Measurement and machinability study under environmentally conscious spray impingement cooling assisted machining

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

doi:10.1016/j.measurement.2018.12.037

Cited by 54 publications

(19 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…In the entire set of experiments, the colours of chips are metallic which indicates the optimum amount of heat generation during the process. In each run, sawtooth profile (as shown in Figure 6) on the chip is noticed due shear cyclic crack formation on to the thin sections of the chips [10,26]. According to Ueda et al [30], sawtooth is formed due to high stain concentration by local weakening through high heat penetration into the workpiece.…”

Section: Chip Morphology and Chip Reduction Coefficientmentioning

confidence: 96%

“…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%

See 2 more Smart Citations

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

Kumar¹,

Modi²,

Panda³

et al. 2019

IJAME

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Chip Morphology and Chip Reduction Coefficientmentioning

confidence: 96%

See 1 more Smart Citation

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

Kumar¹,

Modi²,

Panda³

et al. 2019

IJAME

View full text Add to dashboard Cite

show abstract

“…Temperature induced during machining process is a significant parameter to be studied as it plays an important role in determining the life of the tool [45]. The initial trial selected is test number 14 with low net outrank flow (Table 6) as A2Vc2f2d3 indicating A2 = dry cutting, Vc2 = m/min, f2 = 0.08 mm/min and d3 = 1.5 mm [52]. Hence, in dry condition a rise in temperature in noticed due to rubbing action developed at the primary and secondary deformation zone [46].…”

Section: Cutting Temperaturementioning

confidence: 99%

Evaluation of graphene based nano fluids with minimum quantity lubrication in turning of AISI D3 steel

Babu¹,

Anandan

Muthukrishnan

et al. 2019

SN Appl. Sci.

View full text Add to dashboard Cite

Minimum quantity lubrication is an extensively accepted lubrication system owing to its improved heat removal in the machining areas. The purpose of the current study is to examine the effectiveness of graphene based nanofluids in turning on D3 tool steel with minimum quantity lubrication. Experiments were executed with Taguchi's L27 orthogonal matrix array. The effectiveness were examined with the machining parameters under environment (dry condition, flood lubrication and minimum quantity lubrication), cutting speed, feed rate and depth of cut in terms of surface roughness, cutting temperature, tool wear and chip morphology. A technique known as Preference Ranking Organization Method for Enrichment Evaluations were used to determine the imperative parameters governing the responses. The results reveal that graphene nanofluids with minimum quantity lubrication found to be a right substitute in reducing the surface roughness, tool wear and cutting temperature.

show abstract

“…Das et al [25] utilised rice bran oil-based three different nanofluid samples like Al2O3, Cuo and Fe2O3 in MQL hard turning and performance of CuO nanofluid was best among others nanofluids succeeded by Fe2O3 and Al2O3 nanofluids. Nune and Chaganti [26] developed a new metalworking fluid and found the highest ratio of the volume of metal removed to flank wear compared to oil-based metalworking fluid at same cutting situations.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

Pandey

Kumar

Sahoo

et al. 2020

IJAME

View full text Add to dashboard Cite

The current research presents an overall performance-based analysis of Trihexyltetradecylphosphonium Chloride [[CH3(CH2)5]P(Cl)(CH2)13CH3] ionic fluid mixed with organic coconut oil (OCO) during turning of hardened D2 steel. The application of cutting fluid on the cutting interface was performed through Minimum Quantity Lubrication (MQL) approach keeping an eye on the detrimental consequences of conventional flood cooling. PVD coated (TiN/TiCN/TiN) cermet tool was employed in the current experimental work. Taguchi’s L9 orthogonal array and TOPSIS are executed to analysis the influences, significance and optimum parameter settings for predefined process parameters. The prime objective of the current work is to analyze the influence of OCO based Trihexyltetradecylphosphonium Chloride ionic fluid on flank wear, surface roughness, material removal rate, and chip morphology. Better quality of finish (Ra = 0.2 to 1.82 µm) was found with 1% weight fraction but it is not sufficient to control the wear growth. Abrasion, chipping, groove wear, and catastrophic tool tip breakage are recognized as foremost tool failure mechanisms. The significance of responses have been studied with the help of probability plots, main effect plots, contour plots, and surface plots and the correlation between the input and output parameters have been analyzed using regression model. Feed rate and depth of cut are equally influenced (48.98%) the surface finish while cutting speed attributed the strongest influence (90.1%). The material removal rate is strongly prejudiced by cutting speed (69.39 %) followed by feed rate (28.94%) whereas chip reduction coefficient is strongly influenced through the depth of cut (63.4%) succeeded by feed (28.8%). TOPSIS significantly optimized the responses with 67.1 % gain in closeness coefficient.

show abstract

Measurement and machinability study under environmentally conscious spray impingement cooling assisted machining

Cited by 54 publications

References 37 publications

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

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

Evaluation of graphene based nano fluids with minimum quantity lubrication in turning of AISI D3 steel

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

Contact Info

Product

Resources

About