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  • 基于偏最小二乘回歸模型的帶鋼熱鍍鋅質量監控方法

    Quality monitoring method of strip hot-dip galvanizing based on partial least squares regression

    • 摘要: 提出了基于偏最小二乘回歸模型的帶鋼熱鍍鋅質量監控方法.以帶鋼熱鍍鋅生產中帶鋼力學性能和鋅層質量的質量監控為研究對象,用偏最小二乘方法建立了生產過程參數與質量結果之間的回歸模型,對生產過程控制能力進行了分析,并給出了產品質量的預測方法.用鞍鋼股份有限公司帶鋼熱鍍鋅的實際生產數據進行驗證.結果表明,偏最小二乘法比傳統的多元線性回歸方法具有更好的預測精度,基于偏最小二乘回歸的鋅層質量預測模型,其相對預測誤差可達到5.93%.

       

      Abstract: A quality monitoring method for strip hot-dip galvanizing based on partial least square regression was proposed. Taking the quality monitoring of mechanical properties and zinc coating mass in strip hot-dip galvanizing as the investigated subject, a regression model between process parameters and quality results was constructed through partial least square method. With the regression model, the capability of production process control was analyzed and a production quality prediction method was presented, Real field data from strip hot-dip galvanizing production in Angang Steel Company Limited were used for validation, The results show that partial least square regression has a better predicting precision than traditional multiple linear regression, and that the zinc coating mass prediction model based on partial least square regression has a relative prediction error of 5.93%.

       

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