Today's manufacturing sector demands for higher productivity, better product quality and economy in manufacturing process [1]. The tool life plays a major role in increasing the productivity [2]. Metals, especially alloy steels, are the mostly used materials in industries as many ways, for example, by casting, metal forming, machining and sintering having its own advantages and disadvantages. During machining, the performance of cutting tool is higher if the cutting edge of the tool can be used for longer time [3]. Due to tool wear, the tool has to be changed so that a fresh edge can be used [2], which leads to increase in production time and cost. The tool may be cheap, but interrupts the machining process, which costs time and therefore money [1]. The tools have to withstand high temperature and stress during turning; they have to be shock resistant during milling, corrosion resistant and chemically inert towards the work piece material [1] .

The most popular approach towards reducing the heat generated during cutting is by far, by employing cooling mechanism. The cutting conditions in metal cutting can be improved by the use of cutting fluids, acting both as a coolant and a lubricant [4-6]. Further to improve the characteristics of tribological processes, which are present on the contact surfaces between tool and workpiece, the coolants are used in machining processes [7].

Different types of cooling methods are used to overcome temperature rise. Among them, the use of emulsion fluid is the most popular cooling method, mainly because of economy and ease of use [8]. However, the main problem with the conventional coolant is that it does not reach the real cutting area. The extensive heat generated developed from the tool chip interface evaporates the coolant before it reaches the cutting area. Hence heat generated during machining is not removed and is one of the main causes of the reduction in tool life [9]. To overcome this problem, the cryogenic coolants are used. This work especially, focuses on the effect of cryogenic coolants on machining characteristics.

Bartley [10] firstly investigated the cryogenic machining using liquid CO₂ as coolant in 1953. Hollis [11] investigated that the life of carbide tool was increased by cryogenically cooling. The reduction of cutting forces, improvement in tool life and better surface finishing by use of liquid nitrogen was reported by Uhera and Kumagai [12]. Chattopadhyay et al. [13] also reported the same results in the machining of mild steel. Paul et al. [14] described the beneficial effect of liquid nitrogen used as a coolant in terms of tool life, surface roughness and tool wear. Hong et al. [15] investigated that, the use of cryogenic coolant in machining produced better tool life with better surface finish and reduced tool wear by minimizing the cutting temperature with reduced friction. Cakir et al. [16] concluded the influence of cutting fluid and gases applications on cutting forces, surface roughness and friction coefficient. Liu et al. [17] also discussed that the use of water vapour and other gases as coolants effectively reduced the cutting temperature, coefficient of friction and surface finish. Dhar and Kamruzzaman [18] discussed the beneficial effects of cryogenic cooling in machining. They conducted an experiment on cryogenic cooling and proved that cryogenic cooling helps to reduce the cutting temperature in tool work interface and maintain the cutting edge sharp, which results in better tool life, surface finish and higher dimensional accuracy as compared to dry and wet machining.

As seen from the literature, cryogenic cooling suggests many advantages in machining processes. Most of the investigations on machinability aspects are limited to the role and effectiveness of cryogenic cooling over dry and wet machining. In order to achieve minimum tool wear, good surface quality and minimum cutting forces the cutting conditions to be carefully selected with appropriate conditions, so that during machining, a new tool material is to be developed with lower coefficient of friction and high heat resistance. As per knowledge, no systematic research work has been reported in the literature to determine the optimum cooling conditions with appropriate cutting conditions for achieving better machinability. Hence, an attempt has been made in this paper to enhance the machinability characteristics in turning of AISI 4340 steel using uncoated carbide inserted with cooling conditions, cutting speed and feed rate as the process parameters. In the present investigation, Taguchi method and the utility concept have been led to determine the best combination of the process parameters by simultaneously minimizing tool wear and specific cutting force.

1.1. Taguchi Approach

Taguchi method is a powerful statistical tool, which is used to provide a parameter design of performance characteristics. It deals with a choice of suitable orthogonal array and assignment of parameters to obtain the best model with minimal number of experiments. Taguchi recommended the signal-to-noise (S/N) ratio as the main objective function for the matrix experiments, which led to determine the performance characteristics and the percent contribution of individual process parameter on responses through analysis of variance (ANOVA). Taguchi evaluated the signal-to-noise ratio as the log function in three types of categories i.e. lower-the-better (LB), higherthe- better (HB) and nominal- the-better (NB). Therefore, optimal level for a process parameter is a level, which results in highest value of S/N ratio in the experimental region. This paper discusses the application of Taguchi method and the utility concept for multiple quality characteristics [19], which engage the weighting factors to each of the S/N ratio of the responses to obtain a multiresponse S/N ratio for each trial of an orthogonal array.

In the present study, three input parameters, namely, cutting speed (v), feed rate (f) and different cooling conditions (cc) [Cooling conditions 1 signifies dry, 2 signifies wet and 3 signifies cryogenic cooling] were examined and the selection of the range of parameters were determined through preliminary experiments. The depth of cut 1 mm was kept constant throughout the machining and for each experiment a fresh cutting edge was used. The selected parameters and their related levels are shown in Table 1. According to Taguchi parametric approach, for three parameters and three levels, nine experiments are performed and hence L9 orthogonal array was selected as shown in Table 2.

Table 1. Process parameters and their levels

Table 2. L9OA, responses and the computed values of S/N ratio

A high power rigid lathe (Kirloskar) was used to perform the machining operations. Uncoated tungsten carbide inserts were used to machine the AISI 4340 steel with tool geometry as follows- rake angle: 15° (positive); clearance angle: 7°; major edge cutting angle: 91°; cutting edge inclination angle: 0° and nose radius: 0.4 mm. The AISI 4340 steel work material having initial diameter 58 mm and length 200 mm was used for turning. The chemical composition of the material is 0.36% C, 0.10% Si, 0.45% Mn, 0.040% S, 0.035% P, 1.00% Cr, 0.20% Mo and 1.30% Ni. It is used for components such as gears, shafts, studs and bolts. The steel is supplied in hardness range of 248- 302 HB. In general, tool wear is the function of length and time. Therefore, maximum wear values of the tools were measured at fixed machining time, i.e. after 2 minutes of machining. After experimentation, a tool room microscope and piezo-electric three component dynamometer setup was used to measure the V_c and main cutting force (F_c ). The specific cutting force (K_s ) is calculated from the following equations:

[1]

Where, d is the depth of cut, F_c is cutting force measure from dynamometer and f is the feed rate from process parameters. The measured values of V_c and K_s are shown in Table 2.

2.1 Dry, Wet and Cryogenic machining:

In dry machining (Figure 1), the experiments were performed without use of any coolant. In wet machining, an oil based conventional coolant (mixing ratio of 1:20) is used which is supplied by the nozzle at rake face and principal flank surface of the tool. In cryogenic machining, external cooling method is used having a pipe with an internal diameter of 1.5 mm. The cryogenic cooling setup is shown in Figure 2. The flow of liquid nitrogen from the nozzle was targeted at the rake face along with the main cutting edge of the tool. The flow was controlled using flow meter. To avoid the possibility of excessive cooling, liquid nitrogen was used in the jet form flowing from a nozzle at a predetermined rate of flow of 0.36 lit/min.

Figure 1. Turning at lathe

Figure 2. Coolants Used

3. Analysis of Data, Results and Discussions

3.1 ANOVA & S/N ratio analysis

In the present study, the multiple performance characteristics, namely, V_c and K _s, are to be minimized and hence “lower the better type” quality characteristic has been selected for each of the responses. Signal to noise (S/N) is employed to interpret a response or quality characteristics and the largest S/N ratio is required. Equation (2) shows to calculate the S/N ratio.

[2]

Where, Y is the value of V_c as shown in Figure 3 and K_s obtained from measurements. In the utility concept [19], the multi-response S/N ratio for each trial in an orthogonal array is:

[3]

Where w₁ and w₂ are the weighting factors associated to the S/N ratio of each of the responses V_c and K_s , respectively. In this present work, weighting factor of 0.5 for each of the responses is examined, which gives equal priorities to both V_c and K_s for simultaneous minimization. The computed values of S/N ratio for each response and the multiresponse S/N ratio for each trial in the orthogonal array are shown in Table 2.

Figure 3. Tool Wear at 5x zoom

The two-factor interaction effects of process parameters on multi-response S/N ratio are analyzed to check the relative importance of the parameters on the machinability aspects. Figures 4 and 5 show the interaction effects due to cooling conditions with respect to cutting speed and feed rate. It can be observed that machinability is highly sensitive and affected with the change in cooling conditions at any cutting speed and feed rate

Figure 4. Interaction effect plot of cooling conditions and cutting speed

Figure 5. Interaction effect plot of cooling conditions and feed rate

Figure 6 illustrates the interaction effect due to change in cutting speed and feed rate. Although, when the cutting speed is low (57 m/min), the result of feed rate from 0.179 to 0.248 mm/rev is imperceptible. Likewise, small interaction effect occurs when the cutting speed is high (141 m/min), with increase in feed rate from 0.205 to 0.248 mm/rev. Further, it also observed that the machinability is highly sensitive to change in cutting speed for every value of feed rate studied.

Figure 6. Interaction effect plot of cutting speed and feed rate

From the above discussion, it is clear that the actual degree of two-factor interaction of the process parameters on machinability depends on the levels of cutting speed, feed rate and cooling conditions. Hence, analysis of means (ANOM) is carried out to determine the optimal combination of process parameters and ANOVA is performed to determine the percentage contribution of each parameter on multi-response S/N ratio [20-21]. The result of ANOM is represented in the response plot as shown in Figure 7. The level of a parameter with the highest S/N ratio is the optimal level. Thus, the optimal process parameter setting for the present study is v₁ , f₃ and cryogenic cooling conditions. Hence, the best combination values for simultaneously minimizing tool wear and specific cutting force are:

• Cutting Speed 57 m/min
• Feed Rate 0.248 mm/rev
• Cooling Conditions i.e. cryogenics

Figure 7. Factor response plot for multiple performance characteristics

Result of ANOVA on multi-performance characteristics are shown in Table 3. From ANOVA table, the cooling condition has maximum contribution (89.33%) in optimizing the multiple performance characteristics followed by cutting speed (5.25%) and feed rate (4.50%). However, the feed rate has the least effect (0.62%) in controlling the multiresponse. Further, it is also observed that ANOVA has resulted in 0.98% of error contribution, clearly indicating that the interaction effects of process parameters are negligible for simultaneously minimizing tool wear and specific cutting force.

Table 3. Summary of ANOVA for multi-performance characteristics

3.2 Confirmation tests

After selecting the optimal level of process parameters, the final step is to predict and verify the multiple performance characteristics. The predicted optimum value of S/N ratio is [20]:

[4]

where is the signal to noise ratio of optimum level i of factor j, m is the overall mean of S/N ratio and p is the number of main design parameter that affect the machining characteristics.

The confidence interval (CI) of for the optimum process parameter level combination at 95% level is evaluated to validate the closeness of the observed value of S/N ratio with that of the predicted value . The CI is given by [21]:

[5]

where F(1,Ve) is the F value from the F table from any statistical book at 95% confidence level (5.32 tabulated) , Ve is error of variance (0.294419), where N = Total no of experiments, v = degree of freedoms of p factors (0.55555).

If the prediction error i.e. within the CI value, then the optimum factor level combination and additive model for the factor effects in this experiment are valid. Here, the optimum combinations for the above process parameters were set. From the result of conformity test (Table 4), it can be observed that the calculated value of the prediction error is within the confidence limit. This indicates that the additive models are adequate and validated

Table 4. Results of the confirmatory test

3.3 Discussion

From the given Taguchi optimization results, it is observed that low temperature cooling effect i.e. cryogenic cooling is required for minimizing both tool wear and specific cutting force. This can be explained that, the cutting temperature increased with an increase in the cutting speed and feed rate. The increase in temperature negatively affects the tool properties (hardness and wear resistance) and dimensional accuracy of the part to be machined. The tool wear and specific cutting force decreases in cryogenic cooling due to the low temperature effect of cryogenic fluid, which was directly applied to the rake and principal flank surface. Hence, there’s reduction of cutting temperature and provides low friction between the newly generated work piece and tool surface, and therefore there’s low wear rate of tool at machine zone due to minimization of abrasion and attrition mechanism. Also, the lower cutting forces produce minimal vibrations in machining. Thus lower surface roughness is obtained on the machined part during cryogenic machining.

Conclusion

In the present study, Taguchi parametric approach and the utility concept, a multi-response optimization method has been led to determine the optimal process parameters for simultaneously minimizing the tool wear and specific cutting force during machining of AISI 4340 steel with uncoated tungsten carbide insert. The optimum cooling conditions and the most appropriate cutting speed and feed rate were determined using ANOM and the relative significance of the parameters was identified through ANOVA. The following conclusions are drawn from the present investigations:

• The ANOM on multi-response S/N ratio indicates that cryogenic is the optimum cooling condition, cutting speed of 57 m/min and a feed rate of 0.248 mm/rev is necessary to simultaneously minimize tool wear and specific cutting force.
• The ANOVA illustrates that cooling condition is the most significant parameter followed by feed rate and cutting speed in optimizing the machinability characteristics i.e. V_c and K_s .
• The ANOVA also reveals that the error contribution is 0.40%, which clearly indicates absence of the interaction effects of process parameters on optimization of multiple performance characteristics
• Mathematical models for V_c and R_a are statistically significant as the P-value is less than 0.05 at 95% significance level. Both the models presented high determination coefficients (R² ) explaining 89.76% and 91.90% variability for V_c and R_a . It shows high significance of the model developed
• The validation experiment confirmed that the additive model is adequate for determining the optimum quality characteristics at 95% confidence interval.

 

Acknowledgment

The authors gratefully acknowledge the help and laboratory facilities extended to them by G.N.D.E.C. Ludhiana and NIT, Hamirpur.

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