Helisel işleme yay sabitinin belirlenmesi amacıyla parametrik korelasyon tekniğini kullanarak bir yapay sinir ağı geliştirilmesi

Abstract

 Development of artificial neural network for helical machined springs  Utilization of Parametric Correlation Technique  Confirmation of ANN results with FEA and Torsional testsDevelopment of an artificial neural network (ANN) for the determination of the spring constant of machined helical springs, which may be preferred over conventional springs due to their high performance and operating efficiency, is presented. Inıtially, finite element analyses were performed with various dimensional parameters and the obtained spring constant values were verified with the tests performed with the designed test setup. Parametric correlation analysis was performed using the confirmed finite element results and the effect of each spring dimensional parameter on the spring constant was determined. The parameters required for ANN training was determined according to the this correlation result. The spring constant results obtained from the developed ANN was compared with the finite element results confirmed by the tests and it was determined that the ANN was successful in the determination of the spring constant. The importance of parametric correlation analysis has been revealed in the development of ANN. Figure A. (Movement directions of the test setup)Purpose: The main purpose of this study is; To determine the effects of the design parameters of machining springs working under angular displacement on spring stiffness and stress by experimental and numerical methods, Verifying numerical approaches by experimental methods, To develop an artificial neural network that enables us to quickly obtain the torsion spring constant value depending on the processing spring design parameters.Theory and Methods: Torsion spring constants of different sizes of machining springs were determined by experimental and numerical methods. Results:In the results of this study; A maximum of 1.6% difference between the parameters in ANN training data set and finite element results and a maximum of 8% in intermediate values were observed. Considering the differences between the test and finite element analysis, this difference was observed to be at an acceptable level. The results obtained with the values selected outside the database set show an increase in% difference values. It is concluded that this developed ANN model is not suitable to be used for values outside the database. Conclusion:As a result of this study; An artificial neural network (ANN) based on parameter correlation was developed to determine the spring constants of the machined springs that are frequently used in the aviation industry.