Performance of Carbon Nanotubes Based Cutting Oil on Turning of AISI 1040 Steel

Amrita, M.; Chandra, Poorna; Ramana, P. Venkata; Sai, U. Shyam; Sreeram, Chatti

doi:10.1007/978-981-32-9417-2_14

Cited by 1 publication

(3 citation statements)

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“…Amongst different grades of steel, AISI 1040 steel has wide usage in the manufacturing sector and has hence received widespread attention of researchers [8][9][10][11][12]. AISI 1040 steel has usage in production of studs, springs, rods, different types of bolts, couplings and crankshafts [3].…”

Section: Introductionmentioning

confidence: 99%

“…A significant effort has been made to improve the quality characteristics of machined AISI 1040 steel using suitable machining parameters, use of lubricants, cryogenic cooling, etc [2,[13][14][15][16]. The parameters that were considered are speed, feed rate, depth of cut, tool material, work-piece hardness, minimum quantity lubricant (MQL), etc [7][8][9][10][11][12][13][14][15][16]. The responses or the quality parameters include surface roughness, fractal dimension, multiple roughness characteristics, tool wear and cutting forces [7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…The parameters that were considered are speed, feed rate, depth of cut, tool material, work-piece hardness, minimum quantity lubricant (MQL), etc [7][8][9][10][11][12][13][14][15][16]. The responses or the quality parameters include surface roughness, fractal dimension, multiple roughness characteristics, tool wear and cutting forces [7][8][9][10][11][12][13][14][15][16]. It was also revealed from the works that the relationship between the machining parameters and the environment with the different quality characteristics is quite complex and hence different methodologies were adapted successfully to model and optimize them.…”

Section: Introductionmentioning

confidence: 99%

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Measurement, modelling and optimization of the average temperature at the tool work interface for turning of AISI 1040 steel using ANN-GA methodology

et al. 2021

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Due to the mechanical properties and machinability of AISI 1040 steel, it has a broad range of industrial applicability. The present work investigates the dependence of the average temperature generated at the tool work interface on machining parameters such as cutting speed, feed rate and depth of cut. The machining parameters were varied at three equally spaced levels. Experiments were carried out following combinations in Taguchi's L 27 orthogonal array. A simple and cost-effective tool work thermocouple was devised and calibrated to obtain the tool work interface temperature. A 3-5-1 feed forward artificial neural network (ANN) model trained using the Levenberg-Marquardt (LM) back propagation algorithm resulted in efficient modelling of the complex relationship between the average cutting temperature and machining parameters (R-value of 0.99). The trained network was optimized using genetic algorithm (GA) to predict optimal turning parameters. This methodology has been termed as ANN-GA method. A significant reduction in the average cutting temperature could be realized due to optimization using ANN-GA method. Analysis of variance revealed highest contribution from the cutting speed and its square term in controlling the cutting temperature at tool work interface.

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