Prediction of Strength Error in Fe-Ni-Cu-C Low Alloy Based on Multiple Regression
When analyzing the interaction between elements in Fe-Ni-Cu-C alloy using ordinary methods,if the interaction effect is not considered,it will lead to low prediction accuracy.In view of this,a diversified linear regression method is proposed to reduce prediction errors.By understanding the concept of multiple regression and conducting model correlation analysis,five factors affecting the tensile strength of Fe-Ni-Cu-C low alloy were identified.A multiple linear regression model was established to compare and analyze the predicted strength values with the measured values,and the accuracy of the model was tested.The results show that the strength prediction error of Fe-Ni-Cu-C low alloy based on multiple linear regression is small,the model fitting effect is good,and it is suitable for predicting the strength of complex alloy systems.The prediction results can provide guidance for the study of material strength characteristics and performance optimization.
low alloystrengthmultiple linear regression modelinfluencing factorserror analysis
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