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Uncertainty analysis of force coefficients during micromilling of titanium alloy

ORIGINAL ARTICLE

Abstract

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.

Keywords

Micromilling Mechanistic modeling Bayesian inference Markov chain Monte Carlo Uncertainty analysis 

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Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Department of Industrial EngineeringBilkent UniversityAnkaraTurkey
  2. 2.Micro System Design and Manufacturing Center, Department of Mechanical EngineeringBilkent UniversityAnkaraTurkey
  3. 3.UNAM–Institute of Materials Science and NanotechnologyAnkaraTurkey

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