Modelling of machined surface topography and anisotropic texture direction considering stochastic tool grinding error and wear in peripheral milling

Cai, Chongyan; An, Qinglong; Ming, Weiwei; Chen, Ming

doi:10.1016/j.jmatprotec.2021.117065

Cited by 23 publications

(8 citation statements)

References 17 publications

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“…The removal (reduction) of high-frequency measurement errors can be provided with regular filters, also available in commercial software. Firstly, the GF, which is very popular in the analysis of surface topography, has been widely presented in many standards (e.g., firstly in ISO 11562:1996 [62] and revised in ISO 16610-21:2011 [23]) when evaluating surface roughness. However, when the surface contains some extraordinary results (usually points, e.g., spikes), using the GF, it is difficult to extract the shape components [63].…”

Section: Applied Methodsmentioning

confidence: 99%

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

Podulka

2022

Sensors

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Errors that occur when surface topography is measured and analysed can be classified depending on the type of surface studied. Many types of surface topographies are considered when frequency-based errors are studied. However, turned surface topography is not comprehensively studied when data processing errors caused by false estimation (definition and suppression) of selected surface features (form or noise) are analysed. In the present work, the effects of the application of various methods (regular Gaussian regression, robust Gaussian regression, and spline and fast Fourier Transform filters) for the suppression of high-frequency measurement noise from the raw measured data of turned surface topography are presented and compared. The influence and usage of commonly used available commercial software, e.g., autocorrelation function, power spectral density, and texture direction, which function on the values of areal surface topography parameters from selected (ISO 25178) standards, are also introduced. Analysed surfaces were measured with a stylus or via non-contact (optical–white light interferometry) methods. It was found that the characterisation of surface topography, based on the analysis of selected features, can be crucial in reducing measurement and data analysis errors when various filters are applied. Moreover, the application of common functions can be advantageous when feature-based studies are proposed for both profile and areal data processing.

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Section: Applied Methodsmentioning

confidence: 99%

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

Podulka

2022

Sensors

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“…The relative errors of S a were less than 18.09%. Cai et al [14] Kinematics; Dynamics Tool setting error (radial offset and axial tilt), static tool deflection, forced vibration, chatter vibration and stochastic tool grinding error and wear (STGEW)…”

Section: Tool Runoutmentioning

confidence: 99%

A divide and conquer approach for machined surface topography reconstruction in peripheral milling inconel 718

Lyu¹,

Liu²,

Cai³

et al. 2023

Surf. Topogr.: Metrol. Prop.

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This paper proposes a reconstruction procedure to characterize the texture of milled surfaces following the principle of a divide and conquer algorithm. It is a combination of a numerical simulation of the systematic component of the surface topography with the stochastic features of surface roughness. Firstly, the milled surface topographies are decomposed into kinematic and random components, both of them are calculated separately. Then, the kinematic components are numerically simulated based on relative motion between milling tool and workpiece. The random components are numerically simulated by fast Fourier transform (FFT) and the Johnson transform system. The measured and kinematic topographies are aligned fitting orientation and position by point clouds registration. Finally, the milled surface topographies are reconstructed by superposition of kinematic and random components based on the alignment datum. The proposed approach is applied to surface topography simulation and surface roughness prediction in peripheral milling Inconel 718. The milled surface topographies can be simulated by using the milling parameters as input parameters of the proposed model. The validation tests were carried out in Inconel 718 peripheral milling. The results show that the proposed approach has high accuracy in topography simulation and roughness prediction. The relative errors of predicted Sa values are less than 7.55% in all the case studies of this paper. The effect of cutting parameters on roughness Sa is sorted as follows, radial cutting depth > cutting speed > feed rate. In addition, the feed rate greater than 0.01 mm/tooth should be selected as far as possible in milling Inconel 718.

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“…The study of tool surface texture has been gaining popularity in recent years. Although the dry cutting process can reduce the pollution and waste of the cutting fluid, the dry cutting process also tends to increase the friction and adhesion between the tool and the chip contact area [ 1 , 2 , 3 , 4 , 5 ]. More researchers have begun to change the contact area between the tool and the workpiece to reduce tool wear and friction.…”

Section: Introductionmentioning

confidence: 99%

Research on the Influence of Tool Surface Texture on Cutting Performance Based on Finite Element Method

Wang

Yin

2022

Micromachines

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As research progresses, the surface texture tool can significantly reduce the cutting heat and cutting force. However, the tool surface texture width, depth, and spacing also have an impact on the cutting performance. Using the Taguchi method and finite element analysis, the changing laws of cutting temperature, pressure, stress distribution, and cutting force were studied. The results showed that the tool texture width had the greatest influence on the cutting performance, followed by the tool texture depth and spacing. The increase of tool texture width lead to the decrease of cutting temperature, stress distribution, and cutting force, while the effect of texture depth on cutting stress distribution was more significant. Cutting performance could be improved by optimizing the texture size and structure of the cutting tool. This research has theoretical significance for improving the cutting performance of cutting tools.

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Modelling of machined surface topography and anisotropic texture direction considering stochastic tool grinding error and wear in peripheral milling

Cited by 23 publications

References 17 publications

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

A divide and conquer approach for machined surface topography reconstruction in peripheral milling inconel 718

Research on the Influence of Tool Surface Texture on Cutting Performance Based on Finite Element Method

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