Comparative study on machinability improvement in hard turning using coated and uncoated carbide inserts: part II modeling, multi-response optimization, tool life, and economic aspects

Kumar, Ramanuj; Sahoo, Ashok Kumar; Mishra, Purna Chandra; Das, Rabin Kumar

doi:10.1007/s40436-018-0214-0

Cited by 49 publications

(31 citation statements)

References 34 publications

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“…The flank wear evolution ranges from 0.06 to 0.098 mm respectively. However it increases at slightly higher rate at higher cutting speed and feed of 190 m/min and 0.16 mm/rev respectively and reaches 0.155 mm (at run 4) but still within the limiting range of tool wear of 0.3 mm [20,29,30,31].The cutting inserts are free from any types of failures such as chipping, fracturing, and adhesion and induced regular flank wear with stable machining. Similar observations are noticed at depth of cut of 0.2mm (Run 5, 6, 7, 8), 0.3mm (Run 9, 10, 11 and 12) and 0.4mm (Run 13, 14, 15 and 16) respectively.…”

Section: Analysis Of Flank Wearmentioning

confidence: 94%

“…The dry hard turning tests were experimented by means of Taguchi L16 orthogonal array (OA) design which comprises of total 16 experimental runs of different combinations of process inputs such as cutting speed (v), feed (f) and depth of cut (d) respectively each at four levels [26,27,28,29]. The levels of depth of cut vary from 0.…”

Section: Cutting Parametersmentioning

confidence: 99%

“…The measured surface roughness varies from 0.2 microns to 1.33 microns respectively which is below the criterion limit of 1.6 microns. [20,29,30,31] This justifies its implementation in hard turning as surface roughness values are comparable to traditional grinding operation and may be replaced.…”

Section: Analysis Of Surface Roughnessmentioning

confidence: 99%

See 2 more Smart Citations

Investigating Machinability in Hard Turning of AISI 52100 Bearing Steel Through Performance Measurement: QR, ANN and GRA Study

Panda¹,

Sahoo²,

Panigrahi³

et al. 2018

INT. J. AUTOMOT. MECH. ENG.

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The existing endeavor investigates on machinability characteristics through performance measurement of flank wear, surface quality and chip morphology during finish turning of AISI 52100 bearing steel (55 ± 1 HRC) under dry environment employing carbide insert coated along with various layers (TiN/TiCN/Al2O3). Secondly the influence of machining variables viz. cutting speed, feed rate and depth of cut on responses are assessed by ANOVA and modeled through quadratic regression and artificial neural network. Multiparametric optimization of cutting conditions has been obtained through Taguchi based grey relational analysis. Finally, tool life at ideal conditions has been evaluated through experiment. Based on the study, it is disclosed that coated carbide with multilayer insert outperformed during hard machining as wear at the flank surface and surface quality are within the benchmark cap of 0.3 mm and 1.6 microns respectively. From the chip morphology analysis, multilayer coated carbide insert generates lower temperature and maintains cutting edge sharpness and delays the growth of tool wear. ANN model using multilayered feed forward network yields accurate prediction of responses with minimum error percentage compared to QR model. The optimal parametric combination through GRA approach is found to be d1 (0.1 mm)-f1 (0.04 mm/rev)-v2 (110 m/min) and is greatly improved. Feed is the compelling aspect for multi-responses pursued by cutting speed. The tool life at optimized cutting condition is found to be approximately 19 minutes.

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Section: Analysis Of Flank Wearmentioning

confidence: 94%

Section: Cutting Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Investigating Machinability in Hard Turning of AISI 52100 Bearing Steel Through Performance Measurement: QR, ANN and GRA Study

Panda¹,

Sahoo²,

Panigrahi³

et al. 2018

INT. J. AUTOMOT. MECH. ENG.

View full text Add to dashboard Cite

show abstract

“…The responses to be measured are flank wear (VBc), average surface roughness (Ra) and chip-reduction coefficient (CRC). The standard limit of VBc and Ra have been fixed as 0.3 mm and 1.6 microns respectively [20,[24][25][26]. Width of flank wear is measured through optical micrographs images which are captured offline via STM6 microscope whereas Taylor Hobson's roughness tester is utilised for Ra measurement and followed the ISO 3274-1996 standard.…”

Section: Methodsmentioning

confidence: 99%

“…Kumar et al [27] found the catastrophic failure of TiN coated tool at 182 m/min of machining of D2 steel under dry condition. Dominancy of cutting speed on wear is clearly identified through test results [25][26][27].…”

Section: Wear Morphologymentioning

confidence: 99%

Hard Turning on JIS S45C Structural Steel: An Experimental, Modelling and Optimisation Approach

Kumar¹,

Modi²,

Panda³

et al. 2019

IJAME

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The present research is performed while turning of JIS S45C hardened structural steel with the multilayered (TiN-TiCN-Al2O3-TiN) CVD coated carbide insert by experimental, modelling and optimisation approach. Herein, cutting speed, feed rate, and depth of cut are regarded as input process factors whereas flank wear, surface roughness, chip morphology are considered to be measured responses. Abrasion and built up-edge are the more dominant mode of tool-wear at low and moderate cutting speed while the catastrophic failure of tool-tip is identified at higher cutting speed condition. Moreover, three different Modelling approaches namely regression, BNN, and RNN are implemented to predict the response variables. A Back-propagation neural network with a 3-8-1 network architecture model is more appropriate to predict the measured output responses compared to Elman recurrent neural network and regression model. The minimum mean absolute error for VBc, Ra and CRC is observed to be as 1.36% (BNN with 3- 8-1 structure), 1.11% (BNN with 3-8-1 structure) and 0.251 % (RNN with 3-8-1 structure). A multi-performance Optimisation approach is performed by employing the weighted principal component analysis. The optimal parametric combination is found as the depth of cut at level 2 (0.3 mm)-feed at level 1 (0.05 mm/rev) – cutting speed at level 2 (120 m/min) considered as favourable outcomes. The predicted results were validated through a confirmatory trial providing the process efficiency. The significant improvement for S/N ratio of CQL is observed to be 9.3586 indicating that the process is well suited to predict the machining performances. In conclusion, this analysis opens an avenue in the machining of medium carbon low alloy steel to enhance the machining performance of multi-layered coated carbide tool more effectively and efficiently.

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Investigation on Surface Roughness and Power Consumption for Sustainability Assessment in Hard Turning of HSLA Steel With SPPP‐AlTiSiN–Coated Carbide Tool Under Various Cooling‐Lubrications

Roy,

Pradhan,

Padhan

et al. 2024

Lubrication Science

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The present research analyses the power consumption (Pc) and surface roughness (Ra) in hard turning of high‐strength low‐alloy (HSLA) grade AISI 4140 steel using a recently developed AlTiSiN‐coated carbide tool under different cooling‐lubrication conditions (dry, flooded, nanofluid‐MQL). The nanofluid was prepared by mixing the MWCNT nanoparticles with an eco‐friendly automotive radiator coolant (base fluid). The cooling‐lubrication performance is investigated briefly by comparing the machining responses like machined surface morphology, tool wear, cutting force and temperature. The experiments associated with 46 trials were performed by considering various machining variables, namely cutting speed, nose radius, depth of cut, feed and cooling‐lubrication methods. From the perspective of predictive modelling and multi‐response optimisation, response surface methodology has been employed to minimise power consumption and surface roughness. Thereafter, the predictive modelling and optimisation results are implemented for economic analysis and energy‐saving carbon footprint evaluation. This innovative research also addresses comparative environmental sustainability evaluation in hard turning under different cooling‐lubrication conditions using a life cycle assessment methodology for cleaner and safer production. Results indicate that cutting speed was the most influential item in power consumption enhancement. Furthermore, compared with dry and flooded turning, lower cutting force, reduced cutting temperature, shorter width of flank wear and better surface morphology were obtained under nanofluid‐MQL machining. It has been observed that nanofluid‐MQL machining outperformed sustainability improvement concerning techno‐economically viable societal acceptable and environmental friendliness.

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Comparative study on machinability improvement in hard turning using coated and uncoated carbide inserts: part II modeling, multi-response optimization, tool life, and economic aspects

Cited by 49 publications

References 34 publications

Investigating Machinability in Hard Turning of AISI 52100 Bearing Steel Through Performance Measurement: QR, ANN and GRA Study

Investigating Machinability in Hard Turning of AISI 52100 Bearing Steel Through Performance Measurement: QR, ANN and GRA Study

Hard Turning on JIS S45C Structural Steel: An Experimental, Modelling and Optimisation Approach

Investigation on Surface Roughness and Power Consumption for Sustainability Assessment in Hard Turning of HSLA Steel With SPPP‐AlTiSiN–Coated Carbide Tool Under Various Cooling‐Lubrications

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