Statistical regression modeling and machinability study of hardened AISI 52100 steel using cemented carbide insert

Panda, Amlana; Sahoo, Ashok Kumar; Rout, Arun Kumar

doi:10.5267/j.ijiec.2016.7.004

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…The L16 [33] set of turning parameters, flank wear (VBc), chip reduction coefficient (CRC) and chip morphology results are located in the Table 1.…”

Section: Resultsmentioning

confidence: 99%

Grey-Fuzzy Hybrid Optimization and Cascade Neural Network Modelling in Hard Turning of AISI D2 Steel

Kumar

Pandey

Panda

et al. 2021

IJIE

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Nowadays hard turning is noticed to be the most dominating machining activity especially for difficult to cut metallic alloys. Attributes of dry hard turning are highly influenced by the amount of heat generation during cutting. Some major challenges are rapid tool wear, lower tool-life span, and poor surface finish but simultaneously generated heat is enough to provide thermal softening of hard work material and facilitates easier shear deformation thus easy cutting. Also, plenty of works reported the utilization of various cooling methods as well as coolants which successfully retard the intensity of cutting heat but this leads to additional cost as well as environmental and health issues. However, still, there is scope to select proper cutting tool materials, its geometry, and appropriate values of cutting parameters to get favorable machining outcomes under dry hard turning and avoid the cooling cost, environmental, and health issue. Considering these challenges, current work utilizes PVD-coated (TiAlN) carbide insert in dry hard turning of AISI D2 steel. The multi-responses like tool-flank wear, chip morphology, and chip reduction coefficient are considered. The amalgamation of input processing variables must be optimum for the effectual performance of hard to process materials turning. Generally, the Fuzzy logic hypothesis represents the uncertainties co-related with fuzziness, and deficiency in the data concerned with the problem. Further, to achieve the best combination of input cutting terms, grey-fuzzy hybrid optimization (Type I and Type II) is utilized considering the Gaussian membership function. Type II grey-fuzzy system attributed to 15 % less error (between GRG and GFG) compared to Type I. Hence, Type II grey-fuzzy system is utilized to get the optimal set of input terms. The optimal combination of input terms is found as t-1 (0.15 mm), s-4 (0.25 mm/rev) and is Vc-2 (100 m/min) which is comparable to the results obtained under spray impingement cooling using CVD tool in the literature. However, hard turning can be assessed under the dry condition with a PVD tool at the obtained optimal input condition for industrial uses. Further, six different types of cascade-forward-back propagation neural network modelling is accomplished. Among all models, CFBNN-4 model exhibited the best prediction results with a mean absolute error of 2.278% for flank wear (VBc) and 0.112% for the chip reduction coefficient (CRC). However, this model can be recommended for other engineering modelling problems. The outcomes of this research may be of immense importance to the tool manufacturers and machining industry.

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“…The L16 [33] set of turning parameters, flank wear (VBc), chip reduction coefficient (CRC) and chip morphology results are located in the Table 1.…”

Section: Resultsmentioning

confidence: 99%

Grey-Fuzzy Hybrid Optimization and Cascade Neural Network Modelling in Hard Turning of AISI D2 Steel

Kumar

Pandey

Panda

et al. 2021

IJIE

Self Cite

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Evaluation of the cutting performance of PVD, CVD and MTCVD carbide inserts in dry turning of AISI 4140 steel using RSM-based NAMDE optimization

Hamadi

Yallese²,

Boulanouar

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

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Statistical regression modeling and machinability study of hardened AISI 52100 steel using cemented carbide insert

Panda¹,

Sahoo²,

Rout³

2017

10.5267/j.ijiec

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The present study investigates performance and feasibility of application of low cost cemented carbide insert in dry machining of AISI 52100 steel hardened to (55 ± 1 HRC) which is rarely researched as far as machining of bearing steel is concerned. Machinability studies i.e. flank wear, surface roughness and morphology analysis of chip has been investigated and statistical regression modeling has been developed. The test has been conducted based on Taguchi L16 OA taking machining parameters like cutting speed, feed and depth of cut. It is observed that uncoated cemented carbide insert performs well at some selected runs (Run 1, 5 and 9) which show its feasibility for hard turning applications. The developed serrated saw tooth chip of burnt blue colour adversely affects the surface quality. Adequacy of the developed statistical regression model has been checked using ANOVA analysis (depending on F value, P value and R 2 value) and normal probability plot at 95% confidence level. The results of optimal parametric combinations may be adopted while turning hardened AISI 52100 steel under dry environment with uncoated cemented carbide insert.

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Statistical regression modeling and machinability study of hardened AISI 52100 steel using cemented carbide insert

Cited by 3 publications

References 21 publications

Grey-Fuzzy Hybrid Optimization and Cascade Neural Network Modelling in Hard Turning of AISI D2 Steel

Grey-Fuzzy Hybrid Optimization and Cascade Neural Network Modelling in Hard Turning of AISI D2 Steel

Evaluation of the cutting performance of PVD, CVD and MTCVD carbide inserts in dry turning of AISI 4140 steel using RSM-based NAMDE optimization

Statistical regression modeling and machinability study of hardened AISI 52100 steel using cemented carbide insert

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