Process Mechanics Based Uncertainty Modeling for Cutting Force Prediction in High Speed Micromilling of Ti6Al4V
| Autor: | Kundan K. Singh, Ramesh Singh |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Posterior probability Flexural rigidity Markov chain Monte Carlo 02 engineering and technology Mechanics Bayesian inference Stability (probability) Industrial and Manufacturing Engineering symbols.namesake 020303 mechanical engineering & transports 020901 industrial engineering & automation 0203 mechanical engineering Machining Sampling (signal processing) Artificial Intelligence Convergence (routing) symbols Mathematics |
| Zdroj: | Procedia Manufacturing. 48:273-282 |
| ISSN: | 2351-9789 |
| Popis: | High speed machining is essential to counter the low flexural rigidity of micro-end mill in micromilling process. High speed machining can excite the higher frequency modes and in addition, run-out is also amplified. These effects in high speed micromachining induces the uncertainty in cutting forces. The estimation of cutting coefficients without inclusion of uncertainty cannot give accurate values and hence, the predicted cutting force and machining stability using these cutting coefficient may not be accurate. In the present work, cutting coefficients have been determined using the Bayesian inference which includes the uncertainty in estimated cutting coefficients. Note that, estimated cutting coefficient independent of chip load and cutting velocity does not include the high speed micromachining mechanics like size effect. The cutting coefficients have been estimated as a function of cutting velocity and chip load in the present work. The segmented cutting coefficients for different ranges of cutting speed varying from 10000 rpm to 110000 rpm has been obtained as a non-liner function of chip load. Bayesian inference modelling for cutting coefficients has been carried out using the Metropolis-Hastings (MH) algorithm Markov Chain Monte Carlo (MCMC) method. The convergence of prior and posterior distribution has been verified using correlation and trace of samples used for sampling. The posterior distribution shows that there is a good fitting, which accurately predicts the mean of the cutting coefficients. Finally, predicted cutting coefficients have been compared with cutting coefficients obtained from the deterministic approach using least square method. The experimentally estimated cutting coefficients are found to be lying within the upper and lower limit of predicted cutting coefficients with Bayesian inference approach. The predicted cutting coefficients using Bayesian inference shows the deviation of 0.89% and 8.4% at 40000 rpm from experimentally obtained cutting coefficients for tangential and radial cutting coefficients, respectively. The experimental cutting force is found to be lying within the upper and lower limit of predicted cutting force with Bayesian inference based cutting coefficients fitting at 105000 rpm. |
| Databáze: | OpenAIRE |
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