On the tool wear behavior during ultrasonic vibration-assisted form grinding with alumina wheels

Cao, Yang; Zhao, Biao; Ding, Wenfeng; Liu, Yichen; Wang, Lifeng

doi:10.1016/j.ceramint.2021.06.059

Cited by 34 publications

(5 citation statements)

References 34 publications

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“…The average chip length (L CGmax ) is 493 µm and the average width (W Cgmax ) is 25 µm under CG, while the average chip length (L UVAGmax ) is 164 µm and the average width (W UVAGmax ) is 11 µm under UVAG. Meanwhile, pro le grinding of UVAG is subjected to alternating grinding forces and the abrasive grains are susceptible to micro-breakage and maintain sharp, excellent form accuracy on formed surfaces consequently, as reported by Cao et al [35].…”

Section: Ground Surface and Chip Morphologiesmentioning

confidence: 88%

MOPSO process parameter optimization in ultrasonic vibration-assisted grinding of hardened steel

Huang

Zhao

Qiu

et al. 2023

Int J Adv Manuf Technol

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Ultrasonic vibration-assisted grinding (UVAG) is frequently prescribed as an effective technique to improve the grindability of di cult-to-cut materials, earning tremendous application opportunities in the industrial eld. However, the traditional optimization of grinding parameters requires substantial experimental analyses and is prone to fall into a local optimum. In this study, a multiobjective particle swarm optimization (MOPSO) model for grinding forces and surface roughness is established on the basis of comparative experiments between UVAG and conventional grinding. Optimized process parameters are then used to conduct ultrasonic vibration-assisted pro le grinding experiments. Results show that the tangential and normal grinding forces are reduced by 20.51% and 18.91%, respectively, and the ground surface roughness (R a ) is decreased by 9.47%. In addition, the sharpness of grinding wheels can be maintained for UVAG. A Pareto solution set with 15 noninferior solutions is obtained using the MOPSO algorithm, suggesting that the good surface roughness is realized using larger wheel speed and cutting depth and a smaller feed speed. Finally, forming workpieces with excellent shape accuracy and high surface quality, as well as optimized machining parameters, are achieved under the ultrasonic vibration-assisted pro le grinding process.

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Section: Ground Surface and Chip Morphologiesmentioning

confidence: 88%

MOPSO process parameter optimization in ultrasonic vibration-assisted grinding of hardened steel

Huang

Zhao

Qiu

et al. 2023

Int J Adv Manuf Technol

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“…where μ d and σ d are the average value and standard deviation of protrusion height, σ d =102 μm [30]. In the study, the surface, where the average protrusion height located, is the circumferential surface of grinding wheel.…”

Section: Simulation Of Grinding Wheel Surface Topographymentioning

confidence: 99%

Intermittent cutting behavior and grinding force model in ultrasonic vibration-assisted grinding K4002 nickel-based superalloy

Cao,

Zhao,

Ding

et al. 2024

Int J Adv Manuf Technol

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The intermittent cutting behavior, performing as periodic contact phase and separation phase between abrasive grit and workpiece, is an important characteristic in the ultrasonic vibration-assisted grinding (UVAG). This study investigated the effect of intermittent cutting behavior on the grinding force in UVAG. At first, a grinding force model was established by determining the quantity of effective grits and the grinding force of a single grit. Then the influences of ultrasonic vibration phase on the intermittent cutting behavior and grinding force were discussed. Finally, the prediction accuracy of the model was tested through experiments. Results indicated that the quantity of effective grits, unchanged chip thickness and proportion of grits at the sliding, plowing, cutting stages were all changed with the vibration phase. A large unchanged chip thickness and 56% effective grits at the cutting stage were generated at the start of the grits-wheel contact phase. Then, the quantity of effective grits firstly increased to maximum 232 at the vibration phase of 0.5π and then decreased to zero when entering the grits-wheel separation phase. The maximum error of the grinding force model was 15%, indicating that the model was of high accuracy for the force prediction in UVAG.

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“…Compared with traditional grinding, the ultrasonic-assisted grinding effectively enhances the surface quality and material removal rate, and reduces the grinding force and tool wear [4,5]. Commonly, the machining quality of ultrasonic-assisted grinding is determined by the degree of wear of the grinding tools, and the decrease of workpiece surface quality and even grinding burn are mainly caused by the wear of grinding tools [1,6]. Effective monitoring of the wear behavior of grinding tools is helpful to enhance the surface quality of workpieces and the machining efficiency.…”

Section: Introductionmentioning

confidence: 99%

EMD-based time–frequency denoising algorithm for the self-sensing of vibration signals in ultrasonic-assisted grinding

Zhang¹,

Sang²,

Zhao³

2022

Meas. Sci. Technol.

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An empirical mode decomposition (EMD) based time-frequency denoising algorithm is derived in this paper to accomplish the extraction of active ingredients for the multi-frequency mixed signals, and then the self-sensing of vibration signals is realized without additional sensors for the ultrasonic assisted grinding device. The EMD is employed to accomplish the signal decomposition, and multiple intrinsic mode function (IMF) components and the residual are obtained. Then, the weighted factors used for signal reconstruction are obtained based on innovation statistical distance, which is selected as a criterion to evaluate the time-frequency domain correlation between decomposition results and the original signal. Next, the maximum correntropy criterion (MCC) based similarity detection method is designed to adaptively modify the weighted factors, and the signal is reconstructed on the foundation of the modified weighted factors. Finally, numerical simulations and self-sensing experiment are conducted to verify the denoising performance of the proposed algorithm. The self-sensing of 4.59 dB vibration signal is realized for the ultrasonic assisted grinding device.

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On the tool wear behavior during ultrasonic vibration-assisted form grinding with alumina wheels

Cited by 34 publications

References 34 publications

MOPSO process parameter optimization in ultrasonic vibration-assisted grinding of hardened steel

MOPSO process parameter optimization in ultrasonic vibration-assisted grinding of hardened steel

Intermittent cutting behavior and grinding force model in ultrasonic vibration-assisted grinding K4002 nickel-based superalloy

EMD-based time–frequency denoising algorithm for the self-sensing of vibration signals in ultrasonic-assisted grinding

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