Improved Fatigue Strength of Steel with HHO Optimized Ensemble Learning
The fatigue strength of steel is an important information required in the design and failure analysis of mechanical components.In actual engineering,the components under fatigue load often crack or even fracture,so the accurate prediction of fatigue strength is especially important.In response to the long calculation period of traditional S-N curves and other issues,the fatigue strength of steel is studied by the Stacking ensemble learning method,and the Harris Hawk optimization algorithm is used to improve the model accuracy,while Piecewise mapping,quasi-inverse learning,and whale predation strategy are introduced together to improve Harris Hawk algorithm(POW-HHO),which helps the algorithm jump out of the local optimal solution and improve the convergence accuracy.By combining the constructed forward prediction model with the POW-HHO algorithm,the inverse design of steel fatigue is carried out,and the Null Importance method is used for feature selection to improve the design efficiency,and the final inverse design results are of certain guiding significance for the study of steel fatigue strength.The experimental re-sults show that the model performs well and has the ability of forward prediction and inverse design with a high accu-racy rate.
Fatigue strength of steelHHOEnsemble learningPredictionInverse design
NETL
NSTL
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