An Initial Residual Stress Inference Method by Incorporating Monitoring Data and Mechanism Model

Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality issues.The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control.Due to the principle error of existing residual stress detection methods,there are still challenges in practical applications.Aiming at the detection problem of the initial residual stress field,an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper.Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress,and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish;the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model.Finally,the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model.Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.

A novel method for workpiece deformation prediction by amending initial residual stress based on SVR-GA

High-precision manufactured thin-walled pure copper components are widely adopted in precision physics experiments,which require workpieces with extremely high machining accuracy.Double-sided lapping is an ultraprecision machining method for obtaining high-precision surfaces.However,during double-sided lapping,the residual stress of the components tends to cause deformation,which affects the machining accuracy of the workpiece.Therefore,a model to predict workpiece deformation derived from residual stress in actual manufacturing should be established.To improve the accuracy of the prediction model,a novel method for predicting workpiece deformation by amending the initial residual stress slightly based on the support vector regression(SVR)and genetic algorithm(GA)is proposed.Firstly,a finite element method model is established for double-sided lapping to understand the deformation process.Subsequently,the SVR model is utilized to construct the relationship between residual stress and deformation.Next,the GA is used to determine the best residual stress adjustment value based on the actual deformation of the workpiece.Finally,the method is validated via double-sided lapping experiments.

Energy principle and material removal sequence optimization method in machining of aircraft monolithic parts

In the machining process of aircraft monolithic parts,the initial residual stress redistribution and structural stiffness evolution often lead to unexpected distortions.On the other hand,the stress redistribution and stiffness reduction during the machining process depend on the material removal sequence.The essence of the stress redistribution is releasing the initial elastic strain energy.In the present study,the influence of the material removal sequence on the energy release is studied.Moreover,a novel optimization method is proposed for the material removal sequence.In order to evaluate the performance of the proposed method,the mechanism of the machining distortion is firstly analyzed based on the energy principle.Then a calculative model for the machining distortion of long beam parts is established accordingly.Moreover,an energy parameter related to the bending distortion and the procedure of the material removal sequence optimization is defined.Finally,the bending distortion analysis and material removal sequence optimization are performed on a long beam with a Z-shaped cross-section.Furthermore,simulation and experiments are carried out.The obtained results indicate that the optimized sequence results in a low distortion fluctuation and decreases the bending distortion.