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基于BP神经网络风力发电机轴承座的铸造工艺参数预测模型
Casing Process Parameter Prediction Model of Wind Turbine Bearing Pedestal Based on BP Neural Network
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- DOI:
- 作者:
- 陈德平 陈营 罗建设 谢建 陈立明
CHEN Deping;CHEN Ying;LUO Jianshe;XIE Jian;CHEN Liming
- 作者单位:
- 成都工业学院材料工程学院
School of Materials Engineering, Chengdu Technological College, Chengdu 611730, China
- 关键词:
- BP神经网络 正交试验 浇注工艺参数 缩孔缩松 数值模拟
BP neural network orthogonal test casting process parameters shrinkage cavity and porosity numerical simulation
- 摘要:
- 基于KBE概念和BP神经网络,结合正交试验设计方法和铸造模拟建立了大型风力发电机轴承座铸件品质的预测模型。浇注温度、浇注时间和模具初始温度作为BP网络训练样本的输入值,基于Procast铸造模拟软件仿真得到的轴承座缩松缺陷面积、轴承座凝固时间、轴承座凝固后铸件最大温差作为模型目标值。结果表明,利用该模型可预测铸件任意工艺参数组合下的结果值,经过模拟试验和预测值的对比,两种方式获得的结果十分吻合,从而缩短大型铸件研发周期,降低了试制成本,并能给出最佳工艺参数组合,对实际生产可以进行快速高效的指导。A prediction model for the casting quality of large wind turbine bearing pedestal based on the concept of KBE and BP neural network was established combining the orthogonal test design method and casting simulation. The pouring temperature, pouring time and the initial temperature of the mold were taken as the input values of BP network training samples, and the shrinkage cavity defect area of the bearing seat, the solidification time of the bearing seat and the maximum temperature difference of the casting after the solidification of the bearing seat obtained by the simulation software Procast were taken as the model target values. The results show that using this model can predict the result value of random combinations of process parameters of castings, through simulation test and the comparison of predictive value,the results obtained are consistent with in two ways, to shorten the development cycle, large casting reduces the manufacture cost, and can give the best process parameter combination and guidelines for the actual production can be fast and efficient.