Research on Performance Optimization of Small-Sized Turbine Drilling Tools Based on Machine Learning
In order to solve the drawbacks brought about by the small torque of the small-sized turbine drilling tools,an optimiza-tion model based on BP neural network and non-dominated sorting genetic algorithm(NSGA-II)was established,the turbine structure size was optimized to obtain a turbine with greater torque under high efficiency.Sensitivity analysis of torque and efficiency was conduc-ted using weight estimation and Garson algorithm,multiple machine learning regression models were compared for their fitting perform-ance and back propagation neural network(BPNN)was selected to establish the regression model between torque&efficiency and de-sign parameters.Finally,the non-dominated sorting genetic algorithm II(NSGA-II)was used to search for the Pareto optimal solution sets.The results show that the installation angle has the greatest impact on the output torque,and the number of blades has the greatest impact on output efficiency.The regression model constructed with BP neural network exhibits the highest accuracy.The optimized tur-bine has 1.2 times more torque and 1.35%more efficiency than the initial turbine.
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