Consistency study on three cutting force modelling methods for peripheral milling

Wan, Min; Zhang, W H; Qin, Guohua; Wang, Zhen-Pei

doi:10.1243/09544054jem1085

Cited by 24 publications

(11 citation statements)

References 27 publications

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“…Some scholars have proposed the concept of instantaneous cutting force coefficient and comprehensively studied its identification method to improve the prediction accuracy of instantaneous cutting force and reduce the amount of experiments [92,152,165,166,191,192]. Shin et al [192] proposed a new method to determine the instantaneous cutting force coefficient required for process simulation through mechanical modeling.…”

Section: Identification Methods Based On Measured Milling Forcementioning

confidence: 99%

“…The factors related to the instantaneous milling force coefficient are shown in Figure 20. Wan et al [191] proposed three calibration methods for instantaneous cutting force coefficient and tool runout parameter in cylindrical milling and analyzed and verified the different parameters of instantaneous milling force coefficient model. Then, Wan et al [193] expressed the instantaneous milling force coefficient as an exponential function of instantaneous uncut chip thickness.…”

Section: Identification Methods Based On Measured Milling Forcementioning

confidence: 99%

“…In these [167] 1995 Tool parameters [168] Helix angle [183] 1997 Cutting force signals [165] 1999 Cutting system deflections [195] 2001 Chip flow angle [116] 2002 Feed; cutter deflection; cutter runout. [174] 2003 Tool parameters [157] Tool parameters [150] Cutter flexibility [196,197] 2004 Deflection; runout [175] 2007 Cutting depth [176,177] Cutter deflection [198] 2008 Milling parameters [188,199] Cutter runout [191] 2009 Tool parameters [178] ALE formulation Cutter runout [51,154] 2011 Cutting geometry [187] 2012 Feed per tooth and cutting speed [179] 2014 Milling parameters [164,183] Chip thickness [194] 2015 Optimization technique [184,185] 2016 Material data [200] 2018 Axial depth; radial width; feed rate [180,181,201] models, accurately predicting the chip force component at the peak or valley position is difficult because of the great difference between the average and instantaneous chip thicknesses. When the instantaneous cutting force coefficient is expressed as an exponential function of the instantaneous cutting thickness, the prediction accuracy of the instantaneous milling force can be improved and the experimental amount of the identification of the cutting coefficient can be reduced.…”

Section: Identification Methods Based On Surface Errormentioning

confidence: 99%

See 2 more Smart Citations

Milling Force Model for Aviation Aluminum Alloy: Academic Insight and Perspective Analysis

Duan

Ding

et al. 2021

Chin. J. Mech. Eng.

164

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Aluminum alloy is the main structural material of aircraft, launch vehicle, spaceship, and space station and is processed by milling. However, tool wear and vibration are the bottlenecks in the milling process of aviation aluminum alloy. The machining accuracy and surface quality of aluminum alloy milling depend on the cutting parameters, material mechanical properties, machine tools, and other parameters. In particular, milling force is the crucial factor to determine material removal and workpiece surface integrity. However, establishing the prediction model of milling force is important and difficult because milling force is the result of multiparameter coupling of process system. The research progress of cutting force model is reviewed from three modeling methods: empirical model, finite element simulation, and instantaneous milling force model. The problems of cutting force modeling are also determined. In view of these problems, the future work direction is proposed in the following four aspects: (1) high-speed milling is adopted for the thin-walled structure of large aviation with large cutting depth, which easily produces high residual stress. The residual stress should be analyzed under this particular condition. (2) Multiple factors (e.g., eccentric swing milling parameters, lubrication conditions, tools, tool and workpiece deformation, and size effect) should be considered comprehensively when modeling instantaneous milling forces, especially for micro milling and complex surface machining. (3) The database of milling force model, including the corresponding workpiece materials, working condition, cutting tools (geometric figures and coatings), and other parameters, should be established. (4) The effect of chatter on the prediction accuracy of milling force cannot be ignored in thin-walled workpiece milling. (5) The cutting force of aviation aluminum alloy milling under the condition of minimum quantity lubrication (mql) and nanofluid mql should be predicted.

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Section: Identification Methods Based On Measured Milling Forcementioning

confidence: 99%

Section: Identification Methods Based On Measured Milling Forcementioning

confidence: 99%

Section: Identification Methods Based On Surface Errormentioning

confidence: 99%

See 1 more Smart Citation

Milling Force Model for Aviation Aluminum Alloy: Academic Insight and Perspective Analysis

Duan

Ding

et al. 2021

Chin. J. Mech. Eng.

164

View full text Add to dashboard Cite

show abstract

“…The approach correlates cutting forces with uncut chip area determined geometrically using Mechanistic constants. 6 The model has matured over the years by incorporating effects of various process characteristics such as size effects, 7 cutting edges, 8 cutter run-out, 9 and so on. It can be inferred that the Mechanistic model can predict cutting forces with reasonable accuracy over a wide range of cutting conditions.…”

Section: Introductionmentioning

confidence: 99%

Modeling of flatness errors in end milling of thin-walled components

Agarwal

Desai

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Machined components deviate in size, form, and orientation in comparison to actual features realized by the designer. The deviations originate from several process-related factors and can be specified as per the Geometric Dimensioning and Tolerancing standards (ASME Y14.5-2009 or ISO 1101:2017). According to these standards, the deviation of planar or flat components is expressed in the form of flatness error. This article presents an overall framework to estimate static deflection–induced flatness errors during end milling of thin-walled planar components. The framework incorporates the Mechanistic force model, finite element analysis–based workpiece deflection model, and particle swarm optimization–based algorithm to estimate flatness-related parameters. The individual elements of the proposed framework are implemented in the form of computational tools, and a set of experiments are conducted on thin-walled parts. It has been observed that the static deflections of the thin-walled component have considerable influence on flatness error, and the same can be captured effectively using the proposed framework. The study also investigates the effect of inevitable aspects of the thin-walled machining, such as workpiece rigidity and thinning on the flatness error. The findings of the present study aid process planners in devising appropriate machining strategies to manufacture thin-walled components within tolerances specified by the designer.

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“…For this reason, many attempts have been made to accurately predict cutting forces under various cutting conditions, leading to the development of analytical [1][2][3][4][5][6][7][8][9][10][11], finite element analysis [12][13][14][15][16][17][18], and mechanistic [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] models. Among them, the mechanistic models have been widely employed for practical purposes because of their ease of use; i.e.…”

Section: Introductionmentioning

confidence: 99%

Error analysis of the cutting coefficients and optimization of calibration procedure for cutting force prediction

Ahn

Hwang

Choi

2010

Proceedings of the Institution of Mechanical Engineers, Part B:

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Cutting force prediction is very important because phenomena occurring during cutting and their resulting outcomes can be predicted and controlled by knowing the cutting forces. The accuracy of predicted cutting forces depends on that of calibration data. Therefore, the accuracy of calibration data needs to be known to estimate the prediction accuracy for cutting forces. In the present study, it is experimentally shown that calibration data used for mechanistic models have a specific probability distribution. A number of calibration data are generated through Monte-Carlo simulation using this probability distribution, resulting in an error index of calibration data ( MPE) and the relationship between error of the predicted cutting forces and the error index. The results from this error analysis are utilized to estimate the prediction accuracy of the cutting forces and to optimize the calibration procedure that ensures the error in the cutting forces is within a given tolerance. The error analysis made in the present study is very practical because the calibration data accumulated in industry for various cutting processes can be directly utilized for the analysis without any additional experiments.

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Consistency study on three cutting force modelling methods for peripheral milling

Cited by 24 publications

References 27 publications

Milling Force Model for Aviation Aluminum Alloy: Academic Insight and Perspective Analysis

Milling Force Model for Aviation Aluminum Alloy: Academic Insight and Perspective Analysis

Modeling of flatness errors in end milling of thin-walled components

Error analysis of the cutting coefficients and optimization of calibration procedure for cutting force prediction

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