Optimization of Dry Machining Parameters for High-Purity Graphite in End-Milling Process by Artificial Neural Networks: A Case Study

Shie, Jie‐Ren

doi:10.1080/03602550600728257

Cited by 31 publications

(20 citation statements)

References 9 publications

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“…e statistical analysis also showed that the surface roughness decreased with a low feed rate [19]. Experimental design methods [20], arti cial neural networks [21], and the gray relational analysis method [22] were used to optimize the machining parameters for highpurity graphite in the end milling process. Additionally, Bajpai and Singh investigated the orthogonal micro-grooving of anisotropic pyrolytic carbon [23] and established a nite element model to understand the mechanics of material removal in the plane of transverse isotropy of pyrolytic carbon [24].…”

Section: Introductionmentioning

confidence: 99%

Rake Angle Effect on a Machined Surface in Orthogonal Cutting of Graphite/Polymer Composites

Yang

Wan

et al. 2018

Advances in Materials Science and Engineering

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Graphite and its composites have been widely used in various industrial fields. It has been generally accepted that, for positive rake angles, there is a significant increase in tension stress at the cutting zone during the machining of brittle materials, and cracks occur and spread easily, degrading the quality of the machined surface quality. However, it is found in this study that positive rake angles can improve the machined surface finish during the orthogonal cutting of graphite/polymer composites. Better machined surface finish is obtained for a larger rake angle. A finite element model is developed to reveal the mechanism of influence of the positive rake angle on the machined surface. Based on the effective stress field obtained from finite element analysis, it can be predicted that the crack initiates at the tool tip, subsequently propagates downward and forward, and later spreads gradually toward the free surface of the workpiece. A larger rake angle can promote crack propagation far from the machined surface. e crack initiation and propagation laws are validated by the edge-indentation experiments. In addition, the cutting force at various rake angles is investigated.

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Section: Introductionmentioning

confidence: 99%

Rake Angle Effect on a Machined Surface in Orthogonal Cutting of Graphite/Polymer Composites

Yang

Wan

et al. 2018

Advances in Materials Science and Engineering

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“…[16], the authors focused on surface roughness prediction with ANNs. Shie [17] focused on finding an optimal combination of cutting parameters using neural networks for the optimization of dry machining parameters for high-purity graphite in end milling processes. Suresh et al [18] developed an approach using RSM.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness prediction through internal kernel information and external accelerometers using artificial neural networks

et al. 2011

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In this paper, the average surface roughness parameter (Ra) is predicted using artificial neural network (ANN) models and internal kernel information and external piezoelectric accelerometer data. Experiments were conducted to obtain data to develop ANN models to predict surface roughness. A total of 72 samples were used to develop two networks, one based on accelerometer inputs and the other on kernel inputs. The Matlab ANN Toolbox was used for the modeling. The two networks had similar characteristics. Feed-forward backpropagation, 'newff', was the network structure selected, with a Levenberg-Marquardt backpropagation training function, 'trainlm', and a backpropagation weight and bias learning function, 'learngdm'. Samples obtained at the experimental stage were randomly divided into three groups to train (70% of the samples), validate (15% of the samples) and test (15% of the samples) the neural networks with a 'dividerand' data division function. The input processing functions used were 'fixunknowns', 'removeconstantrows' and 'mapminmax'. The transfer function was 'tansig' for hidden layers and 'purelin' for the output layer. The output processing functions used were 'removeconstantrows' and 'mapminmax'. The inputs consisted of the process parameters of radial depth of cut (Ae), the axial depth of cut (Ap), the spindle speed (N), the feed rate (f), the feed per tooth (fz), the cutting speed (Vc), the tooth passing frequency (ft), the cutting section (Cs), the material removal rate (MRR) and the cutting tool characteristics of the cutter radius (R), the number of teeth (Z) and the tool shape. The main difference between the two neural networks consisted of data origin: one considered data obtained with accelerometers and the other data collected in the NC kernel. Results showing high correlation factors between outputs and targets confirm that data provided by both internal and external sources can be useful for Ra prediction. However, NC kernel data provide several advantages.

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“…При этом материал можно наноструктурировать и осуществлять механосинтез различных ком позиций с высокой степенью их гомогени зации. Режимы механоактивации природных графитов подробно рассмотрены в работах [1][2][3][4][5][6].…”

Section: Introductionunclassified

Influence of the activation time on parameters of a graphite structure

Мамина

Gilmanshina

Anikina

et al. 2016

Russ. J. Non-ferrous Metals

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Optimization of Dry Machining Parameters for High-Purity Graphite in End-Milling Process by Artificial Neural Networks: A Case Study

Cited by 31 publications

References 9 publications

Rake Angle Effect on a Machined Surface in Orthogonal Cutting of Graphite/Polymer Composites

Rake Angle Effect on a Machined Surface in Orthogonal Cutting of Graphite/Polymer Composites

Surface roughness prediction through internal kernel information and external accelerometers using artificial neural networks

Influence of the activation time on parameters of a graphite structure

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