Simulation of Surface Topography Considering Cut-in Impact and Tool Flank Wear

Gao, Haining; Yue, Caixu; Liu, Xianli; Nan, Yuechong

doi:10.3390/app9040732

Cited by 18 publications

(6 citation statements)

References 25 publications

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“…Zhu et al [33] presented the visualization of UVAM to show the intermittent cutting process. Gao et al [32] obtained the surface topography machined by milling based on the Z-map algorithm and the simulation results were in good agreement with the experimental results. Liu et al [29] took the geometry of the tool into account when establishing simulation model of the surface in ultrasonic vibration-assisted turning.…”

Section: Simulation Of the Micro-textured Surfacementioning

confidence: 55%

“…The end edge of the milling tool is mainly composed of the rake face, flank face and the blade at the intersection of the two faces [30]. Tao et al [31] and Gao et al [32] simplified the double-edge end milling tool as a 2D profile line of the blade edge, which was composed of two symmetrical straight lines. However, the 2D line model is not competent to describe the detailed effect of milling tool due to the absence of the flank face which is vital to the generation of the micro-texture as analyzed in section 2.1.…”

Section: The Tool Modelmentioning

confidence: 99%

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Generation mechanism of surface micro-texture in axial ultrasonic vibration-assisted milling (AUVAM)

Zhang

Wang

Chen

et al. 2022

Int J Adv Manuf Technol

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Although axial ultrasonic vibration-assisted milling (AUVAM) is promising for rapid and economical fabrication of 3D micro-texture on various surfaces, it has been a challenge to obtain micro-texture with small feature size and large height simultaneously.The mechanism and major influence factors for the formation of micro-texture by this method were explored in this study. It was found that the generation of micro-texture was mainly caused by cutting and extrusion from the flank face of the milling tool under ultrasonic vibration. Therefore, a novel 3D tool model that considered the relief angle, end cutting edge angle and the blade profile was established in simulation analysis. The good agreement between simulation and experimental results demonstrated that the influence of the flank face on the micro-texture was mainly attributed to the relief angle of the milling tool. It was the first time to report that both the height and the profile shape of the micro-texture unit were affected by the overlap and extrusion between the flank surface and the micro-texture. But this influence diminished with the increase of spindle speed. 3D sinusoid-shaped micro-texture with minimum width of 20 μm and height of 2 μm (fully reproduced the ultrasonic amplitude) was realized with the relief angle of 40 o and spindle speed of 4000 rpm. Other typical textures of weave, shell, scale and corrugation were also presented to show the effective regulation of texture patterns in AUVAM. This work provides both theoretical and practical basis for such a low-cost, efficient and controllable 3D micro-texture preparation method.

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Section: Simulation Of the Micro-textured Surfacementioning

confidence: 55%

Section: The Tool Modelmentioning

confidence: 99%

Generation mechanism of surface micro-texture in axial ultrasonic vibration-assisted milling (AUVAM)

Zhang

Wang

Chen

et al. 2022

Int J Adv Manuf Technol

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“…The milling dynamics Equation 18can be solved by the Runge-Kutte method, and the vibration displacements x(t), y(t), and z(t) are obtained. In order to fulfill the requirements of calculation accuracy and calculation speed, this paper uses the fourth-order Runge-Kutta method to solve the vibration differential equation [26]. Taking the vibration displacement in the X direction as an example, the detailed process of calculating the vibration differential equation by the fourth-order Runge-Kutta method is given in detail.…”

Section: Effect Of Cutting Vibration On Surface Topographymentioning

confidence: 99%

Modeling of Convex Surface Topography in Milling Process

Hao

Yue

Liu

et al. 2020

Metals

Self Cite

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Cr12MoV die steel is a typical high-strength and high-hardness material. Because of the high hardness of Cr12MoV die steel, which is approximately 50–65 HRC after quenching, and the tool’s weak rigidity, cutting vibration, and tool deformation are inevitable during the cutting process. In this paper, a model for predicting the surface topography of a convex curved die steel machined by a ball-end milling cutter was established. In addition, the surface springback of the workpiece is considered. According to the surface characteristics of the convex curved workpiece, the vector algorithm and transformation matrix are applied to calculate the milling cutter motion trajectory equation. Then, the influence of dynamic factors on the tool path is calculated, and finally the surface topography of the workpiece is simulated through the Z-map model. The simulation error of three-dimensional surface roughness Sa at different positions of the curved surface is between 10% and 16%. After considering the dynamic factors, the simulation error is reduced by about 50%.

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“…Many researchers developed mathematical models to predict the surface roughness of the workpiece to avoid the impact of contact measurement on the workpiece's surface quality. Gao et al [8] proposed a workpiece surface topography prediction model based on Z-MAP method that considers the parameter variations of the cutting system and tool wear. He et al [9] from Southeast University proposed a hybrid model that uses a Hidden Markov model based on Bayesian inference and a support vector machine to evaluate surface roughness.…”

Section: Introductionmentioning

confidence: 99%

A monitoring method for surface roughness of γ-TiAl alloy based on deep learning of time–frequency diagram

Wu,

Liu,

Huang

et al. 2023

Int J Adv Manuf Technol

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γ-TiAl alloy is a typically difficult material to machine, and machining defects such as grain pull-out and material spalling are common during machining, resulting in component scrap.As a result, it is critical to investigate the real-time monitoring method of surface roughness during milling. A new model for predicting surface roughness is proposed in this article. Based on the idea of deep learning, the prediction problem of surface roughness is turned into a classification problem.The features of the force signal are extracted using a continuous wavelet transform, and the onedimensional signal is converted into a two-dimensional time-frequency diagram to obtain more information. The transfer learning mechanism is introduced to make the model run faster and improve prediction accuracy. The overfitting problem is solved and the model's generalization ability is improved through batch normalization and dropout layer, and the prediction accuracy is increased by 3%. The results show that the method has high recognition accuracy, with a maximum classification accuracy of 98% and an average accuracy of 96.12%, a 6% improvement over the traditional model, allowing it to accurately realize real-time monitoring of surface quality in milling processing.

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Simulation of Surface Topography Considering Cut-in Impact and Tool Flank Wear

Cited by 18 publications

References 25 publications

Generation mechanism of surface micro-texture in axial ultrasonic vibration-assisted milling (AUVAM)

Generation mechanism of surface micro-texture in axial ultrasonic vibration-assisted milling (AUVAM)

Modeling of Convex Surface Topography in Milling Process

A monitoring method for surface roughness of γ-TiAl alloy based on deep learning of time–frequency diagram

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