Investigation on Tool Wear and Surface Characteristics in Hard Turning Under Air-Water Jet Spray Impingement Cooling Environment

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

doi:10.24874/ti.2019.41.02.04

Cited by 12 publications

(4 citation statements)

References 26 publications

(48 reference statements)

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“…Blue colour chips denoted the higher heat generation compared to other machining tests where metallic chips produced. Similar illustration reported by [43].…”

Section: Chippingsupporting

confidence: 81%

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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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“…Blue colour chips denoted the higher heat generation compared to other machining tests where metallic chips produced. Similar illustration reported by [43].…”

Section: Chippingsupporting

confidence: 81%

“…Tool-tip-lost Micro-chipping VBc impact analysis through ANOVA (Table 3), as the impact of feed (43.11 %) on CRC is about 2.54 times more than cutting speed (15.92%). Singh et al [43], also found a decrement in CRC with leading feed. ANOVA also reported that all the input terms significantly influence the CRC.…”

Section: Chippingmentioning

confidence: 87%

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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“…This equation is essential for machining parameters optimization. For this reason, starting with Taylor, this subject permanently came to the attention of researchers from the field [1,[37][38][39][40][41][42][43][44][45]. One of the commonly used tool life equations is the extended Taylor's tool life equation, as follows:…”

Section: Literature Reviewmentioning

confidence: 99%

Machining Parameters Optimization Based on Objective Function Linearization

2022

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Manufacturing process optimization is an ever-actual goal. Within this goal, machining parameters optimization is a very important task. Machining parameters strongly influence the manufacturing costs, process productivity and piece quality. Literature presents a series of optimization methods. The results supplied by these methods are comparable and it is difficult to establish which method is the best. For machining parameters optimization, this paper proposes a novel, simple and efficient method, without additional costs related to new software packages. This approach is based on linear mathematical programming. The optimization mathematical models are, however, nonlinear. Therefore, mathematical model linearization is required. The major and difficult problem is the linearization of the objective function. This represents the key element of the proposed optimization method. In this respect, the paper proposes an original mathematical procedure for calculating the part of the objective function that refers to the analytical integration of the tool life into the model. This calculus procedure was transposed into an original software tool. For demonstrating the validity of the method, a comparison is presented among the results obtained by certain optimization techniques. It results that the proposed method is simple and as good as those presented by the literature.

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“…Soybean oil-based Al2O3 nanofluid was recommended for the best quality finish. Dong et al [24] found the superior quality of surface finish with 0.5 weight % of MoS2 nanofluid MQL in hard machining of tool steel. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Pandey

Kumar

Sahoo

et al. 2020

IJAME

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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.

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Investigation on Tool Wear and Surface Characteristics in Hard Turning Under Air-Water Jet Spray Impingement Cooling Environment

Cited by 12 publications

References 26 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

Machining Parameters Optimization Based on Objective Function Linearization

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

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