Investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ANN

Bagci, Eyup; Işık, Birhan

doi:10.1007/s00170-005-0175-x

Cited by 62 publications

(26 citation statements)

References 11 publications

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“…An end-milling test on carbon nanotube reinforced composites has been conducted in [6], in which the surface roughness under various cutting parameters has been investigated by using the analysis of variance. The multiple regression analysis model of the surface roughness was offered to reveal the significant effects of feed rate, spindle speed, and depth of cut on surface quality in milling [7] and turning [8] glass FRP composites. A mathematical model of surface roughness was further studied based on the response surface methodology [9,10].…”

Section: Introductionmentioning

confidence: 99%

Factor analysis of machining parameters of fiber-reinforced polymer composites based on finite element simulation with experimental investigation

Gao

Xiao

et al. 2015

Int J Adv Manuf Technol

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A three-dimensional (3D) micromechanical finite element (FE) model of machining of fiber-reinforced polymer (FRP) composites was developed in the paper. The FE modeling considers the three phases of a composite, in which the interphase between the fiber and matrix can realize interfacial debonding to represent the failure of composites and allow heat transfer. The machined surface observations and surface roughness measurements of carbon fiber-reinforced polymer (CFRP) composites at different fiber orientations were done firstly, and then, the model predictions of the machining responses, such as cutting force, temperature, and surface roughness, at different fiber orientations were compared with various experimental data for model validation. It is indicated that the three-phase micromechanical model is capable of precisely predicting machining responses and describing the failure modes of fiber shearing or bending related with fiber orientations in the chip formation process. To investigate the complex coupling influences of multiple machining parameters on the key responses of CFRP composites, the singlefactor analyses of each machining parameter were first carried out, and then, the multi-factorial analysis of multiple machining parameters was performed based on the orthogonal design of experiment and the analysis of variance (ANOVA) to quantitatively compare the influences of these key machining parameters on the cutting force and surface roughness. It was found that the fiber orientation angle, depth of cut, and cutting speed prove to be the important factors affecting the cutting force and surface roughness and that the coupling effects of these machining parameters all are relatively negligible in the machining of CFRP composites.

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Section: Introductionmentioning

confidence: 99%

Factor analysis of machining parameters of fiber-reinforced polymer composites based on finite element simulation with experimental investigation

Gao

Xiao

et al. 2015

Int J Adv Manuf Technol

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“…Normalizing the S/N ratio values for SR and TW is computed using equation (2). Grey relational coefficient for the normalized S/N ratio values was calculated using equation (3), and the grey relational grade can be computed by equation (4). Finally, the grades were considered for optimizing the multi-response parameter design problem.…”

Section: Resultsmentioning

confidence: 99%

Multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rod

Raveendran¹,

Marimuthu²

2015

Advances in Mechanical Engineering

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Glass fiber-reinforced plastics are extensively applied in engineering fields as a potential replacement to conventional steels, owing to its corrosive resistance property and high specific strength. But machining is complicated due to its anisotropic properties and non-homogeneous structure. In machining processes, minimum surface roughness and less tool wear are important factors influencing the quality of the surface, tool life, and productivity. Thus, the selection of tool and optimizing machining parameters are essential for perfect finishing. The tool used in this study is TiCN/TiN coated. The cutting parameters applied are cutting speed, feed, and depth of cut. As a dynamic approach, the multiple response optimization is carried out using grey relational analysis and desirability function analysis for simultaneous evaluation. These two methods are considered in optimization, as both are multiple criteria evaluation and not much complicated. Analysis of variance is employed to classify the significant parameters affecting the response.

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“…The authors concluded that ANN performance was clearly superior to that obtained by the polynomial model. Bagci and Işik (2006) developed an ANN and a response surface model to predict surface roughness on the turned part surface in turning unidirectional glass fiber reinforced composites. Both models were deemed as satisfactory.…”

Section: Rbf's Applied To Surface Roughness Predictionmentioning

confidence: 99%

“…Each cutting condition (V, f, d) was assigned to a network input. The choice is usual in literature (Bagci and Işik, 2006). Networks of single output were employed, being the output defined as the network prediction for surface roughness (Ra).…”

Section: Experimental Design For Selection Of Ann Parametersmentioning

confidence: 99%

A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning

Pontes¹,

Silva²,

Ferreira³

et al. 2010

J. Braz. Soc. Mech. Sci. & Eng.

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Investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ANN

Cited by 62 publications

References 11 publications

Factor analysis of machining parameters of fiber-reinforced polymer composites based on finite element simulation with experimental investigation

Factor analysis of machining parameters of fiber-reinforced polymer composites based on finite element simulation with experimental investigation

Multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rod

A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning

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