Study on Multi-Objective Optimization of the Turning Process of EN 10503 Steel by Combination of Taguchi Method and Moora Technique

In this study, the multi-objective optimization problem of turning process was successfully solved by a Taguchi combination method and MOORA techniques. In external turning process of EN 10503 steel, surface grinding process, the orthogonal Taguchi L9 matrix was selected to design the experimental matrix with four input parameters namely insert nose radius, cutting velocity, feed rate, and depth of cut. The parameters that were chosen as the evaluation criteria of the machining process were the surface roughness (Ra), the cutting force amplitudes in X, Y, Z directions, and the material removal rate (MRR). Using Taguchi method and MOORA technique, the optimized results of the cutting parameters were determined to obtain the minimum values of surface roughness and cutting force amplitudes in X, Y, Z directions, and maximum value of MRR. These optimal values of insert nose radius, cutting velocity, feed rate, and cutting depth were 1.2 mm, 76.82 m/min, 0.194 mm/rev, and 0.15 mm, respectively. Corresponding to these optimal values of the input parameters, the surface roughness, cutting force amplitudes in X, Y, Z directions, and material removal rate were 0.675 µm, 124.969 N, 40.545 N, 164.206 N, and 38.130 mm3/s, respectively. The proposed method in this study can be applied to improve the quality and effectiveness of turning processes by improving the surface quality, reducing the cutting force amplitudes, and increasing the material removal rate. Finally, the research direction was also proposed in this study


Introduction
Turning is one of the most common machining processes in the cutting methods. The work volume that the lathes perform about 40 % of the total workload of the machining processes, and the number of lathes accounts about 25-35 % of the total number of machine tools in the cutting workshop [1].
Many studies were performed to improve the accuracy and productivity of machining processes [1][2][3][4][5][6][7][8][9][10][11][12]. In which, most studies focus on determining the optimal values of the cutting parameters to ensure the surface roughness with the smallest value, the force components with the smallest values, and the material removal rate with greatest value.
The response surface method (RSM) was applied to optimize the turning process of AISI 410 [2], turning process of Inconel 718 Nickel-base super alloy [3,4]. RSM and Genetic Algorithm (GA) were also combined to optimize the turning process of AISI 1040 [5], turning process of martensitic stainless steel [6], and turning process of EN8 steel [7].
Particle swarm optimization (PSO) algorithm was applied to optimize the turning process of AISI D2 [8]. The regression analysis method was used to optimize the turning process of PM nickel-based superalloy [9]. Weighting factor method and GA algorithm were applied to optimize the turning process of 52100 steel [10].
A combination method of Taguchi and Grey relational analysis (GRA) was used to optimize the turning process of DIN 1.2344 steel [28], turning the unidirectional glass fiber reinforced plastic (UD-GFRP) composite rods [29], turning the EN-8, EN-31 steel and EN-36 steel [30], turning the DIN Ck45 steel [31]. Taguchi was combined to TOPSIS and SAW method to optimize the turning process of Ti-6Al-4V alloy under minimum quantity lubrication (MQL) [32]. Taguchi was also combined to GA and PSO algorithm to optimize the turning process of S45C steel [33].
The summary of the reviewed literatures about the optimization of the turning processes including the materials, the aims, the methods, and the optimized results of each study as listed in Table 1. From the summary of the reviewed literatures in Table 1, it is clear that many methods and algorithms were applied in optimization of turning processes. However, with different machining material, the obtained values of cutting parameters were different. So, the optimization process should be performed with each specific material. Taguchi method has been successfully applied to optimize the turning processes with different cases. Besides, Taguchi was also successfully combined with one or two of algorithms (GRA, GA, TOPSIS, SAW, PSO, etc.) to optimize the turning processes. Table 1 Original Research Article: full paper (2021), «EUREKA: Physics and Engineering» Number 2

Continuation of
Engineering Up to date, it seems that the combination of Taguchi method and MOORA technique in optimization of the turning processes have not mentioned. Besides, in previous studies, the surface roughness or cutting forces or MRR or two parameters of them were chosen as the output parameters. It also seems a study that was performed in consideration of all five output parameters (Surface roughness, cutting force in X, Y, Z directions, and MRR) have not been mentioned. EN 10503 steel is a steel type widely used to manufacture the parts in the machine manufacturing. Because this steel has good machinability and low cost. The optimization of turning process of the EN 10503 steel with five above output parameters have been not mentioned and this is a necessary study.
The aim of this research is simultaneously determining the values of four parameters including the tool insert radius, cutting speed, feedrate, and depth of cut to ensure simultaneously output criteria including the minimum value of surface roughness, the minimum values of three cutting force components, and maximum value of MRR when turning the EN 10503 steel. To solve this problem, Taguchi method was applied to design the experimental matrix and MOORA technique was applied to solve the multi-objective optimization problem.

Multi-Objective Optimization using MOORA Technique 1. Multiple-Criteria Decision Making (MCDM)
The Multiple-Criteria Decision Making (MCDM) was used to choose the best solution from the set of solutions where d ij Î R + with i = 1, 2, …, m and j = 1, 2, …, n.
In the MOORA technique, the weights were calculated using measurement of Entropy, because this method can get the high accuracy. The steps of the weight calculation process will be performed as following [34,35]: Step (1) Step 2: Calculating the measurement entropy e j of each criterion C j with j = 1, 2, …, n by Eq. (2): Step 3: Calculating the weight w j of each criterion C j with j = 1, 2, …, n by Eq. (3): The above equations will be used to maximize the multi-objective optimzation in next part of this paper.

MOORA technique
MOORA technique was introduced the first time in 2004 by Brauers [36]. This multi-objective optimization technique can be successfully applied to solve the complex decision problems Engineering in the production environment with the together conflicting objectives. The MOORA technique includes the steps as following: Step 1: Calculating the values p ij with i = 1, 2, …, m and j = 1, 2, …, n using Eq. (1).
Step 2: Calculating the measurement entropy e j of each criterion C j with j = 1, 2, …, n by Eq. (2).
Step 3: Calculating the weight w j of each criterion C j with j = 1, 2, …, n by Eq. (3).
Step 8: Ranking the solutions A k > A i if Q k < Q i with i, k = 1, 2, …, m.

Material and Experimental Method 1. Material
In this study, EN 10503 was used in the external turning process. This is common steel and is often used to manufacture the parts in the machine manufacturing such as mechanical shafts, gears, mechanical levers, etc. The equivalent sign of EN 10503 steel according several standards is described in Table 2.
The specimen is analyzed for spectrum and its chemical composition is introduced in Table 3.

Engineering
The properties of EN 10503 steel are listed in Table 4. The length and diameter of workpiece were 300 mm and 27.5 mm, respectively, as shown in Fig. 1.

2. Experimental Machine and Cutter
The manual lathe (FEL-1440GMW, MAGNUM-CUT, Taiwan) was used to conduct the experiments. Three insert types (Lungaloy, Japan) with the nose radius of 0.4 mm, 0.6 mm, and 1.2 mm were used in the experimental process. The cutting inserts are coated with titanium.

Experimental Matrix
In this study, the Taguchi method was used to design the experimental matrix. Four input parameters were insert nose radius (r), cutting speed (n), feed rate ( f ), and depth of cut (a p ). Three selected values of the insert nose radius are those commonly used in turning processes. The values for cutting speed, feedrate, and depth of cut are chosen based on the cutting tool manufacturer's recommendation for turning steel in general and EN 10503 steel in particular and also based on the adjustment ability of these parameters of the experimental machine. These parameters were selected as the controllable factors, and their levels were presented in Table 5. The orthogonal array (L 9 ) with 9 experiments was selected to design the experimental matrix as listed in Table 6.  According to this experimental matrix form, there will be 9 experiments to be performed. At each experiment, the five input parameters will be changed simultaneously.

4. Measurement system and Calculation of MRR 4. 1. Surface roughness measurement system
The MITUTOYO-Surftest SJ-210 surface roughness tester was used to measure the surface roughness of the machined parts. The evaluation length was fixed at 0.8 mm (The standard length) as described in Fig. 2.

Fig. 2. Surface roughness measurement setup
The surface roughness was measured perpendicular to the cutting velocity direction and repeated three times following three repeated times of each cutting test. The average value of surface roughness of three measurement consecutive times was used for analysis and evaluation of surface roughness.

4. 2. Cutting force measurement system
Cutting forces in three directions (X, Y, and Z) were measured using a dynamometer (Kistler Type 9139AA: Force Ranges: (-3KN ÷ 3KN), a data processing box, and a PC with DynoWare software as described in Fig. 3.
The data-processing devices were connected to the computer and they processed the results of the measurement of the component forces by the dynamometer. The value of the forces at each experiment is the average during the machining operation.

4. Calculation of Material Removal Rate
The material removal rate (MRR) was calculated by Eq (9).
where n is cutting speed (rev/min); d is diameter of workpiece (mm); f is feed rate (mm/rev); a p is depth of cut (mm).

1. Experiment results
The experimental results were listed in Table 7. The experimental results in this table show that it is difficult to determine which of the experiment in 9 performed experiments have simultaneously the minimum value of surface roughness, minimum values of all three cutting force components, and the maximum of MRR. This is explained as follows: Engineering With the results in Table 7, for example, in the experiment 2, the surface roughness was the smallest value (equal to 0.605 µm), but in this experiment, the values of all three cutting force components were not the smallest values. Besides, MRR in this experiment was also not the maximum value. Another example is experiment 3, in this experiment, MRR was the largest value, but also in this experiment, the value of the cutting force components also were the maximum values. Besides, the surface roughness was not the smallest in this experiment.
From above analysis showed that, it is not possible to choose one experiment from 9 performed experiments to ensure simultaneously the minimum value of surface roughness, the minimum values of cutting force components, and the maximum value of MRR. So that, it is necessary to solve the multi-objective optimization problem to determine the experiment with small surface roughness, small cutting force components, and large MRR. This issue will be presented in next section.

2. Multi-Objective Optimization of Turning Process using MOORA Technique
To facilitate for the using of the mathematical symbols when optimizing according to MOORA techniques, the surface roughness, cutting force in X direction, cutting force in Y direction, cutting force in Z direction, and MRR criteria were set as C 1 , C 2 , C 3 , C 4 , and C 5 as presented in Table 8. Table 8 The evaluation criteria of the turning process From the data in Table 3, MOORA technique applied to calculate the values according to the following steps: Step 1: Using Eq. (1), the values p ij were calculated and listed in Table 9. Table 9 The values of p ij Step 2: Using Eq. (2), the values e j of each criterion C j were calculated and listed in Table 10.
Step 3: Using Eq. (3), the values w j of each criterion C j were calculated and listed in Table 10.  Step 4: Using Eq. (4), the standardized matrix X = [X ij ] m×n was calculated and listed in Table 11.
Step 5: Using Eq. (5), the decision matrix W after standardizing with the weight was calculated and listed in Table 12.
Step 6: Using Eq. (6) and Eq. (7), the values P i and Q i were calculated and listed in Table 13.
Step 7: Using Eq. (8), the values Q i were calculated and listed in Table 13.

Engineering
The calculated results from Table 13 showed that the solution A 9 was the best solution in 9 solutions because this is the solution having the smallest value of Q i . If considering only the surface roughness criterion or only the cutting force components or only MRR, A 9 is not the best solution (Table 7). However, when simultaneously considering five parameters including the surface roughness, three cutting force components, and MRR, the solution A 9 was the best solution. In this experiment, the surface roughness was smaller than that ones in Experiments 1, 4, and 8. The cutting force components in x and z directions both have very small values and these cutting force components are at position number 2 (these cutting force component values were only larger than that ones in experiment 1); the force component in Y direction also has very small value and it was ranked at position number 3 (this cutting force component value was only larger than that ones in experiment 1 and 5), in this experiment, MRR was ranked at position number 6 (this MRR value was smaller than ones in experiment 2, 3, 4, 5, and 7). So, these optimal values of insert nose radius, cutting velocity, feed rate, and cutting depth were 1.2 mm, 76.82 m/min, 0.194 mm/rev, and 0.15 mm, respectively. Using these optimal values of the input parameters, the surface roughness, cutting force amplitudes in X, Y, Z directions, and material removal rate were 0.675 µm, 124.969 N, 40.545 N, 164.206 N, and 38.130 mm 3 /s, respectively. The proposed method in this study can be applied to improve the quality and effectiveness of turning processes by improving the surface quality, reducing the cutting force amplitudes, and increasing the material removal rate.
In this study, only four input parameters are considered, have not considered the material and shape of the cutting tool (insert). Besides, other factors of the turning process affect the output parameters such as workpiece material, workpiece hardness, cooling lubrication conditions, etc. also have not considered in this study. These are issues that need to be done in the next research to evaluate the turning process in a more comprehensive way.

Conclusions
In this study, Taguchi method and MOORA technique were applied to solve the multi-objective optimization problem for external turning process of EN 10503 steel. The conclusions of this study were drawn as following: -Taguchi method and MOORA techniques were successfully used to solve the multi-objective optimization problem for external turning process of EN 10503 steel.
-These optimal values of the insert nose radius, cutting velocity, feed rate, and cutting depth were 1.2 mm, 76.82 m/min, 0.194 mm/rev, and 0.15 mm, respectively. Using these optimal values of the input parameters, the surface roughness, cutting force amplitudes in X, Y, Z directions, and material removal rate were 0.675 µm, 124.969 N, 40.545 N, 164.206 N, and 38.130 mm 3 /s, respectively.
-The proposed method in this study can be applied to improve the quality and effectiveness of turning processes by improving the surface quality, reducing the cutting force amplitudes, and increasing the material removal rate.