Estimation of surface roughness using cutting parameters, force, sound, and vibration in turning of Inconel 718

Deshpande, Yogesh V.; Andhare, Atul B.; Sahu, Neelesh Kumar

doi:10.1007/s40430-017-0819-4

Cited by 60 publications

(28 citation statements)

References 30 publications

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“…As reported by Deshpande et al [2], attaining good finish is very tough for Inconel 718. Hence, authors have reported very extensive statistical study for estimating regression-based R a models.…”

Section: Introductionmentioning

confidence: 52%

“…ANN is an effective tool used for estimation of response factors by considering its easiness, speed and learning ability [18]. Therefore, to confirm the results obtained by Deshpande et al [2], it was decided to use ANN to the experimental data.…”

Section: Introductionmentioning

confidence: 92%

“…The existence of R a is very complicated and method oriented. Machining can be modelled and confirmed for realtime control of the process to acquire the desired R a [2], where modelling is the process of fitting input and output data [3]. Therefore, numerical models offering relationship amongst R a and cutting parameters such as cutting speed, feed and depth of cut are developed [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…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. This paper deals with use of artificial neural network (ANN) for predicting R a , and therefore, some works reporting use of ANN in machining of different alloys are discussed below.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, authors have reported very extensive statistical study for estimating regression-based R a models. Due to enormous statistical calculation of used method [2], it was thought to apply some pattern classifier technique such as ANN. ANN is a soft computing method commonly used in several applications such as forecasting, data control and recognition of pattern.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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.

Self Cite

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Modeling and Optimization Algorithms in Rapid Prototyping, Submerged Arc Welding, and Turning

Gupta

Mia

Gupta³

et al. 2021

Modeling and Optimization in Manufacturing

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Effect of Eco‐Friendly Minimum Quantity Lubrication in Hard Machining of Vanadis 10: A High Strength Steel

Özbek

Kara

et al. 2022

steel research int.

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Sustainable manufacturing involves a reduced negative environmental impact, lower consumption of resources and energy, minimum waste generation, and greater concern for operator health and safety, and at the same time, an assurance of enhanced product quality. [1] With the industrial revolution, more and more types of metal are being produced and processed to meet increasing demands. In the early 20th century, the use of coolants became widespread, as dry machining conditions performed poorly in terms of dimensional accuracy, surface roughness, and tool life. [2][3][4] Cutting fluids are known to increase the quality of the final product, but on the other hand, they have many negative properties, from their threat to human health, to their lack of economy, and many studies have been carried out in these areas. [5][6][7] In addition, starting from the last quarter of the 20th century, after the coolant service life ends, the cost of its disposal can be twice the cost of the coolant. [8,9] Dry cutting is preferred due to the fact that there is no cutting fluid to be disposed of after processing. In addition, because of the absence of cutting fluid, it is harmless to employee health. However, due to the high temperatures in the cutting area, tool wear increases, which negatively affects important outputs such as surface roughness and tool life. Minimum Quantity Lubrication (MQL) is the process of pulverizing a small amount of oil (<30 mL h À1 ) in a compressed air flow. Another contribution of the MQL system is the contribution of its lubrication feature to surface roughness.Özbek and Saruhan, [10] using carbide tools in dry turning and MQL cutting environments, evaluated AISI D2 tool steel (50 HRC) in terms of surface roughness, cutting tool vibration, cutting temperature, tool wear, and tool life. When the machining with MQL was compared to dry machining, experimental results showed 25%, 23%, and 45% lower values for cutting temperature, tool wear, and cutting tool vibration, respectively. The authors claimed that these improvements led to an 89% improvement in workpiece surface roughness and prolonged tool life by up to 267%. Chinchanikar and Choudhury [11] investigated the effects of cutting environment on wear behavior when turning AISI 4340 (55 HRC) steel using tungsten carbide tools with three different coating materials (AlTiN, TiAlN/TiSiN, and AlTiCrN) under dry and MQL machining conditions. It was observed that in all three tools, lower nose wear occurred at all cutting parameters when machining with MQL. Although tool life was decreased in all tools, with increasing cutting speed, cutting with MQL provided longer tool life compared to the dry cutting environment. Emami and Karimipour [12] studied the effect of MQL technique on chattering during turning. The tool wear test shows that the occurrence of chatter in machining has a more destructive effect on tool failure than the type of lubrication, or friction condition itself. During chattering, the cutting tool is heavily subjected to microcracks, microchi...

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Estimation of surface roughness using cutting parameters, force, sound, and vibration in turning of Inconel 718

Cited by 60 publications

References 30 publications

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

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

Modeling and Optimization Algorithms in Rapid Prototyping, Submerged Arc Welding, and Turning

Effect of Eco‐Friendly Minimum Quantity Lubrication in Hard Machining of Vanadis 10: A High Strength Steel

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