Development of the innovative differential tool wear modeling for high-feed milling and its experimental verification

Zhao, Peiyi; Cheng, Kai; Jiang, Bin; Zuo, LinHan

doi:10.1177/0954405420949226

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…In the existing research, 22 the crater wear model is established and the depth of crater wear is obtained by finite element simulation method. However, in this paper, the crater wear model is established by analytical method.…”

Section: Modeling Of Crater Wear Rate On the Tool Rake Facementioning

confidence: 99%

Analytical modeling of tool failure boundary map in milling titanium alloy

Yue

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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As a typical aerospace difficult-to-machine material, tool failure in milling titanium alloy Ti6Al4V will reduce the stability of the milling process and affect the surface quality of the workpiece. Aiming at the fact that cemented carbide tools are prone to wear failure and breakage failure in milling titanium alloy, a safe tool failure boundary map is provided to ensure that the tools will not occur failure with the cutting parameters selected in the safe area during the prediction time. Based on the processing characteristics of Ti6Al4V, the failure boundary map mainly considers three forms of tool failure: flank wear, rake wear, and cutting edge breakage. By revealing the three failure mechanisms, the failure analytical model is established and the failure boundary map is obtained. Compared with the experimental results, it has good consistency, and the research results can provide a reference for the field of titanium alloy cutting process.

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Section: Modeling Of Crater Wear Rate On the Tool Rake Facementioning

confidence: 99%

Analytical modeling of tool failure boundary map in milling titanium alloy

Yue

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Elias et al 16 investigated the worn tool geometry based on the flank wear prediction model. Lastly, Zhao et al 17 developed the innovative differential tool wear modeling for high-feed milling.…”

Section: Introductionmentioning

confidence: 99%

Numerical and experimental analysis of ball screw accuracy reliability with time delay expansion under non-constant operating conditions

Liu

Yang

Hun

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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A ball screw (BS) is a key functional component for precision machining equipment, medical instruments, and intelligent manufacturing production lines. The accuracy reliability of a ball screw plays a key role in the functioning of all such equipment. Since the operating conditions have time-dependent characteristics, the non-constant operating conditions affect the BS accuracy decay. In this paper, the numerical modeling and analysis of the BS accuracy decay are considered under non-constant operating conditions. The BS accuracy reliability under single and multiple non-constant operating conditions is investigated. The experimental findings on accuracy decay under non-constant operating conditions validate the proposed analysis. The average relative error of accuracy decay between the theoretical and the experimental findings is estimated at 10% (in the range of 10.15%–12.66%). The error is evaluated under the three different amplitudes operating conditions, including AL (non-constant axial load), FR (non-constant feed rate), and AL plus FR. The results show that the accuracy decay prediction model successfully predicts the BS accuracy reliability under non-constant operating conditions.

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“…23 Although the model structure is known a priori, experimental data are necessary to identify the model parameters. 10 Exponential lifetime prediction model for ball screw mechanisms under different feed modes, 24 differential models for tool wear evolution in milling 25 and wear model for flank wear in turning 26 are just examples of this; Grey-box models (statistical-based methods): they rely on a dynamic stochastic description of the degradation phenomenon. The model is selected by the user and its coefficients are estimated through experimental data.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel prognostics solution for machine tool sub-units: The hydraulic case

Bernini

Waltz

Albertelli

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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A novel prognostic approach was developed and applied to a machine tool hydraulic unit. Three components were considered: pump, sensor and valve. The proposed methodology exploited a digital twin of the system to perform simulations of the healthy and faulty machine. The digital twin was properly validated through experiments. This approach dealt with the need to carry out time-consuming and expensive experimental campaigns, that is, run-to-failures – not affordable in many industrial applications. The diagnosis module was trained on digital twin simulations and fulfilled the fault detection, isolation and quantification phases. The challenge related to the variability of the operating conditions of the machine was addressed through a robustness analysis of the methodology. The solution successfully dealt with both stationary and non-stationary working conditions. A dedicated classification model was designed for each faulty component, maximising the associated classification rate. The testing procedure consisted of the application of a 10-fold cross-validation to compute the mean classification rates for stationary and non-stationary working conditions. Diagnosis performance results were excellent for the pump, whereas they were lower for the sensor and valve, reaching 79.75% and 74.93% accuracy respectively for the most challenging working cycle. The prognosis directly exploited the output of diagnostics, allowing for experimental effort reduction. Prognosis predictions were built starting from the updated health status provided by the diagnosis output. In order to test the prognosis module, mean and standard deviation of the prediction errors (less than 1.176%) were computed through a Monte Carlo approach. The conceived methodology allowed one of the critical goals of prognostics to be handled: the Remaining Useful Life probability density function estimation.

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Development of the innovative differential tool wear modeling for high-feed milling and its experimental verification

Cited by 9 publications

References 14 publications

Analytical modeling of tool failure boundary map in milling titanium alloy

Analytical modeling of tool failure boundary map in milling titanium alloy

Numerical and experimental analysis of ball screw accuracy reliability with time delay expansion under non-constant operating conditions

A novel prognostics solution for machine tool sub-units: The hydraulic case

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