Simulation Analysis of Cluster Effect of High-Shear Low-Pressure Grinding with Flexible Abrasive Tools

Tian, Chengjin; Han, Jinguo; Tian, Yebing; Liu, Bing; Gu, Zhiqiang; Hu, Xintao

doi:10.3390/mi12070827

Cited by 14 publications

(3 citation statements)

References 33 publications

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“…Additionally, Tian Y. et al [13] have identified that the specific energy is elevated, leading to reduced energy efficiency in grinding machining operations. A proposed energy prediction model for the grinding process incorporates various grinding-specific movements, such as cuttingout and cutting-in along the spindle, stable cutting, and infeed.…”

Section: Optimization Experimental Study Of Machining Energy Consumpt...mentioning

confidence: 99%

Optimization Experimental Study of Machining Energy Consumption of Zig-Zag Milling Cutting Path Using Rsm.

Ghazali,

Hemdi,

Osman

et al. 2023

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Machining operations in CNC milling which remove the work material require power and energy to activate the machine components such as spindle motor, table and tool movement in order to withstand the high friction and load between tool and work material. Energy consumption during cutting operation is greatly influenced by the machining condition and parameters. This experimental research aims to investigate how energy responds to changes in the machining parameters such as depth of cut, spindle speed, and feed rate during face milling operation of CNC machine. The high-speed steel (HSS) tool with a 10mm diameter was used to face mill the 40mm x 40mm of Aluminum 6061. The design of experiment technique using Response Surface Methodology (RSM) is utilized to optimize the experimental work. Power usage and machining time were recorded for each machining process, which is then used to determine the machining energy consumption. The interaction between machining parameter and energy is comprehensively visualized using surface and contour plot. Additionally, the ANOVA analysis investigates the feed rate as the most influential parameter to the machining energy. Finally, the regression equation of machining energy is generated with reliability (R) value of 0.88 which can be used as an energy prediction model.

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Section: Optimization Experimental Study Of Machining Energy Consumpt...mentioning

confidence: 99%

Optimization Experimental Study of Machining Energy Consumption of Zig-Zag Milling Cutting Path Using Rsm.

Ghazali,

Hemdi,

Osman

et al. 2023

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show abstract

“…STFs and abrasive particles were evenly immersed into Kevlar fabrics bonding to the grinding wheel substrate to develop a novel high-shear and lowpressure grinding method [19][20][21]. Numerical simulation on the novel grinding process was implemented to verify the highshear and low-pressure effect [22].…”

Section: Introductionmentioning

confidence: 99%

Investigation on rheological characteristics of magnetorheological shear thickening fluids mixed with micro CBN abrasive particles

Qian¹,

Tian²,

Fan³

et al. 2022

Smart Mater. Struct.

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The novel magnetorheological shear thickening polishing fluids (MRSTPFs) were developed by mixing micro CBN abrasive particles into traditional magnetorheological shear thickening fluids (MRSTFs). MRSTPFs were constructed by uniformly fumed silica and polyethylene glycol as shear thickening fluids (STFs), carbonyl iron particles (CIPs) as ferromagnetic phase and cubic boron nitride particles (CBNs) as abrasive phase. In this work, various MRSTPFs were prepared to explore their rheological characteristics. Sweeps of steady shear rate and dynamic shear frequency were conducted under different magnetic flux densities, respectively. A mathematical model was presented to explain shear rate variation with shear stress. The magnetorheological shear thickening mechanism was well described. The rheological experiment results have revealed that shear thickening effect was still existing in magnetic flux density. However, the increased magnetic flux density played a negative role on the shear thickening effect. Particle size optimizationof CIPs was thus essential to maximize the shear thickening effect. On the other hand, with increased shear frequency, the viscoelastic feature of MRSTPFs was converted from linear to non-linear. It was found that the shear yield stress of the MRSTPFs was magnified with the stronger magnetic flux density and larger CIPs size. The investigation of rheological characteristics demonstrated that MRSTPFs could enhance polishing performance, which contributed to developing a high-efficiency and ultra-precision polishing process.

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“…Correlation algorithms of simulation cluster analysis have been widely applied in different fields: a flexible machine with high shear and low pressure was developed [ 10 ]. Euler–Lagrange coupling method was adopted to simulate the new pressure-shear process.…”

Section: Introductionmentioning

confidence: 99%

Clustering Analysis Algorithm of Volleyball Simulation Based on Radial Fuzzy Neural Network

Yan

Wang

2022

Computational Intelligence and Neuroscience

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Aiming at a series of problems existing in volleyball, based on radial fuzzy neural network theory, the optimized simulation clustering analysis algorithm is used to monitor and analyze volleyball. By analyzing the feature weights of nodes at different stages, the optimal radial fuzzy neural network was constructed, which was combined with the simulation clustering algorithm to obtain the relevant optimization model describing volleyball. The accuracy of the optimization model is verified by comparing with the original model. The results show that with the increase of response, the response curves of different algorithms show fluctuation. Among them, the fluctuation range of MPDR (minimum power distortionless response) algorithm is larger, and the value of the curve obtained by MVDR (minimum variance distortionless response) algorithm differs greatly from that of the optimization algorithm at some key nodes, while the beam changing chart obtained by the optimization algorithm can better reflect the changing trend of the beam. Model indexes under different algorithms are different. When the number of iterative steps is less than 30, indexes under different algorithms are all greater than the standard value. When the number of iterations is more than 30, the indexes under different algorithms are all less than the standard value. Through verification, it can be seen that the original model can only describe the first stage of volleyball, while the optimization model can describe the whole process of volleyball. It shows that the optimization model can be used to describe and analyze volleyball-related data. And the algorithm can be used to better predict and analyze volleyball, and the analysis results can provide relevant guidance for volleyball. The optimization model provides basis and theoretical support for the application of volleyball simulation clustering algorithm, so as to better promote volleyball and better guide the movement.

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Simulation Analysis of Cluster Effect of High-Shear Low-Pressure Grinding with Flexible Abrasive Tools

Cited by 14 publications

References 33 publications

Optimization Experimental Study of Machining Energy Consumption of Zig-Zag Milling Cutting Path Using Rsm.

Optimization Experimental Study of Machining Energy Consumption of Zig-Zag Milling Cutting Path Using Rsm.

Investigation on rheological characteristics of magnetorheological shear thickening fluids mixed with micro CBN abrasive particles

Clustering Analysis Algorithm of Volleyball Simulation Based on Radial Fuzzy Neural Network

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