1 Introduction

Turning of hard to machine materials like hardened steels or high alloy steels is always a challenge in the manufacturing industry due to excessive energy consumption in material removing of hardened materials. The high strength and wear-resistant properties of these hard alloy steel make them suitable for the manufacturing of machine tools, precision components used in automobiles, aircraft and rolling mills molding, and marine industries. In the analysis of energy consumption, a key dominant metric is power consumption, which consists of the machine power consumed by a machine tool’s actions plus the cutting power needed to remove the desired material from the workpiece [1]. It closely relates to the specific energy consumption (SEC), i.e., the energy consumption for removing a unit volume of material from the workpiece [2]. For this reason, significant research has been done to develop predictive models for power consumption in machining. These models provide numerical relationships that enable manufacturers to optimize energy consumption and improve the energy efficiency of various material types.

Energy consumed in the hard turning process depends on the workpiece material, type of machine tool, machining parameters, cutting tool insert material and its geometrical features, tool path trajectory, and ultimately the wet or dry environmental conditions under which machining is performed.

In past, researchers have done extensive efforts to search for optimal machining parameters to minimize power consumption, minimum specific energy consumption, and to minimize the surface roughness, in the machining of different types of hardened steel, which is summarized in Table 1. Padhan et al., investigation of the power consumption of hardened AISI D3 steel by using nano-cutting fluid (graphene nanoparticle) revealed that energy consumption decreases under wet cutting conditions due to its efficient cooling and lubrication properties [3]. Benlahmidi et al. optimized the cutting parameters for minimum power consumption in turning hardened AISI H11 steel (50 HRC) with CBN tool inserts [4]. Chudy and Grzesik found that the power component consumed in material removal from hardened 41Cr4 (AISI 5140) steel was 20% of total power consumption in turning operation [5]. However, Żak used different shaped CBN insert tools to reveal that the actual power consumed in the hard turning operation of 41Cr4 alloy steel hardness was only 14% of the total power consumed [6]. The effect of machining parameters to optimize the energy efficiency, power factor, and active energy consumed by the machine in the machining of EN 353 alloy steel were investigated by Bilga et al. It reported that at optimum machining conditions, the power factor and energy efficiency were at the same level [7]. The power consumption of AISI D3 steel was optimized by Zerti et al. under a dry cutting environment [8]. Grzesik et al. investigated the effect of CBN tool nose radius on cutting power and specific cutting energy of hardened 41Cr4 alloy steel [9]. Park and Nguyen reduced the specific cutting energy to improve the energy efficiency of the hardened 4140 steel bar [10]. Nguyen et al. and Nguyen optimized the energy efficiency of hardened steel of 45 HRC and 51 HRC by using a self-propelled rotary tool [11, 12].

Table 1 Literature overview of various studies in minimization of power consumption, energy consumption and surface roughness for tuning operation of hardened steel
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CBN and ceramic tool inserts are widely used in the manufacturing industry for the machining of various hard materials such as alloy steels, die steels, high-speed steels, bearing steels, white cast iron, and graphite cast iron. Various studies have been conducted to investigate the performance of CBN and ceramic tool inserts in the machining of various hard materials. For example, Benlahmidi et al. [4], Chudy and Grzesik [5], Żak [6], Grzesik, et al. [9], and Park and Nguyen [10] used CBN tool inserts to identify various factors affecting power and energy consumption in hard turning of various grades of hardened steels, whereas Zębala and Siwiec [13], Bouacha et al. [14], and Das et al. [15] identified various factors affecting cutting forces, surface roughness, and tool wear by conducting experiments in the turning operation of various grades of hardened steels by using CBN tools.

Ceramic tool insert was used by Zerti et al. [8] in the power consumption analysis of AISI D3 steel, while Bensouilah et al. [16], Suresh and Basavarajappa [17], and Davoudinejad and Noordin [18] used ceramic tool inserts to identified various factors affecting cutting forces, surface roughness, tool wear, tool life, and surface integrity by conducting experiments in turning operation of various grades of hardened steels. Benga and Abrao [19] used CBN and ceramic tool inserts to investigate the tool life and surface roughness on bearing steel. Aouici et al. [20] compared the performance of coated and uncoated ceramic tool wear on AISI H11 steel. Anthony [21] compared the effectiveness of ceramic tool insert type along with, cermet and coated carbide tool inserts to investigate the effect on the cutting force and chip morphology of AISI D2 steel.

This literature review identified that the majority of research work performed on hard turning was carried out under a dry machining environment at high cutting speeds. The comparison of the effectiveness of CBN and Ceramic tool insert under wet machining conditions was left unaddressed by the researchers. The CNC turning operations, on hardened steels, that are large in dimensions and that carry large weight, required a CNC turning operation at low cutting speeds.

The objective of this study was to find out the optimal machining parameters of CNC turning operation for hardened alloy steel roll that consumed the minimal power, and minimum specific energy to produce the machined surface of the roll, with minimum surface roughness, at a low cutting speed, and under wet machining conditions. This study also contributes to comparing the effectiveness of CBN and ceramic cutting tool inserts under the wet machining environment of hardened alloy steel roll at low cutting speeds. The experimental work was performed on an actual large-diameter-sized, hardened steel roll of a hot rolling mill in the actual industrial environment with the industrial parameters that could incorporate the factors that can affect the results of this research. This makes it a distinguishable work among other researchers’ work.

2 Material and Method

2.1 Material

The workpiece material used in this study for machining operation was hardened alloy steel roll, manufactured by Camet. The material specification of the alloy steel roll is shown in Table 2. The diameter of the hardened steel alloy roll was 315 mm. The overall length of the roll was 1426 mm, while the barrel portion of the roll was 700 mm in length. The barrel was that portion of the roll that was used for the CNC turning operation. The weight of the hardened alloy steel roll was 538 kg. Figure 1 illustrates the microstructure of the alloy steel roll material that was performed on the Tescan VEGA3 series at 1000X. The image shows that the alloy roll has a pearlite structure of matrix with primary and secondary carbides.

Table 2 Chemical analysis of alloy steel roll used in the experimental work
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Fig. 1
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SEM image of the hardened alloy steel roll showing its microstructure

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2.2 Method

The methodology was comprised of the design of experiments, selection of input factors for experimental setup, execution of CNC turning, and collection of response data as shown in Fig. 2

Fig. 2
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Methodology showing the sequence of various work performed

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2.2.1 Design of Experiment

Statistical methods, especially in the domain of design of experiments (DOE), are a very useful tool for data analyses. In this study, a mixed-level design (21 × 33) with four machining input factors including cutting speed, feed, depth of cut, and tool insert type was used for the experimental design. Tool insert was a categorical factor with two levels, while cutting speed, feed, and depth of cut were three-level continuous factors as shown in Table 3, thus resulting in fifty-four treatment combinations. These factors and associated levels were defined according to the machine capacity and Union materials cutting tool catalog. The recommended machining parameters for finish hard turning of steel (H ≥ 45 HRC) by the Union materials cutting tool catalog were, 40 m/min ≤ Vc ≤ 200 m/min, 0.05 mm/rev ≤ f ≤ 0.5 mm/rev, and 0.1 mm ≤ d ≤ 0.5 mm. The low cutting speed levels were selected because of the high weight of the roll, i.e., 538 kg, and the large-sized dimensions of the hardened alloy steel roll, i.e., 1426 mm overall length of the roll. The analysis of results and the optimization were carried out on Minitab 19 software.

Table 3 Input factors in the design of experiments, with their levels and design points
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2.2.2 Experimental Setup

CNC lathe CK8470 × 3500 with SINUMERIK 828D-CNC system was used to perform the experimental runs. The machine size used was 7450 × 2280 × 1950. The spindle chuck had a diameter of 650 mm, and it could clamp the workpiece of diameter ranging from 85 to 500 mm. The main motor had a power capacity of 37 kW. The maximum limit of workpiece load and length that can mount was 6-ton and 3500 mm, respectively. The hardened alloy steel roll used for this experimental run was a large-sized workpiece. The roll diameter was 315 mm, the overall length of the roll was 1426 mm, and it carried 538 kg weight, which was within the specified limits of the CNC lathe.

Diamond-shaped inserts of CBN and ceramic with identical dimensions were used to study the effect of tool insert material. The CBN tool insert had an ISO designation number DNGA 150608 R1, grade SBN1000 made by Union Materials Corporation. The ceramic tool insert had an ISO designation number DNGA 150608 E040, grade ST500 made by Union Materials Corporation. A tool holder having designation number TDJNR 2525 M15 was employed to hold these tool inserts. The experiment was performed under wet environmental machining conditions by using Byco Socol soluble cutting oil having a viscosity index of 116. The workpiece diameter was kept constant for all experimental trials. A new insert was employed before each experimental run as it has already been found that the average specific cutting energy increases, as tool wear increases [22, 23].

2.2.3 Response Data Collection

The response data were collected for the surface roughness generated on the workpiece and the power consumed by the CNC lathe in the machining stage of the hardened alloy steel roll. Mitutoyo surface roughness tester was used to record the surface roughness values, while Fluke 43B power quality analyzer was employed to capture the total power consumption during machining of hardened alloy steel roll. The experimental data were recorded online on a laptop for each set of experiments and analyzed with the aid of Fluke 43B software. A total of fifty-four treatment combinations with three replicates each were recorded, as listed in Table 4. A new tool insert was used for each experimental trial under a wet machining environment. The workpiece material, machine tool setup, and response recording setup are shown in Fig. 3.

Table 4 Matrix for the design of experiments with responses
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Fig. 3
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Experimental setup for CNC turning, Fluke 4B analyzer setup, and the surface roughness tester used for the collection of response data

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3 Results and Discussion

The total power consumed by the CNC lathe machine in the machining stage is the summation of the cutting power and the idle power consumed by the machine as expressed in Eq. (1)

$$ P \, = P_{{\text{c}}} + P_{o} , $$
(1)

where P is the total power consumed by the CNC lathe in the machining stage, Po is the idle power and Pc is the cutting power. Po corresponds to the power demand to turn on: the main motor for rotating the workpiece; feed motor for movement of the cutting tool; coolant system; hydraulic pump; and computer console. Po can vary from one machine tool to another. Pc is the actual power consumed in removing the material from the workpiece during machining, Pc can be expressed as the summation of the power spent on the plastic deformation of the layer being removed and power lost in friction at the tool-chip interface and tool-workpiece. It is expressed in Eq. (2)

$$ P_{{\text{c}}} = P_{{\text{s}}} + \, P_{{\text{f}}} $$
(2)

where Ps and Pf denote the shear power and friction power. Here, it is obvious that the workpiece and the cutting tool with its subordinate insert influence Pc.

Specific cutting energy (Es) in machining is the energy required to remove a unit volume of material from the workpiece as given by Eq. (3)

$$ E_{{\text{s}}} = \frac{{P_{{\text{c}}} }}{{{\text{MRR}}}} = \frac{{P - P_{o} }}{{f \times d \times V_{{\text{c}}} }}, $$
(3)

where MRR, f, d, and Vc denote the material removal rate, feed, depth of cut, and cutting speed, respectively.

Statistical data revealed that the minimum and maximum values for power consumption were 2.38 kW and 4.09 kW, for specific energy consumption was found to be 0.54 kJ/mm3 and 1.95 kJ/mm3, and for surface roughness was found to be 0.38 μm to 2.211 μm, respectively. Empirical cumulative distribution function (e-CDF) for power consumption, specific energy consumption, and surface roughness are shown in Fig. 4a–c, respectively, confirming a close fit by the normal curve. The mean value for power consumption, specific energy consumption, and surface roughness was found to be 3.072 kW, 1.03 kJ/mm3, and 1.027 μm, respectively.

Fig. 4
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Empirical CDF a power consumption, b specific energy consumption and c surface roughness

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3.1 Analysis and Optimization by Response Surface Methodology

Process parameters optimization was performed by using the response surface methodology (RSM) tool. RSM is a dominating methodology to analyze the outcomes of experiments for optimum response. It is a statistical technique that comprises design matrices having input variables and output variable(s). These input variables potentially influence the output variable(s). It is not only considered a tool, to search for the optimum solution, but it is also used to build empirical models among input and output variables. The input variables (cutting speed, feed, depth of cut, and tool insert) were independent variables, while the output variables (power consumption, specific energy consumption, and surface roughness) were the dependent variables in the design matrix of RSM. The mathematical function developed by RSM depends on the system’s response. If responses of the system fit well as a linear function of input factors, it reveals that the empirical model is based on a first-order polynomial equation. However, if there is a curve on the response surface of the RSM model, higher-order polynomial equations should be used for estimating the response model. Another important feature of the RSM is its desirability function. A desirability function is an effective tool for exploring the optimum condition(s) for the desired response target.

3.1.1 Full Quadratic Response Surface Design

Experimental investigations have found that in the machining operation, empirical models of first-order, second-order, and exponential models were fit for power and energy consumption [24, 25]. For this study, a full quadratic model was selected. The significance of adding quadratic terms to the two factor’s interaction was tested by p value. A full quadratic analysis of variance with a 95% confidence level for power consumption, specific energy consumption, and surface roughness was developed. The values of R2 were 89.76%, R2 (adj.) was 88.86% and R2 (Pred.) was 87.6% which was found for the power consumption model, while for the specific energy consumption model, R2 was 98.54%, R2 (adj.) was 98.41% and R2 (Pred.) was 98.24%, and for the surface roughness model, R2 was 97.84%, R2 (adj.) was 97.65% and R2 (Pred.) was 97.41%, it reveals the goodness of fit of these models. The second-order regression equations developed by RSM for power consumption, specific energy consumption, and surface roughness by using the CBN tool insert and ceramic tool insert are shown in Table 5. RSM model for the power consumption is shown in Table 6, which revealed that cutting speed, feed, depth of cut, and tool insert all were significant factors with a p value < 0.05, which indicates that these parameters have a statistically significant effect on the power consumption. F-value in these models gives the contribution amount of these parameters to the responses. For the power consumption model, cutting speed with the F-value of 694.4 was found to be the most contributing factor (48% contribution), and it was followed by tool insert type (16.27% contribution), feed (15.27% contribution), and depth of cut (3.33% contribution). Table 7 shows the RSM model for specific energy consumption. It revealed that feed was the highest contributing factor with 54.51% contribution for specific energy consumption, and it was followed by the depth of cut with 34% contribution. The contribution of cutting speed for specific energy consumption was only 0.08%. Table 8 shows the RSM model for the surface roughness generated on the machined surface of the roll, due to the CNC turning operation. It shows that the feed with an F-value of 5566.3 was the highest contributing factor. The contribution of feed was 81.35%, and it was followed by tool insert type with a 7% contribution. However, the contribution of cutting speed and depth of cut was only 1.15% and 0.26%, respectively.

Table 5 Regression equations developed by RSM for power consumption, specific energy consumption, and surface roughness
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Table 6 Full quadratic response surface design model for power consumption
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Table 7 Full quadratic response surface design for specific energy consumption
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Table 8 Full quadratic response surface design for surface roughness of hard alloy steel roll
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The main effect plots and the two-way factorial interaction plots for power consumption are shown in Fig. 5a and b, respectively. The main effect plot in Fig. 5a revealed that the minimum power consumption was at the cutting speed of 40 m/min, feed of 0.1 mm/rev, and depth of cut of 0.3 mm with CBN tool insert. Figure 5b shows that the two-way factor’s interactions of cutting speed with the feed, the interaction of cutting speed with the depth of cut, and the interaction of feed with the depth of cut were significant.

Fig. 5
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Factorial plots for power consumption in the machining stage of the hardened alloy steel roll. a Main effect plots, b two-way factorial interaction

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The main effect plots and the two-way factorial interaction plots for specific energy consumption are shown in Fig. 6a and b, respectively. Figure 6a shows that the mean specific energy consumption was minimum at the feed of 0.20 mm/rev, and the depth of cut of 0.5 mm with the CBN tool insert. The flat line for the cutting speed in Fig. 6a stated that cutting speed was not the main contributing factor to minimize the specific energy consumption. Figure 6b shows that the two-way factor’s interactions of the cutting speed with the feed, the interaction of the cutting speed with the cutting tool insert type, the interaction of feed with the depth of cut, the interaction of feed with the cutting tool insert type, and the interaction of depth of cut with the cutting tool insert type, all these interactions were found significant.

Fig. 6
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Factorial plots for specific energy consumption in the machining stage of the hardened alloy steel roll. a Main effect plots, b two-way factorial interaction

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The main effect plots and the two-way factorial interaction plots for surface roughness are shown in Fig. 7a and b, respectively. The main effect plots in Fig. 7a revealed that the minimum surface roughness was at the cutting speed of 50 m/min, feed of 0.1 mm/rev, and depth of cut of 0.4 mm with CBN tool insert. Figure 7b shows that the two-way factor’s interactions of feed with the cutting tool insert was significant, while the curves all other interaction were found parallel to each other and were insignificant.

Fig. 7
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Factorial plots for surface roughness of the machined surface of the hardened alloy steel roll. a Main effect plots, b two-way factorial interaction

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3.2 Analysis using Response Contour Plots

A response contour plot, predicated by the quadratic model, was developed to study the effect of input parameters on power consumption, specific energy consumption, and surface roughness as shown in Figs. 8, 9, and 10, respectively. Figure 8 shows the contour plots for the power consumption at three different levels of cutting speed with CBN and ceramic tool inserts. It showed that the contour region for minimum power consumption (P < 2.7 kW) was at a cutting speed of 40 m/min with CBN insert, as shown in Fig. 8a. The curved lines of contours shown in surface plots were the indication that both feed and depth of cut have a significant effect on power consumption. The comparison of contours at three different levels of cutting speed with the CBN inserts is shown in Fig. 8a, c, and e. It revealed that the power consumption was increased with the increase in cutting speed. The same results of cutting speed on power consumption were also found with the ceramic tool insert by comparing contour plots, shown in Fig. 8b with d and f. The effect of tool insert material (CBN and Ceramic) on the power consumption was compared at three levels of cutting speed because it was the most contributing factor (48.06%) in the power consumption model. The contour plots at cutting speeds of 40 m/min, 45 m/min, and 50 m/min are illustrated by comparing Fig. 8a with b, comparing Fig. 8c with d and by comparing Fig. 8e with f, respectively. This comparison revealed that the power consumed by the CBN tool insert was lower than the specific energy consumed by the ceramic tool insert at all three corresponding levels of cutting speed.

Fig. 8
figure 8

Contour plots for power consumption in the machining stage of the hardened alloy steel roll by using CBN and ceramic cutting tool inserts at three cutting speed levels

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Fig. 9
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Contour plots for specific energy consumption in the machining stage of the hardened alloy steel roll by using CBN and ceramic cutting tool inserts at three feed levels

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Fig. 10
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Contour plots for surface roughness produced at the machined surface of the roll by using CBN and ceramic cutting tool inserts at three feed levels

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The contour plots illustration for specific energy consumption is shown in Fig. 9. It showed that the minimum specific energy consumption contour region (Es < 0.63 kJ/mm3) was at the feed of 0.2 mm/rev with the CBN cutting tool insert, as shown in Fig. 9e. The vertical lines of contours in contour plots were the indication that the cutting speed is not significantly contributing to specific energy consumption and its contribution in the specific energy consumption model is only 0.08%. The comparison of contours at three different levels of feed with the CBN inserts is shown in Fig. 9a, c, and e. It revealed that the specific energy consumption was decreased with the increase in feed level. The same result of feed on the specific energy consumption was observed with the ceramic tool insert by comparing contour plots, as shown in Fig. 9b with d and f. The effect of tool insert material (CBN and ceramic) on specific energy consumption was compared at three levels of feed because it was the most contributing factor (54.51%) in the specific energy consumption model. Contour plots at feed level of 0.10 m/rev, 0.15 mm/rev and 0.20 mm/rev were illustrated by comparing Fig. 9a with b, comparing Fig. 9c with d and by comparing Fig. 9e with f, respectively. This comparison revealed that the specific energy consumed by the CBN tool insert was lower than the specific energy consumed by the ceramic tool insert at all three corresponding levels of feed.

Figure 10 shows the contour plots for the surface roughness at three different levels of feed with CBN and ceramic tool inserts. The effect of cutting tool insert type (CBN and Ceramic) on the surface roughness was compared at three levels of feed since the feed was found to be the highest contributing factor (81.35%) in the surface roughness model. This comparison at three different feeds showed that the surface roughness was decreased with a decrease in feed for both the CBN and the ceramic cutting tool inserts. The comparison of surface roughness by using the CBN and the ceramic cutting tool inserts at feed 0.1 m/min are shown in Fig. 10a and b, respectively, and it shows that surface roughness values obtained by using the CBN cutting tool insert were lower than the surface roughness obtained by using the ceramic cutting tool insert. The contour region for minimum surface roughness contour region (Ra < 0.4 μm) was at the feed of 0.1 mm/rev with CBN insert, and it is shown in Fig. 10a.

3.3 Optimization Using Desirability Function

The desirability function is an attractive optimization technique used for various industrial problems. Initially, each response yi is converted into an individual desirability function Δi that varies over the range of 0 to 1. If the goal is minimization and response yi is less than the target value T, Δi is taken as 1, it represents the ideal case. If response yi is higher than the target value T, Δi is taken zero, it represents an unacceptable configuration for the selected response. If response yi lies between target and upper value, Δi lies between 1 and 0. The individual desirability function given by Eq. (4) is used to search the optimal solution for minimization [26].

$$ \Delta_{i} = \left\{ {\begin{array}{*{20}l} 1 \hfill & {\quad y_{i} < T} \hfill \\ {\left( {\frac{{U - y_{i} }}{U - T} } \right)^{r} } \hfill & {\quad T \le y_{i} \le U} \hfill \\ 0 \hfill & {\quad y_{i} > U} \hfill \\ \end{array} } \right. $$
(4)

where Δi is the individual desirability defined for the ith targeted output, U is the upper limit value, T is the target value, and r is the desirability function index.

The composite desirability is the geometric mean of all the individual values of desirability and is given by Eq. (5):

$$ D \, = \left( {\Delta_{1} .\Delta_{2} .\Delta_{3} \ldots \Delta_{m} } \right)^{\frac{1}{m}} $$
(5)

where m is the number of responses.

Table 9 represents the goal and target range used in optimization. The optimal solution was found at a composite desirability value of 0.9345, having a response fit value was 2.709 kW for power consumption and 0.7855 kJ/mm3 for specific energy consumption, and 0.6796 μm for surface roughness. The corresponding values of optimum parameters were cutting speed of 41.2318 m/min, feed of 0.1333 mm/rev, and depth of cut of 0.4939 mm with CBN cutting tool insert as shown in Fig. 11. The experimental validity at the optimal parameters in Table 10 shows that a 3.16% error in power consumption, 4.5% error in specific energy consumption, and 4.28% error in surface roughness were observed between the predicted value and the experimental value performed at optimal machining parameters.

Table 9 Goal and target used in optimization
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Fig. 11
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Optimum solution obtained by using the desirability function

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Table 10 Validation of model showing predicted and experimental values at optimal machining parameters
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4 Conclusion

This experimental study was carried out to perform a CNC turning operation on a large-sized, hardened alloy steel roll, at a low cutting speed, under wet machining conditions. The aim was to determine out the optimized machining parameters that consumed minimum power, and minimum specific energy to produce a machined surface of the roll with minimum surface roughness. The results obtained in the experimental study, at three different levels of cutting speeds, feeds, and depth of cuts with the use of two different types of cutting tool inserts, CBN and ceramic are as follows:

  • RSM full quadratic models reported that cutting speed was the highest contributing factor for power consumption and feed was the highest contributing factor for both the specific energy consumption and the surface roughness.

  • The cutting tool insert type was found statistically significant factor for power consumption, specific energy consumption, and surface roughness

  • The comparison of contour plots between the CBN and the Ceramic cutting tool inserts reported that the CBN cutting tool inserts were more effective than the Ceramic cutting tool inserts, at low cutting speeds and under wet machining conditions. The lowest contour line for the minimum power consumption (P < 2.7 kW) was found at a cutting speed of 40 m/min with the CBN cutting tool insert, the lowest contour line for the minimum specific energy consumption (Es < 0.63 kJ/mm3) was found at the feed of 0.2 mm/rev with the CBN cutting tool insert, and the lowest contour line for the minimum surface roughness (Ra < 0.4 μm) was found at the feed of 0.1 mm/rev with the CBN cutting tool insert.

  • Desirability analysis found that the cutting speed of 41.23 m/min, feed of 0.133 mm/rev, depth of cut of 0.49 mm with the CBN cutting tool insert were the optimized machining parameters, and the predicted power consumption was 2.709 kW, predicted specific energy consumption was 0.785 kJ/mm3, and predicted surface roughness of machined surface was 0.679 μm.

Collectively, this may be summarized that the CBN tool inserts were more effective than the ceramic tool inserts, for CNC turning of large-sized hardened alloy steel at low cutting speed under wet machining conditions.