Optimizing cutting parameters in hard turning of AISI 52100 steel using topsis approach

In the present work optimization of cutting parameters is performed while hard turning of AISI 52100 steel with polycrystalline cubic boron nitride (PCBN) tools using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Experiments are planned and conducted based on Center Composite Rotatable Design (CCD) of the Response Surface Method (RSM). Cutting speed, feed, depth of cut, nose radius and negative rake angle are considered as input parameters. In this study machining force (F) and surface roughness (Ra) are measured during the experiment. Analysis of variance (ANOVA) is deployed to determine the influence of process parameters. Obtained optimal parameters are speed 200 rpm, feed 0.1 mm/rev, depth of cut 0.8 mm, nose radius 1.2 mm and negative rake angle 45o.


INTRODUCTION
Hard turning evolved as an improved machining process incontrast to grinding due to numerous merits such as process flexibility, economic, less setup time, complex parts fabrication and absence of coolant [1][2]. AISI 52100 steel was widely accepted material for abundant applications such as bearings, rollers, and dies etc and the turning process was inevitable for the aforementioned applications. Optimal process parameters selection was essential for higher-order machining performance, Multi criteria decision making methods (MCDM) were proved as tools in several manufacturing applications [3]. Among many TOPSIS method was adopted and gained acceptance for optimizing machining parameters [4].
Himadri Majumder and Abhijit Saha [5] optimized process parameters in turning of ASTM A588 mild steel using a hybrid optimization tool i.e. MOORA-PCA and TOPSIS-PCA approach. Tian [6] used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) for optimization of input parameters in CNC machining of S45C steel. Palanisamy and Senthil [7] carried out of process parameters optimization in turning of 15-5 PH stainless steel using Taguchi based Grey approach and TOPSIS. It is concluded that force and surface roughness are predominantly affected by feed rate.
Maheswararao and Venkata subbaiah [8] employed TOPSIS for optimization of process parameters in the CNC machining of AA7075. Results concluded that feed rate has a significant influence on responses. Sagar Bhise et al. [9] studied the effect of input parameters on surface roughness in hard turning of M42 austenitic stainless steel using CBN and carbide inserts by deploying PCR-TOPSIS. Maity and Khan [10] determined an optimal combination of process parameters during turning of commercially pure titanium (CP-Ti) grade 2 using the MCDM-based TOPSIS method.
Singaravel et al. [11] optimized machining parameters and nose radius in turning of EN25 steel by the application of combined MOORA and entropy measurement method. Singaravel et al. [12] determined optimum process parameters using the Additive Ratio Assessment (ARAS) method in turning of AISI 4340 steel. Optimization of process parameters is performed using various techniques like GRA-PCA [13][14][15], GA [16], ANN [17], TOPSIS [18][19]. Hence, the present work aimed to optimize process parameters for AISI 52100 steel hard turning using TOPSIS.

EXPERIMENTAL DETAILS
Machining details and experimental matrix with responses are shown in Table 1 and Table 2 respectively. The experimental setup is depicted in Figure 1. In the current study, Kirloskar Turn Master-35 type lathe was employed for conducting experiments in dry condition and AISI 52100 steel was deployed as a workpiece having a diameter of 48 mm and length of 500 mm. For this experimentation, five process variables are chosen such as Cutting Speed, Feed, Depth of cut, Nose radius, and Negative rake angles. PCBN tools with designation (CNMG 120404, CNMG 120406, CNMG 120408, CNMG 120410, CNMG 120412) manufactured by Zen Diamond Tools, Chennai, India are depicted in Figure 2.

TECHNIQUE FOR ORDER OF PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS)
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was developed by Hwang and Yoon based on the concept that the chosen parameter should have the shortest distance from the best solution and the longest distance from the worst solution [20]. Normalized and weighted normalised values are shown in Table 3. Positive ideal, Negative ideal solutions, separation measures, closeness coefficient values, and rank are given in Table 4.

Step 1
The normalized value (r ij ) is obtained using the equation (1).

Step 3
Then the positive ideal solution (S + ) and negative ideal solution (S -) calculated using equation (3),

Step 4
The separation of each alternative from positive ideal solution (S +) and negative ideal solution (S -) is found as per equation (4) and equation (5),

Step 5
The closeness coefficient value of each alternative (C i ) is calculated using equation (6),

RESULTS AND DISCUSSION
The higher the value of closeness coefficient indicates better performance. From Table 4, it is evident that the experiment number 7 having the highest value of closeness coefficient was the better performer amongst the 32 number of experiments.
Optimum closeness coefficients are observed (Shown in Fig.3.) at ν = 200 rpm, f = 0.1 mm/rev, d = 0.8 mm, r = 1.2 mm and α = 45º and similar observations are made from mean response table for closeness coefficient shown in Table 5 In the response table (Table 5) it has shown that a negative rake angle has been assigned a rank 1 which means it is the most significant parameter in controlling the response followed by feed, depth of cut, cutting speed and nose radius.
The Closeness coefficient for the obtained optimum combination of parameters was 1.463687 estimated from equation 7 and was 68.73% higher than the maximum Closeness coefficient corresponding to rank 1 in Table 4. Hence the values obtained are optimum.

CONCLUSIONS
Experiments were conducted as per CCD of RSM and optimized cutting parameters in AISI 52100 steel hard turning using TOPSIS. 1. The negative rake angle is the most significant parameter in controlling the response followed by feed, depth of cut, cutting speed and nose radius. 2. From the ANOVA negative rake angle (50.36%) has significant influence followed by feed (29.47%), Depth of cut (

Acknowledgements
Authors would like to thank Karunya Institute of Technology and sciences, Coimbatore for providing facilities.