Experimental investigation of tool wear in face milling of EN-31 steel under different machining environments

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Abstract

The objective of the current study is to investigate the performance of tungsten carbide tool inserts in face milling of EN-31 steel under various machining environments viz. dry machining, air cooling, minimum quantity lubrication(MQL) and flood cooling. Tool performance is generally determined by the progression of the tool flank wear during machining. Using Taguchi’s approach of design of experiments, machining environments is combined with machining parameters such as cutting speed, feed rate and depth of cut, and experiments is carried out as per mixed L18 orthogonal array. Taguchi based analysis of mean (ANOM) and analysis of variance (ANOVA) is utilized to check the effects of input parameters on tool flank wear. The optimum machining parametric setting for minimum tool flank wear is observed as MQL with lubricant flow rate (LFR) of 150 ml/hr, 110 m/min cutting speed, 60 mm/min feed rate and 0.4 mm depth of cut. From the results of the ANOM, tool flank wear in the case of MQL with LFR of 150 ml/hr is observed to be 5.29% lower as compared to flood cooling. The mathematical model revealed that the second-order regression model accurately determined the variability of tool flank wear with the input parameters with least error.

Introduction

The unprecedented worldwide tendency of competition among the production industries forcing the manufacturer to produce the components with minimum possible expenditure and without compromising the product quality. Compelled by this veracity, enhancing the machinability of metals through optimization is the definitive goal of several research projects allied with the manufacturing sector. The cutting temperature is a decisive factor for the machinability indices such as the cutting force, surface finish and tool wear [1]. During the cutting of metal, a significant amount of the mechanical energy is converted into heat due to the plastic deformation of the workpiece surface, friction between the tool-chip and tool-work interface [2], [3]. Due to higher temperature at the cutting edge, softening of the tool occurs and due to large thermal stresses failure of the tool occurs. Lubricant is supplied to the cutting area to take away the heat produced during machining. Considering the high cost of lubricant dry machining or air cooling is employed in many cutting operations [4]. The costs associated with coolant/lubricant may ranging from 7 to 17% which is more than the tool costs [5]. Also, the expenses related to the maintaining and dumping of cutting fluids is very high. But in the case of difficult to cut materials such as EN-31 steel, the use of lubricant became mandatory as the rate of heat generation is very high during machining [3], [7]. This forced the engineers and researchers to find an environment-friendly lubrication/cooling system in machining in the manufacturing industry. Also. it became essential to optimize the different input parameters of the machining process along with optimum usage of cutting fluid, for obtaining the best performances in form of longer tool life, good surface integrities, high Material removal rates [8], [9]. Machining environments are generally defined through the cooling/lubrication system employed during machining. Also, alteration in the process variable of the cooling/lubrication method varies the effectiveness of that system [3], [7], [8]. Sharma et al. [8] compared the performance of various cooling techniques such as dry, MQL and flood cooling for simultaneous optimization of the tool wear and surface roughness. The performance of MQL is found better than the dry cutting and comparable to flood cooling. Kang et al. [9] performed high-speed milling of die steel in various machining environments and the performance of coated carbides tools is studied. In flood cooling, the development of thermal cracks on the tool flank face caused the cracking and chipping off the cutting edge. MQL supply eliminated the effects of thermal damage on the tool which results in better tool performance than flood cooling [9]. Sales et al. [10] evaluated the tool wear experimentally using vegetable oil-based cutting fluid during milling of AISI 4140 steel with TiAlN coated cemented carbide inserts. The flow rate of coolant is chosen between 0 (dry cutting) to 200 ml/h and increases in the LFR results in the reduction of tool wear. Hassanpour et al. [11] did experiments on AISI 4340 alloy steel in hard milling using the MQL technique. Results show that the cutting speed and the LFR have a major contribution to the reduction of surface roughness. Cai. et al. [12] studied the influence of the LFR for milling of titanium alloy using MQL. Results revealed that increment in LFR leads to efficient penetration of lubricant in the machining zone and thus resulted in lower surface roughness. The mechanism tool wear has been changed from diffusion wear to steady wear at the higher LFR. Yan et al. [13], reported a reduction in tool wear and surface roughness with the increase in oil flow rate during milling of forged steel. The lubricant reached the cutting zone effectively at high air pressure. Liu et al. [14] did an experimental investigation during end milling of titanium alloy using TiAlN coated Kennametal inserts by varying the air pressure, lubricant quantity and using different nozzle positions. Results revealed that up to a certain value (10 ml/h), an increase in the oil quantity reduces the cutting force and cutting temperature. The effectiveness of the spraying angle is only found when the viscosity of the lubricant is low. Zhang et al. [15] carried out experiments to study the impact of the different cooling/lubricating techniques on tool wear of coated carbide tool in end milling of Inconel 718. The analysis has shown that Minimum Quantity Cooling Lubrication (MCQL) leads to higher tool life (1.57 times as compared to dry cutting) and lower cutting forces. Sun et al. [16] also studied the effect of dry machining, flood cooling and MQL on tool life and cutting force for milling of Titanium alloys by carbide tool. It is observed that the tool life achieved using MQL is longer than flood cooling and dry machining as MQL leads to better lubrication and cooling effect. Singh et al. [17] found lesser flank wear using MQL conditions than wet and dry machining when milling of Stainless Steel 304 using Coated Carbide Tool Insert is performed. Better lubrication and penetration is observed in MQL conditions. Babu et al. [18] investigated the tool wear and surface roughness in end Milling of AISI 304 and a reduction of 70% in tool wear and 66% in surface roughness is recorded using MQL in comparison to flood Lubrication.

A little research is reported about the comparative analysis of tool performance for the face milling of EN-31 steel under different cooling/lubrication techniques. Generally, Flank wear is most commonly used to determine the life span of the and considered as the most important phenomenon to describe the tool performance. Also, its progression affects the quality of the machined surface. Therefore, current work is focused on the study of tool flank wear under various cooling/lubrication systems namely dry machining, air cooling, MQL and flood cooling. Face milling experiments is performed as per Taguchi based L18 orthogonal array and comparative analysis of cooling/lubrication techniques in terms of tool flank wear is done. Taguchi based experimental design and optimization is effectively utilised for discovering optimal parametric settings for output responses characteristics of different conventional and non-conventional machining operations [6], [3], [7], [19], [20] and the same has been employed in current work to optimize the tool flank wear. Regression analysis is used to formulate the mathematical models between tool flank wear and input parameters.

Section snippets

Workpiece material and machining setup

The EN-31 steel is chosen as workpiece material for the face milling experimentation and its chemical composition is 0.978 % C, 0.353% Mn, 0.215% Si, 1.01% Cr, 0.089% S and 0.086% P. It is widely used for manufacturing die slabs, ejector pins, bearings and numerous automobile components [6], [3]. Blocks of size 75 mm × 75 mm × 16 mm is prepared from an EN-31 metal strip using cutting and milling operation. Then grinding operation is performed to eliminate the top layer having oxides, carbides

Results and discussion

The experimentation is completed as per mixed L18 orthogonal array and all experiments is conducted three times to minimize the error of machining. The machining length is selected as 450 mm and is kept constant for each experiment. For measuring the tool flank wear, the cutting tool inserts of all experiments is collected. Then the high-resolution photographs of the cutting edge of the tool (flank) is captured with a camera having 50x optical zoom. These images are analyzed in Image meter

Regression analysis

For determining the fitness of experimental value, Multi-variable linear regression (MVLR) analysis is exploited to form mathematical models. The first-order and second-order empirical equations showing the relation between mean tool flank wear (VB) and input parameters is generated using Minitab software as given in eq. (1) and eq. 2 respectively. The value is R-square is used to statistically measure the goodness of fit of the model and it is called the coefficient of determination. The

Conclusion

This present study focused on the optimization of the tool flank wear in face milling of EN-31 steel using various cooling/lubrication techniques. The experimentation is done as per Taguchi based L18 mixed orthogonal array. The conclusions of the current study are as given.

  • a)

    Based on the results of ANOM, the optimum parametric setting of lower tool flank wear is obtained as 110 m/min cutting speed, 60 mm/min feed rate, 0.4 mm depth of cut and when MQL with 150 ml/hr LFR as machining environment.

  • b)

CRediT authorship contribution statement

Vijay Kumar Sharma: Conceptualization, Methodology, Investigation. : . Talvinder Singh: Visualization, Software. Mohit Rana: Data curation, Writing – original draft. Anoop Kumar Singh: Supervision, Validation. Kashidas Chattopadhyay: Writing – review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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