An efficient method to predict the chatter stability of titanium alloy thin-walled workpieces during high-speed milling by considering varying dynamic parameters

Hou, Ning; Jian, Zhao; Sun, ZhengMing; Zhang, Jing

doi:10.1007/s00170-020-05031-4

Cited by 14 publications

(5 citation statements)

References 25 publications

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“…The construction and accuracy of SLD greatly depend on the reliability of input data which include tool structure, specific force coefficients, tool/workpiece material properties, cutting parameters and dynamic properties of the machining system [56]. Once the dynamic chip thickness and specific force coefficients are identified, the SLD can be developed by identifying the frequency response functions (FRFs) of the machining system via experimental tests [58][59][60][61] and simulative methods [62][63][64]. The precise determination of the dynamic properties of the dominant modes is essential to carry out an accurate SLD analysis.…”

Section: Prediction Of the Appearance Of Chattermentioning

confidence: 99%

“…Bravo et al [119], for instance, developed the 3D SLD by considering the spindle speed, cutting depth and workpiece geometric state at each machining step, and predicted chatter by considering the changing dynamic characteristics of both the tool/workpiece subsystems. Jin et al [63], Qu et al [120], and Feng et al [64] performed similar works which include the effect of workpiece geometry or cutting position along the tool-path. Campa et al [26] developed both 3D and 2D SLD for the prediction of stable thin floors machining, which was similar to the above-mentioned studies.…”

Section: Prediction Of the Appearance Of Chattermentioning

confidence: 99%

“…An alternative way for on-line detection is to analyse the machined surface by off-line surface characterization methods [154]. Normally, the characterization of the surface topography can be captured and analysed by using power spectral density (PSD) and relevant signal processing methods [64,132,155], which provide a clear assessment of chatter marks on the machined surface. In PSD analysis, higher magnitudes of PSD plots at a smaller spatial frequency or higher spatial wavelength indicates high amplitudes of the wavy surface profile due to dynamic cutting [156].…”

Section: Active Chatter Control Strategiesmentioning

confidence: 99%

See 2 more Smart Citations

A state-of-art review on chatter and geometric errors in thin-wall machining processes

Pan

et al. 2021

Journal of Manufacturing Processes

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Section: Prediction Of the Appearance Of Chattermentioning

confidence: 99%

Section: Prediction Of the Appearance Of Chattermentioning

confidence: 99%

Section: Active Chatter Control Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

A state-of-art review on chatter and geometric errors in thin-wall machining processes

Pan

et al. 2021

Journal of Manufacturing Processes

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“…In recent years, machine learning methods have been increasingly used to develop self‐learning and automated data‐driven models for manufacturing applications, such as process modeling, 45 condition monitoring, 46 and inspection 47 . The Kriging method 48 and the transfer learning method 49 are, respectively, utilized to predict the position‐dependent tool point dynamics and varying workpiece dynamics. However, these existing methods cannot deal with the prediction of the varying mode shapes, which generally have large dimensions.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian process regression‐based surrogate model of the varying workpiece dynamics for chatter prediction in milling of thin‐walled structures

Yang

Xiao

et al. 2022

Int Journal of Mech Sys Dyn

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Since the dynamics of thin‐walled structures instantaneously varies during the milling process, accurate and efficient prediction of the in‐process workpiece (IPW) dynamics is critical for the prediction of chatter stability of milling of thin‐walled structures. This article presents a surrogate model of the IPW dynamics of thin‐walled structures by combining Gaussian process regression (GPR) with proper orthogonal decomposition (POD) when IPW dynamics at a large number of cutting positions has to be predicted. The GPR method is used to learn the mapping between a set of the known IPW dynamics and the corresponding cutting positions. POD is used to reduce the order of the matrix assembled by the mode shape vectors at different cutting positions, before the GPR model of the IPW mode shape is established. The computation time of the proposed model is mainly composed of the time taken for predicting a known set of IPW dynamics and the time taken for training GPR models. Simulation shows that the proposed model requires less computation time. Moreover, the accuracy of the proposed model is comparable to that of the existing methods. Comparison between the predicted stability lobe diagram and the experimental results shows that IPW dynamics predicted by the proposed model is accurate enough for predicting the stability of milling of thin‐walled structures.

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“…Ti-6Al-4V alloys have the characteristics of high specific strength, good thermal stability, corrosion and creep resistance, which are widely used as aero-engine fans, compressor blades, compressor disks and shells [1][2][3][4]. Ti-6Al-4V alloy parts work in high temperature and pressure environments.…”

Section: Introductionmentioning

confidence: 99%

Discrete characteristic and edge effect during subsurface microhardness measurement of Ti-6Al-4V alloy

Hou

Zhang

Wang

et al. 2022

Mater. Res. Express

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This paper investigates the discrete characteristic and edge effect of subsurface microhardness of Ti-6Al-4V alloy. The results show that the discrete degree of Ti-6Al-4V alloy microhardness decreases with increasing loading force. When the loading force is 200 gf, only five microhardness measurements are needed to reduce the interference of discrete characteristic. Importantly, the edge effect was found during measuring the subsurface microhardness of Ti-6Al-4V alloy. The edge effect means that the microhardness value is little when its indentation position is close to a workpiece edge. Finite element analysis reveals that the low support strength of workpiece edge is responsible for the edge effect. This study further clarifies that a Vickers indenter is not suitable for characterizing the machined subsurface microhardness on account of the limitations of edge effect and indenter size.

show abstract

An efficient method to predict the chatter stability of titanium alloy thin-walled workpieces during high-speed milling by considering varying dynamic parameters

Cited by 14 publications

References 25 publications

A state-of-art review on chatter and geometric errors in thin-wall machining processes

A state-of-art review on chatter and geometric errors in thin-wall machining processes

A Gaussian process regression‐based surrogate model of the varying workpiece dynamics for chatter prediction in milling of thin‐walled structures

Discrete characteristic and edge effect during subsurface microhardness measurement of Ti-6Al-4V alloy

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