Browsing by Subject "Finite element simulation"
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Item Open Access Investigating the influence of friction conditions on finite element simulation of microscale machining with the presence of built-up edge(Springer, 2017) Oliaei, S. N. B.; Karpat, Y.In micromachining, the uncut chip thickness is less than the cutting tool edge radius, which results in a large negative effective rake angle. Depending on the material properties, this large negative rake angle promotes built-up edge (BUE) formation. A stable BUE acts like a cutting edge and affects the mechanics of the process. The size of the BUE increases with increasing uncut chip thickness and cutting speed. It also creates a positive rake angle, but it decreases the clearance angle of the tool. A method of including BUE formation in finite element simulations is to use sticking friction conditions at the tip of the tool. However, this approach is shown to be insufficient to simulate BUE formation in microscale machining. Therefore, the cutting edge is modified with the experimental BUE size in the finite element simulations based on experimental measurements. The influence of friction models between BUE and the work material has been investigated, and the study identifies friction coefficients that yield good agreements with experimental results. The finite element model is shown to be capable of simulating process forces and chip shapes for uncut chip thickness values larger than minimum uncut chip thickness.Item Open Access Uncertainty analysis of cutting force coefficients during micromilling of titanium alloy(2017-09) Gözü, ErmanForce modeling based on process input parameters is usually considered as the first step in process modeling. Predicting process forces in micromilling is dif- ficult due to complex interaction between the cutting edge and the work material, size effect, and process dynamics. This study describes the application of Bayesian inference to identify force coefficients in the micromilling process. The Metropolis-Hastings (MH) algorithm Markov chain Monte Carlo (MCMC) approach has been used to identify probability distributions of cutting, edge, and ploughing force coefficients based on experimental measurements and a mechanistic model of micromilling. The Bayesian inference scheme allows for predicting the upper and lower limits of micromilling forces, providing useful information about stability boundary calculations and robust process optimization. In the first part, experiments are performed to investigate the in uence of micromilling process parameters on machining forces, tool edge condition, and surface texture. Built-up edge formation is observed to have a significant in uence on the process outputs in micromilling of titanium alloy Ti6Al4V. In the second part, Bayesian inference is applied to model micromilling forces. The effectiveness of employing Bayesian inference in micromilling force modeling considering special machining cases is discussed. In the third part, finite element simulation of machining processes is employed and process outputs are used to update our knowledge about force coefficients. As a result of uncertainty analysis, the mean and standard deviations of the micromilling forces can be estimated. Bayesian inference can be useful since previous evidence or expertise is insufficient, or when obtaining the related information requires costly and time-consuming machining experiments.