度达到目标值600℃,同时获得均匀的温度分布。采用Morris法对加热器结构参数进行全局灵敏度分析,选取显著影响相对不均匀度的参数并建立径向基函数(RBF)神经网络预测模型。使用多岛遗传算法(MIGA)对筛选的结构参数进行优化,初步获得均匀的温度分布。最后以降低温度分布的相对不均匀度和达到理想平均温度为目标,在优化后的加热器结构基础上使用响应面法(RSM)优化电源参数,实现多目标优化设计。仿真验证结果表明,45号钢带材在加热器出口处的平均温度为600.06℃、相对不均匀度为2.36%,满足45号钢回火热处理的要求。
Transverse flux induction heating(TFIH)devices are widely used in the heat treatment process of flat workpieces,such as metal strips.The heating results affect the application performance of metal strips.Ideal average temperature and uniform temperature distribution of the strip at the heater outlet are desired when the heating state of the strip is stable.The standard tempering temperature of 45#steel is 600℃.This paper divides the optimization parameters into structural and power supply parameters to meet heat treatment requirements.Different methods are used to reduce optimization difficulties while maintaining optimization accuracy.The structure of the TFIH device is determined by seven parameters.The air gap between the magnetic pole and the strip should be as small as possible to meet the processing technology.A global sensitivity analysis(GSA)based on the Morris method ranks the sensitivity of the relative non-uniformity to the six coil structural parameters.The top four structural parameters with significant effects are screened out,ensuring optimization accuracy while reducing calculation time.Due to the strong robustness,the radial basis function(RBF)neural network prediction model replaces the finite element calculation model to estimate nonlinear functions.The optimal Latin hypercube design(OLHD)samples 100 times,and the sampling results are used as the training sample points of the prediction model.After testing,the RBF neural network model has high prediction accuracy within the variation range of the input parameters.The multi-island genetic algorithm(MIGA)optimizes the screened structural parameters globally.The results show that the relative non-uniformity is reduced from 2.88%to 2.38%after optimization,effectively improving the temperature distribution uniformity.Based on the optimized structural parameters,the response surface method(RSM)is used to optimize the current and frequency.Consequently,the average temperature is close to the target value,and the relative non-uniformity is maintained at a low level.The relative non-uniformity and the average temperature expressions for power supply parameters are fitted separately using a second-order polynomial.Both models are tested by the variance(ANOVA)analysis.The multi-objective optimization is then performed using the response optimizer in Design-Expert.The results show that after the optimization of structural parameters and power supply parameters,when the strip heating state reaches stability,the relative non-uniformity of the strip surface temperature distribution at the heater outlet is 2.36%,and the average temperature is 600.06°C,which can meet the tempering requirements of 45#steel strip.
作者
刘志赢
汪友华
刘成成
彭江湃
宋华宾
Liu zhiying;Wang Youhua;Liu Chengcheng;Peng Jiangpai;Song Huabin(State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology,Tianjin 300130 China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology,Tianjin 300130 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2024年第10期3180-3191,共12页
Transactions of China Electrotechnical Society
基金
河北省省级科技计划资助项目(215676146H,225676163GH)。
关键词
横向磁通感应加热
全局灵敏度分析
径向基函数神经网络模型
多岛遗传算法
响应面法
Transverse flux induction heating
global sensitivity analysis
radial basis function neural network model
multi-island genetic algorithm
response surface method
