Performance prediction of microalloying copper rods based on machine learning
In order to realize the optimal design of the performance of microalloying copper rods,this paper proposes a method of predicting the performance of microalloying copper rods by XGBoost algorithm in machine learning based on the actual production data of a copper plant in China.The results show that the prediction accuracy of the XGBoost algorithm is high,and the maximum error of tensile strength is 15 MPa and the maximum error of elongation is 0.3%.Through the calculation of the tensile strength and elongation model,when the La content was controlled in the range of 2×10-6-3×10-6 in the production process,the tensile strength and elongation of copper alloy can be controlled in the ranges of 395-405 MPa and 3.85%-3.90%,respectively,to meet the performance and quality requirements of the products.After optimizing the mechanical performance prediction process,the fluctuation amplitude of microalloying copper rods is reduced,which improves the stability of the product properties of copper rods.
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