Prediction of Surface Roughness in Turning of EN 353 Using Response Surface Methodology

Bhardwaj, Bhuvnesh; Kumar, Rajesh; Singh, Premlata

doi:10.1007/s12666-013-0346-7

Cited by 24 publications

(10 citation statements)

References 15 publications

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“…The capability of the model was analysed with the nearness of the R 2 value. From Table 5, the R 2 = 0.98 close to 1 which is required [34] and adjusted R 2 = 0.95 was attained. Adequate precision used to calculate the signal-to-noise ratio.…”

Section: Anova For Tool Wearmentioning

confidence: 75%

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Experimental investigation of copper nanofluid based minimum quantity lubrication in turning of H 11 steel

Ganesan

Babu

Santhanakumar

et al. 2018

J Braz. Soc. Mech. Sci. Eng.

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Plentiful supply of coolant might enhance the machining cost and also generates environmental hazard. As a result, substitute methods are required to eliminate the problems encountered during use of coolant. In this investigation, the impact of cutting speed, feed and lubrication conditions (dry: no lubrication, oil: machining is performed with groundnut oil and nano fluid: machining performed with copper nano fluid) on surface roughness, tool wear and chip morphology in turning of H 11 steel with minimum quantity lubrication (MQL) were examined. Experiments were conducted using L 18 orthogonal array. The results reveal that copper nanofluids with MQL provide a substitute for dry and oil machining. Response surface methodology has been used to derive optimal values and mathematical models. Tool wear was reduced by 66% and surface roughness by 40% while machining with copper nano fluids. The surface roughness and tool wear were decreased under optimal machining conditions. Generation of large notched tooth in chips has been minimised with copper nano fluids. Furthermore, the morphology of the chips were analysed for dry, oil and nano fluid under scanning electron microscope to observe the texture created.

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Section: Anova For Tool Wearmentioning

confidence: 75%

“…Minimum quantity lubrication method which indicates each value is separated consistently by a line inclined at 45°[34].…”

mentioning

confidence: 99%

Experimental investigation of copper nanofluid based minimum quantity lubrication in turning of H 11 steel

Ganesan

Babu

Santhanakumar

et al. 2018

J Braz. Soc. Mech. Sci. Eng.

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“…though it is costly and difficult to machine [7][8][9][10][11][12]. Researchers have used regression analysis and response surface methodology (RSM) [13][14][15][16][17][18][19][20][21][22] for prediction of R a . Deshpande et al [2] have used regression-based modelling for estimating R a in turning of Inconel 718.…”

Section: Introductionmentioning

confidence: 99%

Application of ANN to estimate surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718

Deshpande¹,

Andhare

Padole

2018

SN Appl. Sci.

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In this paper, artificial neural network approach is used to predict surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718. Experiments were performed by using cryogenically treated and untreated inserts, and various responses were measured. Then, these measured responses were used as input to the artificial neural network to predict surface roughness. It is found that the models developed by artificial neural network are predicting surface roughness with more than 98% accuracy. Further, the predictions obtained by artificial neural network are compared with the results of regression-based prediction models earlier proposed by the authors. The modified regression models were estimating surface roughness with more than 90% accuracy. Based on correlation coefficient values, the prediction results of modified regression model are compared with those obtained by artificial neural network. Finally, it is concluded that artificial neural network models are better for estimating surface roughness than the regression models and such predictions are useful for real-time control of the process to acquire the desired surface roughness. Keywords Artificial neural network • Surface roughness • Inconel 718 Abbreviations CNC Computer numerical control RSM Response surface methodology FOE M Modified first-order equation ANN Artificial neural network BP Backpropagation BR Bayesian regularization LM Levenberg-Marquardt AE Absolute error (%) MAE Mean absolute error (%) MSE Mean square error (%) List of symbols v Cutting speed (m/min) f Feed rate (mm/rev) d Depth of cut (mm) F c Cutting force (N) S Sound pressure level (Pa) V v Vibration velocity of workpiece (m/s) n Number of experiments R ai Average of measured surface roughness in μm R ai Estimated surface roughness h Number of neurons in single hidden layer R 2 Correlation coefficient

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“…No entanto, a Equação 1 só é verdadeira em condições ideias. Em função disso, vários são os estudos desenvolvidos (BHARDWAJ et al, 2014;ASILTÜRK et al, 2016;GRZESIK, 2018) que visam avaliar a influência desses, e dos demais parâmetros de usinagem envolvidos no processo de torneamento. Grzesik (2018) procurou prever a rugosidade superficial (por meio do parâmetro Rz) produzida por operações de torneamento de precisão utilizando ferramentas de corte de CBN e taxas de avanços variáveis de 0,025 a 0,075 mm/rev.…”

Section: Introductionunclassified

“…No trabalho de Bhardwaj et al (2014) uma tentativa foi feita para investigar a influência da velocidade de corte, avanço, profundidade de corte e raio da ferramenta na rugosidade da superfície durante o torneamento úmido do aço 353 usando pastilhas de metal duro. O estudo revelou que a velocidade foi o principal fator de influência na rugosidade da superfície, já a profundidade de corte não teve relevância sobre essa.…”

Section: Introductionunclassified

Estudo Da Interferência Do Raio De Ponta Da Ferramenta Sobre a Rugosidade Superficial Em Um Processo De Torneamento

Medeiros¹,

Lima²,

Diniz³

et al. 2018

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RESUMOO torneamento de acabamento é caracterizado por ser uma operação de finalização da peça, em função do raio de ponta da ferramenta ser considerado um dos fatores de maior influência sobre a rugosidade superficial de peças que passaram pelo processo de torneamento de acabamento, aliado ao fato de pesquisas publicadas relatarem discrepâncias entre os cálculos teóricos e os métodos experimentais. O presente estudo teve como objetivo avaliar a interferência do raio de ponta da ferramenta em termos do parâmetro Rz medido, em relação ao esperado teoricamente, e analisar através de imagens tridimensionais e por vistas laterais, as marcas superficiais deixadas pela ferramenta de corte em cada uma das peças torneadas. A pesquisa permitiu identificar que ao aumentar o raio de ponta da ferramenta de 0,4 mm para 0,8 mm ocorreu uma diminuição da rugosidade superficial, em termos do parâmetro Rz, quando o material torneado foi o alumínio, concordando com a equação teórica. No entanto, quando o material torneado foi o aço, o inverso ocorreu. Esse fato foi justificado em função da menor usinabilidade do aço e pelos outros parâmetros envolvidos no processo os quais não foram levados em consideração, tais como velocidade de corte, desgaste da ferramenta, atrito e forças de corte. Além disso, foram obtidas imagens por Microscopia óptica e por perfilometria (vistas laterais) que permitiram identificar as marcas de avanço e do raio de ponta da ferramenta, assim como irregularidades provenientes de fluxo ABSTRACT Finishing turning is characterized by being a finishing operation of the workpiece, depending on the tool radius being considered one of the factors that have the

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Prediction of Surface Roughness in Turning of EN 353 Using Response Surface Methodology

Cited by 24 publications

References 15 publications

Experimental investigation of copper nanofluid based minimum quantity lubrication in turning of H 11 steel

Experimental investigation of copper nanofluid based minimum quantity lubrication in turning of H 11 steel

Application of ANN to estimate surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718

Estudo Da Interferência Do Raio De Ponta Da Ferramenta Sobre a Rugosidade Superficial Em Um Processo De Torneamento

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