Chatter modelling in micro-milling by considering process nonlinearities

Afazov, Shukri; Ratchev, Svetan; Segal, Joel; Popov, Atanas A.

doi:10.1016/j.ijmachtools.2011.12.010

Cited by 86 publications

(37 citation statements)

References 17 publications

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“…For instance, after experimental force averages are used to update our prior knowledge, the non-linearity of the force averages at small feed values is compensated and the large confidence intervals narrowed for higher feed values. The Metropolis-Hastings algorithm was applied for 10 4 iterations to obtain samples from the joint target densities of force coefficients, and the first 1000 samples were discarded. Figure 10 shows the traces and sampled force coefficients for normal prior.…”

Section: Bayesian Inference Applied To Milling Force Modelingmentioning

confidence: 99%

“…Controlling the dimensional tolerances and surface quality of microcomponents considering productivity issues is a challenge. Predictive process models, which have been successfully applied to macroscale milling, would be helpful to assist in selecting stable machining conditions, estimating surface location errors, and optimizing process parameters in micromilling [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty analysis of force coefficients during micromilling of titanium alloy

Gözü

2017

Int J Adv Manuf Technol

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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.

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Section: Bayesian Inference Applied To Milling Force Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of force coefficients during micromilling of titanium alloy

Gözü

2017

Int J Adv Manuf Technol

View full text Add to dashboard Cite

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“…These modal parameters can be deduced by using impact hammer test where the frequency response function (FRF) is determined. The dynamics of the workpiece-holder system are described by following two second-order ordinary differential equations for each degree-of-freedom [25]:…”

Section: Chatter Model In Time Domainmentioning

confidence: 99%

“…It is considered that chatter occurs when the statistical variances are bigger than the value of 1 μm 2 , for example see Li et al [24] and Afazov et al [25]. In other words, if the statistical variance in the X and Y direction reaches 1 μm 2 , the system is in the critical state and the minimum stability is determined.…”

Section: Chatter Model In Time Domainmentioning

confidence: 99%

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Three-dimensional stability prediction and chatter analysis in milling of thin-walled plate

Zhao

Wang

2016

Int J Adv Manuf Technol

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Stability lobe diagram can be used for selecting proper milling parameters to perform chatter-free operations and improve productivity during milling of thin-walled plates. This paper studies the machining stability in milling of thin-walled plates and develops a three-dimensional stability lobe diagram of the spindle speed, tool position, and axial depth of cut. The workpiece-holder system is modeled as a 2-degree-of-freedom system considering that the tool system is much more rigid than the thin-walled plate, and dynamic equations of motion described for the workpieceholder system are solved numerically in time domain to compute the dynamic displacements of the thin-walled plate. Statistical variances of the dynamic displacements are then employed as a chatter detection criterion to acquire the stability lobe diagram. The experimentally obtained stability limits correspond well with the predicted stability limits. In addition, influence of feed rate on stability limits is also investigated. By performing frequency analysis of the measured cutting forces to judge if chatter occurs, it is found that feed per tooth has little influence on the stability limits. However, feed per tooth impacts the machined surface quality. The results show that the surface quality drops by increasing feed per tooth.

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Multidimensional Time‐Frequency Control of Micro‐Milling Instability

Suh

Liu

2013

Control of Cutting Vibration and Machining Instability

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Chatter modelling in micro-milling by considering process nonlinearities

Cited by 86 publications

References 17 publications

Uncertainty analysis of force coefficients during micromilling of titanium alloy

Uncertainty analysis of force coefficients during micromilling of titanium alloy

Three-dimensional stability prediction and chatter analysis in milling of thin-walled plate

Multidimensional Time‐Frequency Control of Micro‐Milling Instability

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