An examination of the fundamental mechanics of cutting force coefficients

Wang, Ming-Hai; Gao, Lei; Zheng, Yaohui

doi:10.1016/j.ijmachtools.2013.10.008

Cited by 87 publications

(32 citation statements)

References 18 publications

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“…According to Fontaine et al [8] the analytical models focus mainly on the physical mechanisms of chip formation, as slip stress and strain. In contrast, the mechanistic models estimate cutting forces based on the assumption that their values are proportional to the sectional area of cut and specific cutting force coefficients [9]. These models can be used to estimate the cutting forces for a selected combination of cutting parameters and tool geometry.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of machining parameters during ball end milling of hardened steel with various surface inclinations

et al. 2017

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Section: Introductionmentioning

confidence: 99%

Optimisation of machining parameters during ball end milling of hardened steel with various surface inclinations

et al. 2017

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“…According to these studies coefficients are higher at low speed, showing a decrease and then increasing again in high speed area. On the contrary according to Wang et al [15] cutting coefficient is constantly varying with cutting velocity, but only a limited range of speeds has been tested (10-30 m/min). Anyhow all these analyses are affected by uncertainties and errors derived from measuring cutting forces at high rotational speeds.…”

Section: Introductionmentioning

confidence: 99%

Speed-varying cutting force coefficient identification in milling

Grossi

Sallese

Scippa

et al. 2015

Precision Engineering

104

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a b s t r a c tAccurate simulation of the machining process is crucial to improve milling performance, especially in High-Speed Milling, where cutting parameters are pushed to the limit.Various milling critical issues can be analyzed based on accurate prediction of cutting forces, such as chatter stability, dimensional error and surface finish. Cutting force models are based on coefficients that could change with spindle speed. The evaluation of these specific coefficients at higher speed is challenging due to the frequency bandwidth of commercial force sensors. On account of this, coefficients are generally evaluated at low speed and then employed in models for different spindle speeds, possibly reducing accuracy of results.In this paper a deep investigation of cutting force coefficient at different spindle speeds has been carried out, analyzing a wide range of spindle speeds: to overcome transducer dynamics issues, dynamometer signals have been compensated thanks to an improved technique based on Kalman filter estimator. Two different coefficients identification methods have been implemented: the traditional average force method and a proposed instantaneous method based on genetic algorithm and capable of estimating cutting coefficients and tool run-out at the same time.Results show that instantaneous method is more accurate and efficient compared to the average one. On the other hand, the average method does not require compensation since it is based on average signals. Furthermore a significant change of coefficients over spindle speed is highlighted, suggesting that speed-varying coefficient should be useful to improve reliability of simulated forces.

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“…In practice it is estimated by the average or the mean deviation from the mean as shown in (2). For the chosen workpiece and tool material combination, Wang et al (2014) used average forces for X and Y directions [12].…”

Section: Statistical Analysis Methodsmentioning

confidence: 99%

Research and Modelling of Surface Roughness, Cutting Forces and I-kaz Coefficients for S42C in Turning using Response Surface Methodology

2019

IJITEE

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This paper presents the optimization in machining processes on the cutting parameters for the S45C in turning process using the response surface method (RSM). The experimental work conducted investigates the influence of cutting parameters on statistical analysis of signals and surface quality. The paper also presents a statistical analysis of signal processing. The cutting force was measured during machining using the Kistler 9129AA dynamometer to monitor the force signals and the data was analyzed using the I-kazTM method of statistical analysis. This statistical analysis was used to assess the effect of force signals during the machining process. The RSM models for Ra and Rz, and Ideveloped with ANOVA and multiple regression equations. The models also were compared and validated with the predicted and measured of Ra and Rz values, and I-kaz coefficients. The optimal configuration of cutting parameters was observed at 200 m/min, 0.1 mm/rev and 0.521 mm with desirability of 95.9%. It is observed that the models developed are suggested to be utilized for predicting surface roughness values and I-kaz coefficients for the machining of S45C steel.

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An examination of the fundamental mechanics of cutting force coefficients

Cited by 87 publications

References 18 publications

Optimisation of machining parameters during ball end milling of hardened steel with various surface inclinations

Optimisation of machining parameters during ball end milling of hardened steel with various surface inclinations

Speed-varying cutting force coefficient identification in milling

Research and Modelling of Surface Roughness, Cutting Forces and I-kaz Coefficients for S42C in Turning using Response Surface Methodology

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