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Abstract
Article Optimization of the Cutting Parameters Affecting the Turning of AISI 52100 Bearing Steel Using the Box-Behnken Experimental Design Method 1, 2 1 Aytaç Yıldız *, Levent Uğur and İsmail Enes Parlak Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, Bursa 16330, Turkey Department of Mechanical Engineering, Faculty of Engineering, Amasya University, Amasya 05100, Turkey * Correspondence: aytac.yildiz@btu.edu.tr Abstract: In this study, we aimed to optimize the cutting parameters that affect the minimum temperature and power consumption in the turning of AISI 52100 bearing steel. For this, the Box- Behnken experimental design method, which was used for the lowest number of experiments in the experimental systems created using the response surface method (RSM), was used. The cutting parameters affecting the turning of the AISI 52100 bearing steel were determined as the cutting speed, depth of cut, and feed rate based on a literature research. The temperature and power consumption values were obtained via analyses according to the experimental design method determined by the finite element analysis (FEM) method. The results obtained were analyzed in Design Expert 13 software. According to the analysis results, the parameter values were determined for the minimum temperature and power consumption. The temperature and power consumption variables were affected by all three parameters, namely the cutting speed, depth of cut, and feed rate. For the minimum temperature and power consumption, a cutting speed of 162.427 m/min, depth of cut of 1.395 mm, and feed rate of 0.247 mm/rev, as well as the feed rate parameters, affected both the temperature and power consumption the most. In addition, it was determined that the cutting speed parameter had the least effect on both the temperature and power consumption Citation: Yıldız, A.; Uğur, L.; Parlak, İ.E. Optimization of the Cutting variables. In addition, validation experiments were carried out in a real experimental environment Parameters Affecting the Turning of with optimum values for the cutting parameters. The results showed that the output values AISI 52100 Bearing Steel Using the obtained within the limits of the study with the obtained equation were quite close (3.3% error for Box-Behnken Experimental Design temperature, 6.6% error for power consumption) to the real experimental outputs. Method. Appl. Sci. 2023, 13, 3. https://doi.org/10.3390/app13010003 Keywords: AISI 52100; response surface method; cutting parameters; FEM analysis Academic Editor: Abílio Manuel Pinho de Jesus Received: 28 November 2022 1. Introduction Revised: 8 December 2022 Machining is basically a chip formation process in which the excess material on a Accepted: 13 December 2022 Published: 20 December 2022 workpiece is removed using cutting tools [1]. There are important factors that are used to determine the quality increase in the products manufactured using the machining method, including the machine tool, characteristics and coating of the cutting tools, coolant, cutting conditions, cutting speed, depth of cut, and feed of cut that are used. Copyright: © 2022 by the authors. These elements are the parameters that directly affect the quality of the produced material Licensee MDPI, Basel, Switzerland. [2]. Choosing the appropriate cutting parameters and cutting tool in machining This article is an open access article distributed under the terms and operations provides important advantages such as high productivity, the desired surface conditions of the Creative Commons roughness, low costs, and energy efficiency [3,4]. Attribution (CC BY) license In machining processes, because of the wear of the cutting tool in various ways, some (https://creativecommons.org/licenses/ changes occur as it moves away from the initial conditions. A lack of homogeneity in the by/4.0/). structural compositions of the tool and test material could be the origin of changes in the Appl. Sci. 2023, 13, 3. https://doi.org/10.3390/app13010003 www.mdpi.com/journal/applsci Appl. Sci. 2023, 13, 3 2 of 23 ideal initial conditions. In addition, factors such as the type and amount of tool wear [5], chip shape, fluctuations in cutting forces, and chattering vibrations that occur in the natural course of the process are also factors that disrupt the continuity of the machining and cause unpredictable results [6]. Therefore, it is important to examine many variables such as the wear of cutting tool, surface integrity, cutting tool breakage, cutting temperature, and power consumption in machining operations [7,8]. Metal cutting is performed because of the movement of the tool and the test material relative to each other. By transferring the energy given to the machine to the cutting tool and test material, the process of chip removal is carried out with linear or circular motions in various axes. For this reason, the cutting speed, feed rate, and depth of cut come to the fore as basic parameters [6]. One of the oldest and most used techniques for metal cutting is turning with a single-point cutting tool. During turning, some of the energy transferred to the machine to remove chips from the material turns into heat energy due to the high cutting speeds and pressure, as well as high temperatures at the tool and workpiece contact points. Although the increase in temperature facilitates deformation, it affects the material properties and accelerates diffusion [9]. Although the main trigger of the generating temperature is the friction force, the shear strength and plastic deformation also have a share in increasing the cutting temperatures. Thanks to the chip, a significant percentage of the heat generated during cutting is removed. However, the spread of a small amount of heat towards the cutting tool and the workpiece affects the material properties and leads to the advancement of various wear mechanisms and wear types on the edge of the cutting tool. Consequently, it is essential to examine the factors that affect the cutting temperatures [10]. In addition to the cutting temperature, the energy savings for machine tools used in a significant part of the production sector in industrial equipment is becoming increasingly important for customers, consumers, industrial equipment, and governments [11]. The power requirements of a machine tool include variable and fixed power components. It has been determined that a machine tool’s energy consumption depends on the average power demands and the machining time determined by the cutting parameters. The environmental performance of processing systems may be significantly improved by increasing the energy efficiency of the machines. With the recent increase in energy demands, energy conservation has become a priority in the manufacturing industry. When the effects of the processing parameters on the power consumption have been examined using parameter optimization methods such as the Taguchi, response surface method (RSM), and ANOVA techniques, it has been revealed that up to 40% energy conservation can be achieved by choosing the optimum parameters [12]. Turning, which is frequently used in machining, is one of the most frequently used methods in experimental studies on machining. In order to reduce time and cost in production, the use of scientific methods is becoming more common by the day. Experimental design methods also play an important role for businesses to increase their market share and work effectively in increasing competition conditions [13]. In this paper, the studies on the optimization of the cutting parameters that are effective in the processing of AISI 52100 bearing steel used in our work, together with the studies using the RSM method for the optimization of the parameters that are effective in the processing of materials, are analyzed in the Materials and Methods section. For the machining experiments we used AISI 52100 steel bearing, which is widely used in various mechanical applications due to its high tensile and fatigue strengths. The experiments were first performed with the FEM (finite element method) and parameter optimization was carried out with an experimental design method using a Box-Behnken- based RSM approach. The input parameters used in the study were selected as the feed rate, cutting speed, and depth of cut, considering the studies in the literature. Using these input parameters, the effects of these parameters on the power consumption and temperature outputs were analyzed. By using the optimum values obtained for the cutting parameters, estimations with the regression equation, simulations with the FEM, and Appl. Sci. 2022, 13, 3 3 of 23 validation experiments in a real experimental environment were performed. The aim of this study was to optimize the cutting parameters for the minimum temperature and power consumption when turning AISI 52100 bearing steel using the RSM method. Thus, we aimed to increase the durability of the cutting tools by optimizing both the temperature and power consumption variables to protect the microstructure of the processed material and to provide benefits such as energy savings. With the equations obtained here, real experiments were carried out on a machine tool with the optimum input parameters. The contribution of this study is the optimization of the power consumption and temperature outputs for AISI 52100 steel bearings by analyzing them with FEM experiments and real experiments, and to the best knowledge of the authors, this study is the first in the literature. In addition, this study is the first to use the Box-Behnken method with output responses for power consumption and temperature. All experimental data are shared in this paper in order to contribute to the machining community. In the next section of the study (Section 2), the cutting process, RSM, and related studies in the literature are summarized. Additionally, Section 2 includes the FEM simulation and experimental design method. Section 3 includes the comparative results and a discussion of the study. In Section 4, the conclusions and future study directions are given. 2. Materials and Methods Figure 1 shows a diagram of the methodology used in the study. Firstly, similar studies in the literature were discussed, and considering these studies, the input parameters and response outputs of the cutting process were determined. However, the Box-Behnken experimental design, which has not been used in this field before, was used as the method in the literature. The experiments were first performed with FEM in accordance with the experimental design approach. The effects of the input parameters on the outputs were examined and their relationships were analyzed. Then, the optimum values of the input parameters were obtained, and by using these values both the regression equation was estimated and the FEM experiments and real experiments were carried out for validation. Finally, the results obtained by these three methods were compared and analyzed. Figure 1. The proposed methodology. Appl. Sci. 2022, 13, 3 4 of 23 2.1. Experimental Design Methods Experimental design helps identify variables that affect the quality characteristics in a process. Thus, the expected performance from the process or the optimum level of the related quality characteristic can be determined and the quality of the process can be improved. Experimental design is used in many sectors, especially in the development of production processes, new product design, formulation development, and process optimization. It allows the experimental study to be carried out without the consumption of excessive resources (time, material, personnel, equipment, etc.) and the results to be interpreted and all factor effects to be seen [14]. Thanks to the experimental design methods, the parameters of the relevant process can be defined and the important parameters can be controlled [15]. While using an experimental design method to determine how parameters affect the objective or response functions, as the traditional approach, one variable is changed at a time. However, this approach is time-consuming, especially for multivariate systems and also when considering multiple responses. The statistical design of experiments, on the other hand, reduces the number of experiments that need to be conducted, considers the interactions between variables, and can be applied to optimize the process parameters of multivariate systems [16]. Experimental designs are primarily divided into two groups as classical and modern methods. These methods are briefly described below. 2.1.1. Classical Methods One factor at a time: According to classical experimental design approaches, the easiest and most frequently applied method is to observe the changes in product or process performance by changing the level of a single factor in each experiment. As the number of factors at hand increases, different classical designs that can be applied begin to appear. Full factorial experimental design: The experimental design method that seems most ‘optimal’ in terms of evaluating and interpreting the effects of factors is the full factorial experimental design approach because all possible combinations of different levels of factors are evaluated. In such designs, an equal number of test results are taken from each level of each factor and they are compared with each other. This method can only be used when very few factors are involved because the number of experiments required increases rapidly with the number of factors and their levels. Partial factorial experimental design: With this approach, only a consciously chosen part of the possible combinations is tried and evaluated. In this way, all resources, especially manpower, as well as time and money are saved during the experiments. With this design method, orthogonal indexes are used. By changing the level of more than one factor in each experiment, it is ensured that the levels of all factors are tested with a small number of experiments. In order to reduce the number of experiments for partial factorial experimental design, “high value” and “low value” are chosen as the two possible factor levels. Thus, instead of performing all combinations of experiments, only the effects of the factors and levels that are thought to affect the results in terms of the performance characteristics can be investigated [17]. 2.1.2. Modern Experimental Design Taguchi experiment design: The Taguchi method allows the creation of full factorial experiments with a small number of partial factorial selections to a high degree. In the experimental design achieved with this method, experimental errors can be reduced and the reproducibility and efficiency of the laboratory experiments can be increased. With this method, which allows different situations to be designed together, flexible, and compatible with each other, the effects of the parameters on the results can be determined independently of each other [18]. Appl. Sci. 2022, 13, 3 5 of 23 2.1.3. Response Surface Method (RSM) The RSM covers a collection of mathematical and statistical techniques that rely on fitting a polynomial equation to experimental data and need to describe the behavior of a data set in order to make statistical predictions. This approach can be well applied when a response or a set of responses of interest is affected by several variables. The aim is to optimize the levels of these variables simultaneously to achieve the best system performance [19]. The experimental work using the RSM is greatly reduced compared to the number of runs determined using the full factorial design. Besides the reduction in experimental studies, the results from the RSM are claimed to be statistically acceptable [20]. The response surface method (RSM) was used in the experimental design. The RSM is an engineering tool used in the design phase of a new product or process to improve and optimize its performance [21]. The RSM was first introduced by Box and Wilson (1951) and later developed for an experimental design and data analysis [22]. The method includes a sequential process in which statistical and mathematical techniques are used. In this process, the researcher investigates the type of appropriate approximation function, the selection of the appropriate experimental design layout, the location and shape of the optimum region, and the necessity for transformation for the response or design variables [22]. The RSM design process can be summarized in three steps: (i) designing a set of experiments for the measurement of responses (elimination trials); (ii) determining the mathematical model with the best fit between the input variables and the response and obtaining the optimal set of experimental settings that yields the response’s maximum or minimum values (region research); (iii) expressing the effects of the parameters affecting the process with 2D or 3D graphics [23]. The RSM is mostly about approximating an unknown complex function using a first- order or second-order model of a low-order polynomial [15]. If the response variable is expressed as a linear function of the independent variables, the use of the first-order model is appropriate [24]. If there is a curvilinear relationship in the system, the main effect models called first-order models are insufficient. In this case, there is the establishment of a higher order polynomial, such as a second-order polynomial. This model is also called the second-order model. The following first-order model (Equation (1)) is generated if the system response in the RSM fits the independent variable well as a linear function [25]: 𝑦 = 𝛽 + 𝛽 𝑥 + 𝛽 𝑥 + ⋯+ 𝛽 𝑥 + 𝜀 , (1) 0 1 1 2 2 𝑘 𝑘 A quadratic model (Equation (2)) could be more suitable if the system’s response surface is curved: 𝑘 𝑘 2 𝑘 −1 ∑ ∑ ∑ 𝑦 = 𝛽 + 𝛽 𝑥 + 𝛽 𝑥 + 𝛽 𝑥 𝑥 + 𝜀 (2) 0 𝑗 =1 𝑗 𝑗 𝑗 =1 𝑗𝑗 𝑗 𝑖 𝑖 𝑗 where 𝑦 is the response variable, 𝛽 is the unknown regression parameter, 𝑥 and 𝑥 𝑘 𝑖 𝑗 are process variables, and 𝜀 is the error term. The RSM method uses two different designs, the Box-Behnken design and central composite design [25]. Central Composite Designs (CCD): The design developed by Box and Wilson in 1951 can work with both linear and quadric models. The CCD can be considered as a good alternative to the three-level full factorial design, providing a smaller number of experiments [26]. Box-Behnken design: This is a very important experimental design that allows for calculating the response function and estimating the system performance at any experimental point in the examined range by performing a small number of studies [16]. The Box-Behnken method, which is used to develop the models created on the basis of the designs and to determine the optimum experimental conditions, is a three-level design that yields a second-order multivariate polynomial with factor and response 𝑖𝑗 Appl. Sci. 2022, 13, 3 6 of 23 surfaces, the number of which can be increased. The response surface results allow the determination of variables within the range of their maximum or minimum values. In addition to the factorial designs, midpoints connecting the corner points of the study area and repeat experiments at the center are used [19]. It is stated that the Box-Behnken design is obtained by combining the two-level factorial design with the incomplete block design and adding a certain number of copy center points. Additionally, the implementation of the second-level model is seen as the second-best option in the Box-Behnken design. An advantage of the Box-Behnken design is that successful results can be obtained with fewer experiments [16]. Box-Behnken designs are used effectively in the quadratic estimation of models, construction of sequential models, analyses of confidence, blocks, and experimental designs [15]. If we look at the experimental designs in general, the number of experiments required for a factorial design is very large. Therefore, it loses its effectiveness in modeling quadratic functions. Designs that present a smaller number of experimental points, such as Box-Behnken and CCD designs, are more often used [27], as they often require more experimental work for more than two variables than can be accommodated in practice. When the CCD and Box-Behnken designs are compared, the Box-Behnken design requires a smaller number of experiments under the same conditions. In this design, each variable can be examined separately at three different levels, and since the lower and upper limits for all variables are never covered at the same time, unsatisfactory results created by extreme values are prevented [28]. For this reason, the Box-Behnken experimental design was used in our study. In this context, a literature review was performed for the studies using the RSM for the optimization of the parameters that are effective in the processing of materials in the literature, as summarized in Table 1. Table 1. The literature review summary. Author(s) Output Response Input Parameters Used Material Used Surface roughness, coefficient of Applied load, sliding speed, and sliding Patnaik et al. [29] friction, disc mass loss, depth of Ti6Al4V distance wear, and hardness Kumar et al. [30] Surface roughness Shank speed, feed rate, and depth of cut Aluminium alloy 6061 Cutting speed, feed rate, and rotary tool Ibrahim et al. [31] Wear value Magnesium AZ31 speed Metal removal rate, kerf width, A pulse on time, pulse off time, and peak Stir-welded 5754 Shihab [32] and surface roughness current aluminium alloy Abdullahi and Oke [33] Surface roughness Speed, feed, depth of cut, and nose radius IS 2062 E250 plate Gü vercin and Yildiz [34] Surface roughness A feed rate cutting speed, depth of cut AISI 1040 Surface roughness and tool wear Kumar et al. [35] Cutting speed, feed rate, and depth of cut DAC-10 tool steel rate Magnetic field strength, number of working Singh et al. [36] Surface roughness Aluminium 6061 tubes cycles, and hydraulic pressure Surface roughness and material Panchal [37] Feed, cutting speed, and depth of cut EN-36 Alloy Steel removal rate Sivaraj et al. [38] Thrust force Drill diameter, feed rate, spindle speed Al-SiC Garcia et al. [39] Surface roughness Cutting speed, feed rate, and depth of cut 6082-T6 aluminium alloy Surface roughness and carbon Cutting speed, cutting tool material, depth 6061, 6082, and 7075 Ic et al. [40] emission of cut, and work piece material Aluminium alloys Surface roughness and material Surya et al. [41] Speed, feed, and depth of cut Ti6Al4 V removal rate Trung [42] Surface roughness Cutting speed, feed rate, and depth of cut AISI 1045 Spindle speed, depth of cut, feed rate, Raghavendra et al. [43] Surface roughness Ti 6Al4V coolant flow rate Appl. Sci. 2022, 13, 3 7 of 23 Cutting speed, the feed rate, and cutting Vu and Hung [44] Surface roughness 060A4 steel depth Surface roughness, material Radhi et al. [45] Speed, feed, depth of cut AISI440 removal rate, and temperature Trung et al. [46] Surface roughness Cutting speed, feed rate, and depth of cut C45 steel Velocity of workpiece, feed rate, and depth Son and Trung [47] Surface roughness 3X13 Steel of cut Reddy and William [48] Cutting force Cutting speed, feed rate, and depth of cut Inconel 625 Surface roughness and surface Cutting depth, the cutting speed, and feed Deng et al. [49] TC17 titanium alloy microhardness rate Surface roughness and process Table feed rate, pulse on time, and pulse Ferritic ductile cast iron Aydın et al. [50] time space (GGG-40) Uğur [51] Surface roughness Cutting speed, feed rate, and depth of cut Al 7075 Pulse on time, pulse off Shanthi et al. [52] Surface roughness Al 7075 time, wire feed, and wire tension 2.2. Workpiece Material AISI 52100 steel is of considerable interest in bearing and shaft construction due to its higher strength and better corrosion resistance. However, the machining of bearing steel materials such as AISI 52100 steel is one of the challenging areas in metal cutting industries due to their high hardness, resulting in low productivity and high production costs. Table 2 provides a summary of the studies on the optimization of the cutting parameters of AISI 52100 bearing steel. Table 2. A summary of studies on the optimization of the cutting parameters of AISI 52100 bearing steel. Author(s) Input Parameters Output Response Result Achieved Method Used Cutting speed has the most significant effect on both the tool life and radial Cutting speed, feed rate, Radial cutting vibrations Abidi et al. [53] cutting vibration. The feed rate has the Taguchi method and depth of cut and surface roughness most important effect on the surface roughness Gray relationship Panda et al. Cutting speed, feed rate, Surface roughness, flank The feed rate is the parameter that analysis based on [54] and depth of cut wear, and chip morphology affects the output responses the most. the Taguchi method Cutting feed, radial force, Alok and Das Cutting speed, feed rate, The cutting speed is the most effect Central Composite surface roughness, and [55] and depth of cut parameter for output responses Design (CCD) maximum flank wear Tool wear, surface Better surface roughness, less tool wear, Çetindağ et al. Cutting speed, feed rate, Minimum quantity roughness, and residual and higher compressive stresses were [56] and depth of cut lubrication (MQL) stresses obtained with wiper inserts. The feed rate and cutting speed strongly Cutting speed, feed rate, Cutting force and surface affect the surface roughness. The depth Das et al. [57] Taguchi method and depth of cut roughness of cut is the parameter that affects the cutting force the most The depth of cut has the maximum Bouacha et al. Cutting speed, feed rate, Surface roughness and effect on the cutting forces. The feed rate RSM [58] and depth of cut cutting forces is the most important parameter affecting the surface roughness CCD and TOPSIS Cutting speed, feed, The negative rake angle is the most (Technique for Umamaheswar depth of cut, tool nose Machining force and surface important parameter in terms of the Order Preference by rao et al. [59] radius, and negative roughness output responses Similarity to Ideal rake angle Solutions) Appl. Sci. 2022, 13, 3 8 of 23 The feed rate affects the surface Cutting speed, feed rate, Surface roughness and roughness the most. The depth of cut, Azizi et al. [60] Taguchi method and depth of cut cutting forces workpiece hardness, and feed rate affect the cutting forces. As the cutting speed increases, the Cutting speed, feed rate, Sivaiah et al. Surface roughness and tool surface roughness decreases. The feed depth of cut, and type of Taguchi method [61] flank wear rate has effects on both the tool flank tool wear and surface roughness. Cutting parameters (cutting speed, feed rate) Guddat et al. and geometries (tool Cutting forces and surface The insert type has more influence on 3D Topography [62] nose radius, edge radius roughness the surface roughness and cutting forces based on FEM chamfer angle, insert type) Tool nose radius, depth The tool nose radius is the parameter Sankar [63] Cutting force Taguchi method of cut, speed, and feed that affects the cutting force the most. The feed rate has the most effect on the Keblouti et al. Feed rate, cutting depth, Surface quality and cutting surface quality, while the depth of cut RSM [64] and cutting speed forces has the most effect on the cutting force. The feed rate is the parameter that Multiple linear affects the surface roughness and power Cutting speed, depth of Surface roughness, cutting regression analysis Serra et al. [65] the most. The power consumed is more cut, and feed rate time, consumed power, force (MLR) and genetic affected by the interactions of the cutting algorithm (GA) speed and feed rate. Paturi et al. Cutting speed, feed rate, Surface roughness and tool The feed rate affects the surface RSM [66] depth of cut flank wear roughness the most. Cutting speed feed, Tzotzis et al. The depth of cut is the parameter that depth of cut, and tool Cutting force 3D-FEM and CCD [67] affects the cutting force the most. nose radius The surface roughness is most affected Workpiece hardness, Cutting force, surface by the feed, the cutting force is affected Rafighi et al. Taguchi method and cutting speed, feed, and roughness, and sound by cutting speed, and the sound [68] TOPSIS depth of cut intensity intensity is most affected by depth of cut. Cutting speed, feed, Machining force, surface The negative rake angle is the parameter Umamaheswar depth of cut, tool nose roughness, and workpiece that affects the output responses the CCD and TOPSIS rao et al. [69] radius, and negative surface temperature most. rake angle The cutting speed and feed are Mane and Cutting speed, feed, and Cutting temperature important parameters that affect the CCD Kumar [70] depth of cut cutting temperature. Feed rate, cutting speed, The tool type for the cutting force and Bhandarkar Cutting forces, surface depth of cut, and tool feed rate parameters for the surface RSM [71] roughness, and tool wear type roughness have important effects. When the literature review in Table 2 was conducted, it was determined that there was no study investigating the effects of the cutting parameters on the temperature and power consumption with the Box-Behnken design in the turning of AISI 52100 bearing steel. For this reason, it was decided to use AISI 52100 bearing steel in this study. The AISI 52100 steel used in the study is a high-carbon (0.98–1.10%) and low-alloy supra-eutectoid steel with a chromium content of 1.3–1.6%. This steel can be deeply hardened by heat treatment and is widely used in automotive, gear, bearing, tool, and mold applications after its microstructure is changed and hardened as a result of different heat treatments. AISI 52100 steel is usually shaped by machining. However, annealed supra-eutectoid steels are not suitable for machining due to the hard and brittle cementite lamellae in their internal structure. Spheroidization heat treatment is the process of Appl. Sci. 2022, 13, 3 9 of 23 converting carbides into a spherical shape by slow cooling after keeping the steels around the Ac1 temperature for a long time with oscillating annealing in this region. After this process, the lamellar structure in the internal structure of the material is transformed into granular cementite, resulting in improvements in the ductility and machinability properties. During chip removal, a very high heat is generated because of the friction between the cutting tool and the workpiece and the plastic deformation of the workpiece material. The high heat that is generated causes an increase in temperature in the cutting zone, causing a decrease in the hardness of the cutting tool material and rapid wear [72]. The chemical composition and mechanical properties of the workpiece are shown in Table 3 and Table 4, respectively. In the experiments, the coated carbide tool in the form of SNMA120408 produced by Kennametal company in the K68 quality group was used. Table 3. The chemical composition of AISI 52100 bearing steel. Element C Si Mn P S Cr Mo Al Fe % 0.973 0.27 0.33 0.016 0.001 1.41 0.02 0.025 Balance Table 4. The mechanical and thermal properties of the AISI 52100 steel [73]. Properties Workpiece Material (AISI 52100 Steel) Young’s modulus (GPa) 210 Density (kg/m ) 7.83 Poisson’s ratio 0.3 Specific heat (J/kg °C) 476.975 Thermal conductivity coefficient (W/(m °C)) 46.6 Thermal expansion (µ m/(m °C)) 1.19 2.3. FEM Simulations and Experimental Design For the optimization of the cutting parameters affecting the turning of the AISI 52100 bearing steel material, the Box-Behnken experimental design, which was used in the lowest number of experiments in the experimental systems created with the RSM, was used. After the experimental design was decided, the cutting parameters affecting the turning of AISI 52100 bearing steel were determined based on the literature review in Table 2, with their levels given in Table 5. Table 5. The cutting parameters and levels affecting the turning of AISI 52100 bearing steel. Levels Parameters Low Center High Symbol −1 0 +1 Cutting Speed (m/min) V 150 175 200 Depth of cut (mm) d 0.5 1 1.5 Feed rate (mm/rev) f 0.1 0.2 0.3 After the experimental design was determined, the frequencies of the cutting parameters used in the studies in Table 2 were created and are given in Figure 2 to determine the cutting parameters affecting the turning of AISI 52100 bearing steel. Appl. Sci. 2022, 13, 3 10 of 23 Cutting Parameters 18 18 2 2 1 1 Feed rate Cutting Depth of Tool nose Negative Type of Edge Workpiece speed cut radius rake angle tool radius hardness chamfer angle Figure 2. The frequency rates of the cutting parameters used when turning 52100 bearing steel. According to Figure 2, for the optimization of the cutting parameters used in the processing of AISI 52100 bearing steel, it was observed that the feed rate was used in all 19 studies and cutting speed and depth of cut parameters were used in 18 studies. Due to this result, it was decided to use these cutting parameters in our study, and their levels are given in Table 5. According to the Box-Behnken experimental design, the number of experiments to be performed was determined as 15 experiments. In accordance with the experimental design created, the analyses were performed using the finite element method (FEM). The FEM analyses were performed using Third Wave AdvantEdge™ (version 7.1) software. This software is FEM software that is optimized for machining operations. Boundary conditions are required to simulate metal cutting operations using the finite element approach. By choosing the best modelling technique and modeling the material, these criteria may be utilized to compute the deformation rate, comprehend how the material of the workpiece will react during plastic deformation, and simulate metal cutting operations. The strain rate and temperature impacts of the structural equations must be identified to describe the dynamic behavior of the model. Changes in metal stress are dependent on the strain, strain rate, and temperature [74–76]. This study describes the mechanical behavior of the workpiece using the Johnson–Cook (JC) yield-surface-forming material model. Using Equation (3) from the Johnson–Cook material model, the flow stress of the workpiece is obtained: 𝜀 ̇ 𝑇 −𝑇 𝑜𝑜𝑚𝑟 𝜎 = [𝐴 + 𝐵 𝜀 ][1 + 𝐶 𝑙𝑛 ( )][1 − ( ) ] 𝜀 ̇ 𝑇 −𝑇 ⏟ ⏟ (3) 0 𝑚𝑒𝑙𝑡 𝑜𝑜𝑚𝑟 Elasto-Plastic Viscosity Thermal Softening TM The coefficient of friction used in AdvantEdge is defined by the Coulomb friction shown in Equation (4): 𝐹 = 𝜇 𝐹 (4) 𝑓 𝑛 where 𝐹 is the normal force exerted between the surfaces, µ is the coefficient of friction, and 𝐹 is the friction force. In this study, the coefficient of friction is defined as 0.5 between the cast iron and carbide materials. A 4-node, 12-degree-of-freedom mesh structure was used for the workpiece and cutting tool. The initial and mesh structures are shown in Table 6. Appl. Sci. 2022, 13, 3 11 of 23 Table 6. The mesh setup. Initial Tool Mesh Min element size 0.025 mm Max element size 1.8 mm Curvature safety 1.5 Mesh grading 0.2 Segments per edge 2 Initial Workpiece Mesh Size 20 × 20 × 10 mm Min element size 0.03 mm Max element size 5 mm Curvature safety 3 Mesh grading 0.3 Radius of refined region 0.15 mm Segments per edge 2.5 Min. element edge length Chip bulk 0.03866 mm Cutter edge 0.02913 mm The overall geometric framework for the finite element analysis with Third-Wave AdvantEdge is shown in Figure 3, for which 3D turning is preferred as the turning technique. Figure 3. The mesh structure and boundary conditions of the FEM model. The dimensions of the workpiece material used in the first stage of the analysis process were 10 × 5 mm. The refractive constants and JC parameters (used by Pawar et al.) were used as listed in Table 7 [77]. Figure 4 displays the cutting tool shape that was used in the analysis. Table 7. The JC parameters of AISI 52100 bearing steel [77]. AISI 52100 Steel A (MPa) 2482.4 B (MPa) 1498.5 N 0.19 C 0.027 M 0.66 Tmelt (°C) 1487 Appl. Sci. 2022, 13, 3 12 of 23 Figure 4. The cutting tool parameters. The experiments were performed based on the experimental design determined using the FEM. As an example, the results for the temperature and power consumption related to experiment no. 9 are given in Figure 5. Figure 5. The analysis results for experiment no. 9. The experiments were performed based on the experimental design determined using the FEM, and the obtained temperature and power consumption values are given in Table 8. Appl. Sci. 2022, 13, 3 13 of 23 Table 8. The Box-Behnken design and FEM analysis responses. Actual and Coded Level of Variables FEM Analysis Response Run Cutting Speed Depth of Cut Feed Rate Temperature Power Consumption No. (m/min) (mm) (mm/rev) (T, ° C) (P, W) 1 −1 (150) 0 (1) 1 (0.3) 310.56 42.24 2 1 (200) 0 (1) 1 (0.3) 434.642 115.19 3 1 (200) 0 (1) −1 (0.1) 295.257 50.86 4 −1 (150) −1 (0.5) 0 (0.2) 288.78 46.08 5 1 (200) 1 (1.5) 0 (0.2) 401.02 101.2 6 0 (175) 0 (1) 0 (0.2) 341.769 76.65 7 −1 (150) 0 (1) −1 (0.1) 271.266 40.77 8 0 (175) 1 (1.5) 1 (0.3) 442.699 129 9 0 (175) 0 (1) 0 (0.2) 367.183 76.26 10 0 (175) −1 (0.5) −1 (0.1) 245.309 30.59 11 0 (175) −1 (0.5) 1 (0.3) 364.016 69.139 12 0 (175) 0 (1) 0 (0.2) 359.215 76.87 13 1 (200) −1 (0.5) 0 (0.2) 311.755 60 14 0 (175) 1 (1.5) −1 (0.1) 300.143 58.11 15 −1 (150) 1 (1.5) 0 (0.2) 363.775 83.27 3. Results and Discussion The experimental design and the obtained FEM analysis responses were analyzed using the Design Expert 13 software. As a result of the analysis, the quadratic regression model equations suitable for the optimum cutting parameters, which gave the minimum temperature and minimum power consumption when processing the material, were formed as in Equations (5) and (6). 𝑅 = −266.998 + 5.7077𝑉 + 19.101𝑑 − 692.607𝑓 + 0.285 + (5) 2 2 2 10.009 119.245𝑑𝑓 − 0.0199𝑉 − 9.225𝑑 − 1570.758𝑓 𝑅 = −190.650 + 2.989𝑉 − 26.889𝑑 − 737.585𝑓 + 0.080 + 6.286 + (6) 2 2 2 161.705𝑑𝑓 − 0.0107𝑉 + 10.978𝑑 − 762.804𝑓 The F test was used to determine the accuracy of the model and the coefficients in the model related to the temperature and power consumption values obtained from the FEM analysis study, and the analysis of variance (ANOVA) was used to determine the contributions of the cutting parameters affecting the turning to the temperature and power consumption variables. The results of the analysis of variance of the quadratic model are given in Tables 9 and 10. Table 9. The ANOVA table for temperature. Sum of Mean Source df F-Value p-Value Squares Square Model 44,776.37 9 4975.15 10.94 0.0085 significant V 5423.25 1 5423.25 11.93 0.0182 significant d 11,083.89 1 11,083.89 24.38 0.0043 significant f 24,193.62 1 24,193.62 53.21 0.0008 significant Vd 50.91 1 50.91 0.1120 0.7515 Vf 2504.55 1 2504.55 5.51 0.0658 df 142.19 1 142.19 0.3127 0.6001 V 569.27 1 569.27 1.25 0.3140 d 19.64 1 19.64 0.0432 0.8436 f 911.00 1 911.00 2.00 0.2161 Residual 2273.33 5 454.67 𝑉𝑓 𝑉𝑑 𝑉𝑓 𝑉𝑑 Appl. Sci. 2022, 13, 3 14 of 23 Lack of Fit 1935.42 3 645.14 3.82 0.2145 Pure Error 337.91 2 168.95 Cor Total 47,049.71 14 2 2 R = 0.9517, R (adjusted) = 0.8647. Table 10. The ANOVA table for power consumption. Sum of Mean Source df F-Value p-Value Squares Square Model 10,583.57 9 1175.95 10.21 0.0099 significant V 1649.96 1 1649.96 14.33 0.0128 significant d 3435.00 1 3435.00 29.82 0.0028 significant f 3838.59 1 3838.59 33.33 0.0022 significant Vd 4.02 1 4.02 0.0349 0.8591 Vf 987.84 1 987.84 8.58 0.0327 significant df 261.49 1 261.49 2.27 0.1922 V 165.76 1 165.76 1.44 0.2840 d 27.81 1 27.81 0.2415 0.6440 f 214.84 1 214.84 1.87 0.2302 Residual 575.89 5 115.18 Lack of Fit 575.70 3 191.90 2010.84 0.0005 significant Pure Error 0.1909 2 0.0954 Cor Total 11,159.47 14 2 2 R = 0.9484, R (adjusted) = 0.8555. Looking at Table 9, it can be determined that the model was significant (the p-value is 0.0085 < 0.05) in the analysis of the temperature variables. From this analysis, it can be seen that the model parameters of the cutting speed (V), depth of cut (d), and feed rate (f) are significant and have an effect on the temperature variable. When the significance values are examined, it can be determined that the feed rate (f) has the greatest effect of the parameters on the temperature. When we look at the pairwise relations of the parameters, there is no significance. The adjusted R (0.8647) shows that the quadratic model can explain 86.47% of the variance in the response. According to Table 10, the model made for the power consumption variable was significant (the p-value is 0.0099 < 0.05). In this analysis, it can be seen that the model parameters of the cutting speed (V), depth of cut (d), feed rate (f), and cutting speed–feed rate (Vf) are significant and have an effect on the power consumption variable. Considering the significance values of the parameters, it was determined that the feed rate (f) has the greatest effect of the parameters on the power consumption, as well as on the temperature variable. The adjusted R (0.8555) shows that the quadratic model can explain 85.55% of the variance in the response. The predicted values obtained from the FEM analysis and quadratic regression model equations for the temperature and power consumption variables are given in Figure 6. It can be seen in Figure 6 that the predicted values are compatible with the actual FEM analysis results. This result shows that the developed quadratic regression model can be used to optimize the parameters that are effective for the turnability of AISI 52100 steel. After the ANOVA analysis, the effects of the parameters affecting the turning of the AISI 52100 bearing steel material on the temperature and power consumption variables were examined and the findings are given in Figures 7 and 8. Accordingly, the effect of the parameters affecting the turning of AISI 52100 steel on the temperature can be seen in Figure 7. Appl. Sci. 2022, 13, 3 15 of 23 Figure 6. The Box-Behnken experimental versus predicted results: (a) temperature; (b) power consumption. Figure 7. The effects of the parameters affecting the turning of the AISI 52100 bearing steel material on the temperature variable: (a) depth of cut-cutting speed; (b) feed rate-cutting speed; (c) feed rate- depth of cut. Appl. Sci. 2022, 13, 3 16 of 23 Figure 8. The effects of the parameters affecting the turning of the AISI 52100 bearing steel material on the power consumption variables: (a) depth of cut-cutting speed; (b) feed rate-cutting speed; (c) feed rate-depth of cut. In Figure 7, the effects of the factors affecting the turning of the AISI 52100 bearing steel material on the temperature variables are shown. Accordingly, when Figure 7a is examined, it can be seen that the depth of cut parameter has a greater effect on the temperature than the cutting speed parameter, and the temperature continuously increases when the depth of cut increases. In Figure 7b, it can be seen that the feed rate parameter affects the temperature more than the cutting speed parameter, and the temperature increases more as the feed rate increases. The effects of the feed rate and depth of cut parameters on the temperature variables are given in Figure 7c. Accordingly, it can be seen that the feed rate parameter affects the temperature more than the depth of cut parameter, and the temperature increases as the feed rate increases. When Figure 7 is examined, it can be seen that the pairwise interactions of the cutting speed–feed rate parameters increase the temperature the most, while the pairwise interactions of the cutting speed–depth of cut and depth of cut–feed rate parameters have almost the same effect on the temperature. In Figure 8, the effects of the factors affecting the turning of the AISI 52100 bearing steel material on the power consumption are given. When Figure 8a is examined, it can be seen that the depth of cut parameter has a greater effect on the power consumption than the cutting speed parameter, and when the depth of cut increases, the power consumption increases more. In Figure 8b, it is seen that the feed rate parameter has a greater effect on power consumption than the cutting speed parameter, and the power consumption Appl. Sci. 2022, 13, 3 17 of 23 increases more when the feed rate increases. The effects of the depth of cut and feed rate parameters on the power consumption can also seen in Figure 8c. When the figure is examined, it can be seen that the depth of cut and feed rate parameters affect the power consumption almost at the same rate, and the power consumption increases with the increase in both parameter values. When all three pairwise interactions are taken into account, it can be determined that the depth of cut and cutting speed parameters increase the power consumption more. After examining the effects of the parameters affecting the turning of AISI 52100 bearing steel on the temperature and power consumption, the optimum turning parameter values for the minimum temperature and power consumption and the temperature and power consumption values obtained with these values were obtained from the Design Expert 13 program and are given in Table 11. Table 11. Optimum cutting parameters and output responses. Optimal Cutting Parameters Output Response Depth of Cut Feed Rate Temperature Power Consumption Cutting Speed (m/min) (mm) (mm/rev) (° C) (W) 162.427 1.39 0.2472 382.096 88.788 In addition, these optimal values were used in the regression equations in Equations (1) and (2) and in the confirmation experiment in the FEM analysis, and the temperature and power consumptions were obtained. These obtained values are given in Table 12. Table 12. A comparison of the temperature and power consumption values for the optimum cutting parameters. Output Response Error Rate by RSM Result Power Power Temperature Temperature Consumption Consumption Second-order regression 384.14 93.745 0.5% 5.5% model predictions Confirmation experiment 400.92 98.66 4.9% 11.0% with FEM analysis Validation experiment in real 394.73 94.66 3.3% 6.6% experimental environment Additionally, the real experimental setup shown in Figure 9 was prepared and three validation experiments were performed with these optimum values in Table 11, and the output responses were obtained by averaging the test results (Table 12). The validation experiments were carried out on a Hannsa YTH 10700 CNC lathe under dry cutting conditions. The CNC lathe used had a 10 kW motor power and spindle-variable stepless speed, and could reach up to 3000 rpm. A Flir Thermal Imaging Camera was used to measure the cutting temperatures during cutting. A Hioki Po PW3198 phase power analyzer (Hioki Corporation, Nagano, Japan) was used to measure the effects of the cutting parameters on the power consumption during the experiments. This instrument had three current probes (CP-1201) and the measurements were made by connecting these probes separately to the energy input of the machine. In the study, since the effects of the cutting parameters on the power consumption were examined, it was reported that systems that increase the power consumption, such as the cooling system, should be turned off [78]. A schematic view of the turning experiments is presented in Figure 9. Appl. Sci. 2022, 13, 3 18 of 23 Figure 9. The experimental setup. When Table 12 is examined, it can be seen that there is a 0.5% error rate between the minimum temperature values obtained from the RSM and the regression model, and a 5.5% error between the power consumption values. The confirmation experiment using the FEM analysis shows that there is an error rate of 4.9% between the temperature values and of 11% between the power consumption values. In the turning test carried out in the real experimental environment, the temperature and power consumption in the cutting zone were measured as 394.73 °C and 94.66 W, respectively. When these values are compared with the results obtained from RSM, it can be seen that there were error rates of 3.3% in temperature and 6.6% in power consumption. According to these results, the second-order regression model for the temperature and power consumption had less errors. In the turning experiments, the temperature and power consumption in the cutting zone were measured at 394.73 °C and 94.66 W, respectively. Accordingly, it was noted that the second-order regression analysis for temperature and power consumption gave better results than the RSM results. When the results of the turning experiments in the real environment and FEM analyses were compared, it was seen that the temperature and power consumption results from the FEM analyses were higher than the turning experiments in the real environment. 4. Conclusions In this study, we aimed to optimize the cutting parameters affecting the turning of AISI 52100 bearing steel with the three-level Box-Behnken experimental design combined with the response surface method. To achieve this aim, the temperature and power consumption values obtained from the FEM analysis based on the cutting speed, depth of cut, and feed rate cutting parameters were analyzed using Design Expert 13. Then, the cutting parameters affecting the temperature and power consumption variables were optimized. Based on the results of the optimization study, the following points were confirmed: Appl. Sci. 2022, 13, 3 19 of 23 • For the test result predictions made with the Box-Behnken experimental design, we 2 2 obtained values of R (adjusted) = 0.8647 for the temperature and R (adjusted) = 0.8555 for the power consumption. These results indicated that the cutting parameters would be accurately predicted when turning the AISI 52100 bearing steel; • The temperature and power consumption variables are affected by the cutting speed, depth of cut, and feed rate parameters; • The feed rate parameter affects both the temperature and power consumption the most; • The cutting speed parameter has the least effect on both the temperature and power consumption variables; • The cutting speed should be 162.427 m/min, the depth of cut should be 1.395 (mm), and the feed rate should be 0.247 mm/rev for minimal temperature and power consumption; • When the results obtained from the RSM were compared in the turning experiments carried out in the real experimental environment, error rates of 3.3% for the temperature and 6.6% for the power consumption emerged. According to these results, it was seen that the quadratic regression equation gave results closer to the real experimental results; • Since the minimum temperature and power consumption values were obtained with FEM analysis, the Box-Behnken design and real experimental setup are very close to each other. The Box-Behnken experimental design can be used together with an FEM analysis for the optimization of the cutting parameters of AISI 52100 bearing steel. When the literature on the optimization of the cutting parameters of AISI 52100 bearing steel was examined, no study was found that used the RSM and that simultaneously examined the output responses, temperature, and power consumption, as was examined in our study. In [69], in which the CCD and TOPSIS methods were used to determine the parameters that optimize the workpiece surface temperature, it was determined that the parameter that most affected the workpiece surface temperature was the negative rake angle. In another study [70], it was determined that the parameters that affect the cutting temperature the most are the cutting speed and feed rate with the CCD experimental design. In this study, experiments were carried out in a multilayer coated carbide cutting tool insert under a high-velocity pulsing jet minimal cutting fluid application environment. However, in our study, it was determined that the feed rate parameter affected the temperature the most under dry cutting conditions and with a different insert, and the cutting speed variable affected the temperature the least. It was thought that this result was due to the fact that the experiments were carried out in a dry-liquid environment and that the cutting tips were different. In another study [65], which aimed to optimize the cutting forces and used the MLR and genetic algorithm methods, it was seen that the feed rate parameter affected the power consumption the most. This result was similar to the result obtained in our study. When we look at the other cutting parameter optimization studies on AISI 52100 bearing steel in the literature, it can be seen that feed rate generally affects the surface roughness and the depth of cut affects the cutting forces. The first limitation of our study was that only three parameters and three levels were used in the experimental design. Additionally, the second limitation was that the tests were performed with only one type of insert. In future studies, it is thought that these limitations will be eliminated by increasing the parameters and using at least two types of inserts. In addition to carrying out the activities to eliminate the limitations mentioned above, we aim to use machine learning techniques in the optimization of the cutting parameters by conducting more experiments. Appl. Sci. 2022, 13, 3 20 of 23 Author Contributions: Conceptualization, A.Y., L.U., and İ.E.P.; methodology, A.Y., L.U., and İ.E.P.; writing—original draft preparation, A.Y. and İ.E.P.; writing—review and editing, A.Y. and İ.E.P. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. References 1. Maity, S.R.; Chatterjee, P.; Chakraborty, S. Cutting tool material selection using grey complex proportional assessment method. Mater. Des. 2012, 36, 372–378. https://doi.org/10.1016/j.matdes.2011.11.044. 2. Debnath, S.; Reddy, M.M.; Yi, Q.S. Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method. Measurement 2016, 78, 111–119. https://doi.org/10.1016/j.measurement.2015.09.011. 3. Rao, C.; Rao, D.N.; Srihari, P. 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Journal
Applied Sciences – Multidisciplinary Digital Publishing Institute
Published: Dec 20, 2022
Keywords: AISI 52100; response surface method; cutting parameters; FEM analysis