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熱軋機有限元與神經網絡集成建模

Integrated model of a hot rolling mill based finite element analysis and neural networks

  • 摘要: 以某鋼廠1580熱連軋生產數據為基礎,提出一種有限元與神經網絡集成建模的方法.該方法首先對軋制過程的塑性變形進行有限元建模,然后結合有限元數值分析方法和智能技術的優點,實現有限元和神經網絡的集成建模.集成模型中的神經網絡模型為有限元模型提供參數調整的依據,并且在神經網絡訓練過程中使用改進的混沌粒子群優化算法對神經網絡進行優化.通過與現場實際生產數據進行比較,驗證了該模型的有效性.

     

    Abstract: According to production data of a 1580 rolling mill, an integrated method combining finite element analysis and neural networks was presented for hot rolling. In the method, plastic deformation during the rolling process was firstly modeled by a finite element method, and then a neural network provided parameter adjustment for the finite element model, so the integrated model had the advantages of neural network and finite element methods. At the same time, intelligent chaos particle swarm optimization (CPSO) was used to optimize weights and thresholds of the network. A comparison between simulation results and actual production data proved the validity of the integrated model.

     

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