Browsing by Subject "Bayesian inference"

Now showing 1 - 4 of 4

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
Location recommendations for new businesses using check-in data
(IEEE, 2016-12) Eravci, Bahaeddin; Bulut, Neslihan; Etemoğlu, C.; Ferhatosmanoğlu, Hakan
Location based social networks (LBSN) and mobile applications generate data useful for location oriented business decisions. Companies can get insights about mobility patterns of potential customers and their daily habits on shopping, dining, etc.To enhance customer satisfaction and increase profitability. We introduce a new problem of identifying neighborhoods with a potential of success in a line of business. After partitioning the city into neighborhoods, based on geographical and social distances, we use the similarities of the neighborhoods to identify specific neighborhoods as candidates for investment for a new business opportunity. We present two solutions for this new problem: i) a probabilistic approach based on Bayesian inference for location selection along with a voting based approximation, and ii) an adaptation of collaborative filtering using the similarity of neighborhoods based on co-existence of related venues and check-in patterns. We use Foursquare user check-in and venue location data to evaluate the performance of the proposed approach. Our experiments show promising results for identifying new opportunities and supporting business decisions using increasingly available check-in data sets. © 2016 IEEE.
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.