Browsing by Subject "Micromilling"

Now showing 1 - 5 of 5

Open Access
Design and fabrication of micro end mills for the machining of difficult-to-cut materials
(2016-08) Oliaei, Samad Nadimi Bavil
Micromilling is a cost-e ective method of fabricating miniaturized components with complex, three-dimensional features made from di cult-to-cut materials. Microcutting tools are exposed to harsh conditions during machining of such materials, which leads to short tool life and thus a ects the economics of the process. The aim of this thesis is to develop a systematic approach to the design and fabrication of high-precision micro-cutting tools. Machining characteristics of three di erent di cult-to-cut materials–stainless steel, titanium alloy, and silicon–have been investigated using experimental techniques. The results reveal the importance of interaction between tool micro geometry and work material mechanical properties. This observation leads to the development of tailored micro-end mills which are designed and fabricated based on the requirements of the specific machining task. This study also examines in detail built-up edge, an important but usually overlooked issue in micromachining of ductile materials, which a ects the process forces, tool wear, and tool deflections. The protective e ect of built-up edge has been exploited by creating micro-dimples on the tool surface using electrical discharge machining. Its positive influence on tool performance has been demonstrated. As for the micromachining of silicon, the flow of cut material around the cutting edge is paramount in tool design. A novel tool design for machining of silicon has been proposed and its e ectiveness has been validated through experiments. It has been shown that the selection of proper process parameters together with tailored tool design may increase the productivity of micromachining and improve surface quality and dimensional accuracy of micro-scale parts.
Open Access
Investigating the effect of CP titanium microstructure on the mechanics of microscale machining
(2019-09) Aksın, Alp
Metal cutting in microscale brings along many challenges and unanswered questions. Mechanical response of the material to the micro-cutting process is one of them, since feed values and the edge radius of the tool can be in the magnitude of order of the material's grain size. In addition, the grain morphology of the material may affect process outputs. This study investigates microstructure effects of the commercially pure titanium (CP Ti) based on analytical and mechanistic modeling approaches. A slip line field model was studied considering fracture toughness and edge radius effects. Orthogonal micro-cutting tests were performed on different morphologies at feed levels ranging from 0.25 to 6 µm per revolution and cutting force data were collected. Cut chip thickness values were measured by using SEM and used as in-process output in the model. The model outputs were fit to force data and unknown model parameters were identified. Those determined parameters were compared with measurements. The study show that the rake angle and tool edge radius parameters have a consistent disparity between measured and identified values. Evidence of possible wear and material build up at the tool have been observed. Using Bayesian inference, possible range of rake angle values have been further investigated and probability distributions of the rake value were identified for different feed levels. Micromilling of CP titanium has also been considered and a relationship between microscale orthogonal cutting and micromilling has been sought. CP titanium was tested by conducting full immersion micromilling experiments based on mechanistic modeling. In uence of the grain morphology on model coefficients, surface texture and hardness have been discussed.
Open Access
Modelling and analysis of tool deflections in tailored micro end mills
(Inderscience Enterprises, 2019) Oliaei, S. N. B.; Karpat, Yiğit
The deflection of micro end mills has a detrimental effect on surface quality of the machined micro components and adversely affects the achievable dimensional and geometrical tolerances. In this paper, the analysis and modelling of tool deflections of tailored micro end mills have been considered. The tool deflections are obtained using analytical models as well as finite element simulations and verified using a dedicated measurement setup, which uses a capacitive sensor with a nanometre resolution for static tool deflection measurements. The optimisation of the micro end mill geometry has been performed to determine optimum neck taper angle and transition radius of the single edge micro end mill to have minimum tool deflections. With the developed model, tool failure predictions for a given process parameter set can be performed and it can be used for better micro milling process planning.
Open Access
Uncertainty analysis of cutting force coefficients during micromilling of titanium alloy
(2017-09) Gözü, Erman
Force 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.
Open Access
Uncertainty analysis of force coefficients during micromilling of titanium alloy
(Springer, 2017) Gözü, E.; Karpat, Y.
Predicting process forces in micromilling is difficult 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 of the paper, micromilling experiments are performed to investigate the influence of micromilling process parameters on machining forces, tool edge condition, and surface texture. Under the experimental conditions used in this study, built-up edge formation is observed to have a significant influence on the process outputs in micromilling of titanium alloy Ti6Al4V. In the second part, Bayesian inference was explained in detail and applied to model micromilling force prediction. The force predictions are validated with the experimental measurements. The paper concludes with a discussion of the effectiveness of employing Bayesian inference in micromilling force modeling considering special machining cases.