In-Process Machining Distortion Prediction Method Based on Bulk Residual Stresses Estimation from Reduced Layer Removal

Abstract

Manufacturing structural monolithic components for the aerospace market often involves machining distortion, which entails high costs and material and energy waste in industry. Despite the development of distortion calculation and avoidance tools, this issue remains unsolved due to the difficulties in accurately and economically measuring the residual stresses of the machining blanks. In the last years, the on-machine layer removal method has shown its potential for industrial implementation, offering the possibility to obtain final components from blanks with measured residual stresses. However, this measuring method requires too long an implementation time to be used in-process as part of the manufacturing chains. In this sense, the objective of this paper is to provide a machining distortion prediction method based on bulk residual stress estimation and hybrid modelling. The bulk residual stresses estimation is performed using reduced layer removal measurements. Considering bulk residual stress data and machining-induced residual stress data, as well as geometry and material data, real-part distortion calculations can be performed. For this, a hybrid model based on the combination of an analytical formulation and finite element modelling is employed, which enables us to perform fast and accurate calculations. With the developments here presented, the machining distortion can be predicted, and its uncertainty range can be calculated, in a simple and fast way. The accuracy and practicality of these developments are evaluated by comparison with the experimental results, showing the capability of the proposed solution in providing distortion predictions with errors lower than 10% in comparison with the experimental results.