Modeling and analysis of distortion in milling of aerospace parts

The issue of distortion of parts manufactured by machining is a long-standing problem in the aerospace industry. Especially in the case of large, thin-walled machined structural components, post-machining distortions result in a loss worth billions of dollars to the aerospace industry. Control of distortions is, therefore, very critical to ensure the conformity of these parts and the efficiency of the process. Effective control of the process necessitates efficient models and simulation techniques for predicting distortion of the workpiece after machining. Very high cutting force and temperature loads generated during machining affect the workpiece in several ways. These cutting loads are dictated by the parameters of machining and the material characteristics of the cutting tool and the workpiece. Moreover, the initial stress of the blank also dictates the distortion behavior. Accurate modeling of all these factors is crucial to the precise prediction of distortion of the workpiece. Milling is a very important process for the machining of aerospace parts. In this thesis, modeling of the milling process is carried out for efficient prediction of various aspects of the process. A novel analytical algorithm is proposed to predict the cutting temperature of the workpiece. The proposed algorithm allows accurate calculation of the workpiece temperature by duly incorporating the drop in temperature of the workpiece during the non-engagement periods of the cutting tool. Another novel contribution of this thesis is developing a hybrid FEM-analytical model for predicting distortions of machined thin-walled parts. The model considers loads due to cutting as well as the effect of the initial bulk residual stresses of the material. The measurement of the initial stresses of the material is carried out by the crack compliance method, whereas machining-induced loads are calculated analytically. A novel strategy is devised to incorporate these loads in a FEM model. The results of the proposed models are validated with machining experiments. Another contribution of this thesis is the development of a novel method for monitoring of the machining process using a low-cost infrared sensor. Three critical aspects of the machining process, i.e., tool wear, chatter, and workpiece deformations, are detected using a single infrared sensor. Different signal processing techniques are applied in time and frequency domains to analyze the data collected from the sensor. The results of the proposed method are verified by carrying out machining experiments.