Cutting force prediction for ultra-precision diamond turning by considering the effect of tool edge radius

Huang, Peng; Lee, Wing Bun

doi:10.1016/j.ijmachtools.2016.06.005

Cited by 36 publications

(14 citation statements)

References 24 publications

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“…Yong et al [6] proposed a prediction model by applying the modified Oxley's approach to explore the effect of tool thermal property on cutting force then employed the predicted results to compare with experiments. Huang et al [7] presented a cutting force prediction algorithm which takes into account the effect of tool edge radius and verified the prediction algorithm by comparing the experiment results and the predicted value.…”

Section: Introductionmentioning

confidence: 99%

Effect of cutting edge radius on surface roughness and tool wear in hard turning of AISI 52100 steel

Zhao

Zhou

Bushlya

et al. 2017

Int J Adv Manuf Technol

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Performance of cutting tool in hard turning is significantly influenced by its microgeometry, such as edge radius. This study presents an experimental exploration to understand the effect of cutting edge radius on machining performance in terms of surface roughness and tool wear. The cutting tools (CBN) with three groups of nominal edge radius, 20, 30, and 40 μm, were used in the study. The cutting edge radii were characterized with an Alicona optical microscope, and variation of the edge radius was evaluated in this study. The machining tests were then conducted to experimentally assess the effect of cutting edge radius on surface quality and tool wear under different machining conditions. Three-level and two-factor experiments were designed in the test. The results in this study suggest that there is noticeable variation in the edge radius on a cutting tool with a certain nominal value of edge radius. The variations tend to be smaller with increase of the nominal value of edge radius. Besides, the results can be drawn that edge radii have a significant influence on surface roughness and tool wear. Considering all factors, the cutting tool with nominal edge radius of 30 μm demonstrate better machining performance among three groups of cutting tool in hard turning of AISI52100 steel.

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Section: Introductionmentioning

confidence: 99%

Effect of cutting edge radius on surface roughness and tool wear in hard turning of AISI 52100 steel

Zhao

Zhou

Bushlya

et al. 2017

Int J Adv Manuf Technol

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“…In precision and ultraprecision machining, the feed rate is often in the same magnitude as the tool edge radius. The effect of the tool edge radius cannot be neglected because extensive experimental evidence has suggested that the tool edge radius significantly affects chip formation, 1-3 cutting forces, [4][5][6] cutting temperatures, 7 tool wear, 8 and surface integrity. 9 For example, Arif, Rahman, and San 5 found that tool edge radius affects crack propagation in the cutting zone in the ductile-mode machining of brittle materials, such as tungsten carbides.…”

Section: The Important Effect Of Tool Edge Radius In Machiningmentioning

confidence: 99%

Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

Fang

Pai

2020

Journal of Testing and Evaluation

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Tool edge radius plays a significant role in affecting the surface integrity of machined products. The vast majority of existing research, however, takes no account of the effect of tool edge radius in the evaluation and modeling of machined surface roughness, an essential indicator of surface integrity. The present study fills this important research gap and has performed a total of 45 turning experiments on Unified Numbering System (UNS) A92024-T351 aluminum alloy with carefully selected cutting tools with three levels of tool edge radii. This article describes the experimental setup and measurements of tool edge radius and machined surface roughness. Machined surface roughness was evaluated using five parameters, including average roughness, root-mean-square roughness, peak roughness, maximum roughness height, and five-point average roughness. The experimental evidence presented in this article shows that the tool edge radius has a profound effect on machined surface roughness, cutting forces, and cutting vibrations. Based on the experimental data, three types of predictive models are developed, including a multiple regression model, multilayer perceptron neural network model, and radial basis function neural network model. The prediction accuracy of the three models is compared based on average mean squared errors. The results show that different models lead to different prediction accuracy for different surface roughness parameters.

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“…Therefore, the amplitude of cutter vibration is less at starting and increases as the tool wear increase [28]. In addition to that, elastic recovery during the machining results in friction induced vibrations [29].…”

Section: Amplitude Of Cutter Vibrationmentioning

confidence: 99%

Experimental and 3D-ANN based Analysis and Prediction of Cutting Forces, Tool Vibration and Tool Wear in Boring of Ti-6Al-4V Alloy

Murthy¹,

Rao²,

Rao³

2019

IJAME

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In this work, accurate 3D finite element models were developed to study and predict machining characteristics like tool vibration, tool wear, surface roughness, cutting force and thrust forces in the boring of Ti-6Al-4V alloy. Experiments were conducted on the proposed metal using carbide inserts at three levels of spindle speeds, depth of cuts and feed rates and experimental results were collected. Numerical simulation was carried out using Deform 3D software. Johnson-cook material model was also used in simulation to predict the machining characteristics. A Usui’s wear model was taken in simulation to calculate tool wear at different working conditions. Experimental data of the five machining characteristics were analysed using analysis of variance to identify the most significant parameters. Cutting speed, depth of cut and feed rate were found to be the most significant parameters. Simulated results of the machining characteristics were compared with the experimental data and found in a good agreement between them. An Artificial neural network (ANN) model was also developed and trained with the experimental data to validate the results. FEM simulation models provide relevant machining information without conducting experimentation for any metal.

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Cutting force prediction for ultra-precision diamond turning by considering the effect of tool edge radius

Cited by 36 publications

References 24 publications

Effect of cutting edge radius on surface roughness and tool wear in hard turning of AISI 52100 steel

Effect of cutting edge radius on surface roughness and tool wear in hard turning of AISI 52100 steel

Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

Experimental and 3D-ANN based Analysis and Prediction of Cutting Forces, Tool Vibration and Tool Wear in Boring of Ti-6Al-4V Alloy

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