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ARTICLE
Year : 2012  |  Volume : 2  |  Issue : 2  |  Page : 75-81

Computer Numerical Control Turning on AISI410 with Single and Nano Multilayered Coated Carbide Tools under Dry Conditions


1 Department of Mechanical Engineering, Anna University of Technology Madurai, India
2 Department of Mechanical Engineering, Syed Ammal Engineering College, Ramanathapuram, India
3 Department of Mechanical Engineering, Anna University of Technology Madurai, Ramanathapuram Campus, Tamilnadu, India

Date of Web Publication4-Aug-2012

Correspondence Address:
Kamaraj Chandrasekaran
Department of Mechanical Engineering, Anna University of Technology Madurai
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0976-8580.99292

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   Abstract 

Martensitic stainless steels (AISI410) have excellent, such as, corrosion resistance, high strength, low thermal conductivity, and high ductility. On account of these properties, AISI410 is widely used in the manufacture of components in aerospace industries, turbine and compressor components, and nuclear applications. CNC turning of AISI410 is an important task in the manufacturing of components. Turning parameters such as cutting speed, feed rate, depth of the cut, and the cutting tool, play a major role in getting a good surface finish, while turning AISI410. In this article, carbide tools coated with multilayered TiCN+Al 2 O 3 , multilayered Ti (C, N, B), single layered (Ti, Al) N, and nano multilayered B-Tic are used for the turning study on AISI410, under dry conditions. Different cutting parameters, namely, cutting speed, feed rate, and depth of the cut are used for the optimal setting of the parameters on turning AISI410. Experiments were carried out using the Taguchi's L 27 orthogonal array. The effect of cutting parameters on surface roughness (SR) was evaluated and optimal setting conditions were determined for minimization of SR. Analysis of variance (ANOVA) was used for identifying the significant parameters affecting the response.

Keywords: Martensitic stainless steels, analysis of variance, computer numerical control turning, coated carbide tools, taguchi method


How to cite this article:
Chandrasekaran K, Marimuthu P, Raja K. Computer Numerical Control Turning on AISI410 with Single and Nano Multilayered Coated Carbide Tools under Dry Conditions. J Eng Technol 2012;2:75-81

How to cite this URL:
Chandrasekaran K, Marimuthu P, Raja K. Computer Numerical Control Turning on AISI410 with Single and Nano Multilayered Coated Carbide Tools under Dry Conditions. J Eng Technol [serial online] 2012 [cited 2019 Oct 17];2:75-81. Available from: http://www.onlinejet.net/text.asp?2012/2/2/75/99292


   1. Introduction Top


The goal of the modern industry is to manufacture high quality products in a short time. Computer Numerical Control (CNC) machines are capable of achieving high accuracy with very low processing time [1,2]. During machining, surface quality is one of the most specified customer requirements. Surface roughness (SR) is one of the main results of process parameters such as tool geometry and cutting [3]. AISI410, 420, and 440 are all considered as martensitic stainless steels and can be hardened like other alloy steels. AISI410 is widely used in aerospace industries in the manufacture of bearings, water valves, pumps, turbines, plastic moulds, nuclear applications, and so on, which demand high strength and high resistance to wear and corrosion [4]. On account of low thermal conductivity, high ductility, high strength, high fracture toughness, and rate of work hardening, machinability is poor in turning AISI410 [5]. Coated carbides are basically cemented carbide inserts, coated with one or more thinly layers of wear-resistant materials such as TiN, TiC, and Al 2 O 3 [6]. It is well known that coating can reduce tool wear and improve the SR [7,8]. Therefore, coated carbide tools are mostly used in the metal cutting industries, while coating brings about an extra cost [9]. Thamizhmani has investigated the AISI410 with the help of the PCBN cutting tool. He found that the SR value was low at a high cutting speed with a low feed rate [10]. Gutakorskis performed the turning test on AISI410 by using a nano cutting tool [11]. Lin conducted a research to evaluate the behavior of the stainless steel in high speed turning [12]. Only a limited number of research articles are available on the turning of AISI410. Various compositions of cutting tools were used by past researchers for turning. However, comparisons of single layered, multilayered, and nano multilayered cutting tools for turning AISI410 were not carried out by them.

To produce a quality product, manufacturing engineers are employing off-line techniques apart from online quality control (QC) techniques. The QC activities at the manufacturing stage are the online QC methods. The QC methods conducting at the design stage are off-line QC methods. Considerable advantages can be obtained by achieving product quality at the initial stage instead of controlling the quality at the manufacturing stage [13]. The Taguchi method of off-line QC includes all stages of product development. However, the key element for achieving high quality and low cost is parameter design. Through parameter design, optimal levels of machining parameters can be determined [14]. Two factors that affect the product functional characteristics are control factors and noise factors. Control factors are easily controlled. Noise factors are nuisance variables, which are difficult, impossible, or expensive to control [15]. The Taguchi method is recommended for solutions in metal cutting problems, to optimize the machining parameters [16]. Hence, in this study, the Taguchi technique is used to determine the optimum machining parameters for different cases. Analysis of variance (ANOVA) has been performed to analyze the effect of these machining parameters on SR.


   2. Description of the Experiment Top


The main objective of the study is to establish a relation between cutting speed, feed, and depth of the cut on the SR. The AISI410 material is taken as the workpiece material for all trials, with a diameter of 32 mm and machined length of 60 mm. The chemical composition of the workpiece is given in [Table 1]. The workpiece material's qualities are confirmed to grade AISI410. The experiments are conducted in a Fanuc CNC lathe. The workpiece positioning accuracy is ±0.010, and the run out is tested by a dial gauge heavy duty magnetic base. The range of cutting parameters is selected based on the tool manufactures handbook. In this investigation, four types of cutting inserts are taken for the turning process. Coated cutting tool geometry, coating composition, layered thickness, coating methods, and tool holders are shown in [Table 2]. The electron gun vacuum physical vapor deposition machine was used for B-Tic coating on an uncoated carbide tool. An Ultimate Vacuum of a 1×10−6 m bar can be achieved in a clean, cold, empty degassed chamber, after backfilling the chamber with pure and dry nitrogen. The Electron Beam Gun is 4 Source, 8 KW 270-Degree Bent Beam Gun with 10 KW Power supply. Operating pressure is below 5×10−4 m in the bar. SR is measured for both cases by the SURF TEST 211 and it is denoted by Ra. Measurements are taken by the proper setting of the workpieces and instrument. In the present study, three parameters each, set at three levels, are chosen for experimentation. The turning parameters and the levels chosen for all cases are presented in [Table 3]. In order to have a complete study of the turning process, the ranges of parameters are selected, and appropriate planning of experimentation is essential to reduce the cost and time consumption. Hence, an experimental plan based on Taguchi's L 27 orthogonal array has been selected.
Table 1: Chemical composition of AISI410

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Table 2: Tool geometry

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Table 3: Machining parameters and levels

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There are three categories of quality characteristics in the analysis of the S / N ratio, such as, the-lower-the-better, the-higher-the-better, and the-nominal-the-better. As the quality characteristic is to be minimized, the-lower-the-better category is used to calculate the S/N ratio for SR. Equation (1) shows the smaller-the-better characteristic.



Where η is the signal-to-noise ratio, n is the number of repetitions of the experiment and y is the measured value of the quality characteristic. Minitab 14 statistical software has been used for the analysis of the experimental study. The software studies the experimental data and then provides the calculated results of signal-to-noise ratio. The effect of different process parameters on SR, for type I, is given in [Table 4], type II in [Table 5], type III in [Table 6], and type IV in [Table 7]. The main effect plot for S/N ratio for SR for type I is shown in [Figure 1], type II in [Figure 2], type III in [Figure 3], and type IV in [Figure 4]. From these figures it is seen that the process parameters change from one level to another. The average value of the S/N ratio has been calculated to find out the effects of different parameters and their levels. In addition, a statistical ANOVA is performed to see those process parameters that significantly affect the responses. The experimental results are analyzed with ANOVA, which is used for identifying the factors that significantly affect the performance measures. The results of the ANOVA for the SR of all types are shown in [Table 8], [Table 9], [Table 10] and [Table 11], respectively. This analysis is carried out for the significance level of α=0.05, that is, for a confidence level of 95%.
Table 4: Taguchi analysis SR for Type I versus V, F, D

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Table 5: Taguchi analysis SR for Type II versus V, F, D

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Table 6: Taguchi analysis SR for Type III versus V, F, D

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Table 7: Taguchi analysis SR for Type IV versus V, F, D

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Table 8: ANOVA for SR of Type I

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Table 9: ANOVA for SR of Type II

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Table 10: ANOVA for SR of Type III

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Table 11: ANOVA for SR of Type IV

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Figure 1: The main effect of the plot for S/N ratio for SR of Type I

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Figure 2: The main effect of plot for S/N ratio for SR of Type II

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Figure 3: The main effect of plot for S/N ratio for SR of Type III

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   3. Results and Discussions Top


3.1 Optimal setting of machining parameters for type I

The response table of the S/N ratio for SR of type I by Taguchi analysis has been shown in [Table 4]. It shows the rank value for machining parameters and it is stated that feed has the strongest influence on SR, followed by depth of cut, and cutting speed for type I. From [Table 4], the S/N ratio at each level of the machining parameters is clear and also how it changes when the setting of each machining parameter is changed from one level to another can be seen. The main effect plot for SR of type I is shown in [Figure 1]. In the plots, the x-axis indicates the value of each machining parameter at the three levels and the y-axis indicates the response value. The horizontal line indicates the mean value of the response. The main effect plots are used to determine the optimal design conditions, to obtain the optimum SR. The graph shows that for reducing the level of SR, the feed must be set to its lowest level (0.1 mm/rev), depth of cut to its low level (0.7 mm), and cutting speed to its middle level (160 m/minute). [Table 8] shows the results of ANOVA for SR of case I. It is observed that the feed (P value=0.00) is the most significant parameter, followed by the depth of cut (P value=0.00), while the effect of cutting speed has not been found to be statistically significant ( P value=0.357). A larger F-value shows the greater impact on the machining performance characteristics. Larger F-values are observed for feed and depth of cut.

3.2 Optimal setting of machining parameters for type II

The response table for SR of type II is given in [Table 5]. According to the response table, the rank value represents that feed has the strongest influence of SR followed by cutting speed and depth of cut. The main effect plot for SR of type II has been shown in [Figure 2]. The graph showed that for reducing the level of SR, the feed should be set to its lowest level (0.1 mm/rev), cutting speed to its middle level (160 m/min), and depth of cut to its low level (0.7 mm). [Table 9] shows the results of ANOVA for SR of type II. It is observed that the feed (P value=0.00) is the most significant parameter, followed by cutting speed (P value=0.353), while the effect of depth of cut has not been found to be statistically significant (P value=0.946). Larger F-values are observed for feed and cutting speed.

3.3 Optimal setting of machining parameters for type III

The response table for SR of type III is given in [Table 6]. According to the response table, the rank value represents that the feed has the strongest influence of SR followed by cutting speed and depth of cut. The main effect plot for SR of type III is shown in [Figure 3]. The graph shows that for reducing the level of SR, the feed should be set to its lowest level (0.1 mm/rev), cutting speed to its low level (110 m/minute), and depth of cut to its low level (0.7 mm). [Table 10] shows the results of ANOVA for SR of type III. It is observed that the feed ( P value=0.00) is the most significant parameter, followed by cutting speed (P value=0.018), while the effect of depth of cut has not been found statistically significant (P value=0.746). Larger F-values are observed for feed and cutting speed.

3.4 Optimal setting of machining parameters for type IV

Similarly, the response table for SR of type IV is given in [Table 7]. According to the response table, the rank value represents that the feed has the strongest influence on SR, followed by depth of cut, and cutting speed. The main effect plot for the SR of type IV is shown in [Figure 4]. The graph showed that for reducing the level of SR, the feed should be set to its lowest level (0.1 mm/ rev), cutting speed to its low level (110 m/ minute) and depth of cut to its low level (0.7 mm). [Table 11] shows the results of ANOVA for SR of type IV. It is observed that the feed (P value=0.00) is the most significant parameter followed by depth of cut.
Figure 4: The main effect of plot for S/N ratio for SR of Type IV

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3.5 Performances for all types

Comparisons of TiCN+Al 2 O 3 , Ti(C, N, B), (Ti, Al) N, and B-Tic in CNC turning AISI410 are presented in [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12] and [Figure 13], at various cutting ranges. [Figure 5], [Figure 6] and [Figure 7] show the SR for variation with cutting speed at a feed rate of 0.1 mm/rev and depth of cut 0.7, 1.4, and 2.1 mm, respectively. It clearly shows the best result is obtained from the Ti (C, N, B) rather than TiCN+Al 2 O 3 , (Ti, Al) N and B-Tic. SR is decreased for B-Tic and (Ti, Al) N, at low cutting speed.
Figure 5: Cutting speed versus SR - feed rate of 0.1 and DOC 0.7 mm

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Figure 6: Cutting speed versus SR - feed rate of 0.1 and DOC 1.4 mm

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Figure 7: Cutting speed versus SR - feed rate of 0.1 and DOC 2.1 mm

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Figure 8: Cutting speed versus SR - feed rate of 0.2 and DOC 0.7 mm

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Figure 9: Cutting speed versus SR - feed rate of 0.2 and DOC 1.4 mm

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Figure 10: Cutting speed versus SR - feed rate of 0.2 and DOC 2.1 mm

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Figure 11: Cutting speed versus SR - feed rate of 0.3 and DOC 0.7 mm

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Figure 12: Cutting speed versus SR - feed rate of 0.3 and DOC 1.4 mm

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Figure 13: Cutting speed versus SR - feed rate of 0.3 and DOC 2.1 mm

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[Figure 8], [Figure 9] and [Figure 10] show the SR for variation with cutting speed at a feed rate of 0.2 mm/rev and depth of cut 0.7, 1.4, and 2.1 mm, respectively. It clearly shows that the best result is obtained from the B-Tic rather than (Ti, Al) N, Ti(C, N, B), or TiCN+Al 2 O 3 . SR is decreased for TiCN+Al 2 O 3 and (Ti, Al) N at high cutting speed

[Figure 11], [Figure 12] and [Figure 13] show the SR for variation with cutting speed at a feed rate of 0.3 mm/rev and depth of cut 0.7, 1.4, and 2.1 mm, respectively. It clearly shows that the best result obtained is from the B-Tic rather than (Ti, Al) N, Ti(C, N, B), or TiCN+Al 2 O 3 . SR is decreased for TiCN+Al 2 O 3 and Ti(C, N, B) at high cutting speed.

From the analysis of the above figures, multilayered B-Tic gives the minimum surface roughness rather than TiCN+Al 2 O 3 , (C, N, B) and (Ti, Al) N.


   4. Conclusions Top


The current investigation is focused on the optimization and analysis of CNC turning AISI410 during change of cutting parameters, for different cases. From the study, the following results can be concluded:

  1. Optimum parameter setting for minimization of SR is obtained at a cutting speed of 160 m/minute, feed rate 0.1 mm/rev, and depth of cut 0.7 mm, that is, v 2 f 1 d 1 , for TiCN+Al 2 O 3 is used.
  2. Optimum parameter setting for minimization of SR is obtained at a cutting speed of 160 m/minute, feed rate 0.1 mm/rev, and depth of cut 0.7 mm, that is, v 2 f 1 d 1 , for Ti (C, N, B) is used.
  3. Optimum parameter setting for minimization of SR is obtained at a cutting speed of 110 m/minute, feed rate 0.1 mm/rev, and depth of cut 0.7 mm, that is, v 2 f 1 d 1 , for (Ti, Al) N is used.
  4. Optimum parameter setting for minimization of SR is obtained at a cutting speed of 110 m/minute, feed rate 0.1 mm/rev, and depth of cut 0.7 mm, that is, v 2 f 1 d 1ss , for B-Tic is used.
  5. From the results of ANOVA, the feed rate and cutting speed are the significant cutting parameters affecting the SR with Ti (C, N, B), (Ti, Al) N, and B-Tic.
  6. From the results of ANOVA, the feed rate and depth of cut are the significant cutting parameters affecting the SR with TiCN+Al 2 O 3 .
  7. From the analysis of figures, a minimum SR value was obtained using multilayered B-Tic carbide tools rather than TiCN+Al 2 O 3 , (C, N, B) and (Ti, Al) N. Also this minimum surface roughness value is lower than that of Gutakorskis [11].


 
   References Top

1.N. R. Abburi, and U. S. Dixit, "Knowledge-based system for the prediction of surface roughness in turning process", Robotics and Computer-Integrated Manufacturing, Vol. 22, pp. 363-72, 2006.  Back to cited text no. 1
    
2.M. Nalbant, H. Goekkaya, and G. Sur, "Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning", Materials and Design, Vol. 28, pp. 1379-85, 2007.  Back to cited text no. 2
    
3.T. Oezel, and Y. Karpat, "Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks", International Journal of Machine Tools and Manufacture, Vol. 45, pp. 467-79, 2005.  Back to cited text no. 3
    
4.C. T. Kwok, K. H. Lo, F. T. Cheng, and H. C. Man, "Effects of processing condition on the corrosion performance of laser surface melted AISI 440 C martensitic stainless steel", Surface and Coatings Technology, Vol. 166, pp. 84-90, 2003.  Back to cited text no. 4
    
5.A. Attanasion, M. Gelfi, C. Giardini, and C. Remino, "Minimum quantity lubrication in turning: Effect on tool wear", Wear, Vol. 260, pp. 333-8, 2006.  Back to cited text no. 5
    
6.M. Sarwar, X. Zhang, and D. Gillibrand, "Performance of titanium nitride coated carbide tipped circular saws when cutting stainless steel and mild steel", Surface and Coatings Technology, Vol. 94, pp. 617-21, 1997.  Back to cited text no. 6
    
7.E. P. DeGarmo, J. T. Black, and R. A. Kohser, Materials and Processes in Manufacturing, Prentice-Hall, Inc., New Jersey, 1997.  Back to cited text no. 7
    
8.C. Y. H. Lim, S. C. Lim, and K. S. Lee, "The performance of TiN-coated high speed steel tool inserts in turning", Tribology International, Vol. 32, pp. 393-8, 1999.  Back to cited text no. 8
    
9.E. O. Ezugwu, and C. I. Okeke, "Tool life and wear mechanisms of TiN coated tools in an intermittent cutting operation", Journal of Materials Processing Technology, Vol. 116, pp. 10-15, 2001.  Back to cited text no. 9
    
10.S. Thamizhmanii, and S. Hasan, "Machinability of hard martensitic stainless steel and hard alloy steel by CBN and PCBN tools by turning process proceedings of the World Congress on Engineering", Vol. 1, pp. 1, 2011.  Back to cited text no. 10
    
11.V. Gutakovskis, G. Bunga, and T. Torims, "Stainless steel machining with nano coated duratomictm cutting tool", 7 th International DAAAM Baltic Conference, pp. 22-24, 2010.  Back to cited text no. 11
    
12.W. S. Lin, "The study of high speed fine turning of austenitic stainless steel", Journal of Achievements in Materials and Manufacturing Engineering, Vol. 27, pp. 2, 2008.  Back to cited text no. 12
    
13.J. Ross Philip, Taguchi techniques for quality engineering, McGraw-Hill Book Company, New York, 1996.  Back to cited text no. 13
    
14.G. S. Peace, Taguchi methods: A hand book- on approach, Addison-Wesley, New York, 1993.  Back to cited text no. 14
    
15.A. E. Diniz, and A. J. Oliveira, "Optimizing the use of Dry Cutting in Rough Turning Steel Operations", International Journal of Machine Tools and Manufacture, Vol. 44, pp. 1061, 2004.  Back to cited text no. 15
    
16.J. A. Ghani, I. A. Choudhury, and H. H. Hasan, "Application of Taguchi method in the optimizations of end milling operations", Journal of Materials Processing Technology, Vol. 145, pp. 84-92, 2004.  Back to cited text no. 16
    

 
   Authors Top


Mr. K. Chandrasekaran, B.E., M.E., (PhD), MISTE, obtained his Bachelor degree in Mechanical Engineering and Master Degree in Manufacturing Engineering from Anna University Chennai and Anna University of Technology Tiruchirappalli, India. He is doing PhD in the area of Machining science from Anna University of Technology Madurai.




Dr. P. Marimuthu, B.E., M.E., PhD, MISTE, obtained his Bachelor degree in Mechanical Engineering and Master Degree in Production Engineering from Thiagarajar College of Engineering, Madurai. He obtained his PhD in the area of Machining of Metal Matrix Composite from Anna University, Chennai. He had served in many Institutions at various positions as Lecturer, Assistant Professor, Professor, Head of the Department and Principal. Now he is working as a principal in Syed ammal Engineering College, Ramanathapuram. He had published more number of papers in refereed International Journals and Conferences. Presently he is board of study member for PG course at Anna University of Technology, Madurai. He is also Life member in Indian Society for Technical Education. He received "RASTRIYA VIDYA SARASWATI PURASKAR AWARD" during March 2010 for his achievement and remarkable role in the field of education, honored by the International Institute of Education and Management, New Delhi. His research areas include Manufacturing, Composites, Machining Science, Modeling and optimization. He is guiding PhD scholars in different areas. He is active Doctoral committee member for PhD scholars, Question paper setter and Examiner for various autonomous Institutions and Universities.
Dr. K. Raja did his B.E in Mechanical Engineering from Government Engineering College, Salem, M.E. in Industrial Engineering from PSG Technology., Coimbatore and Ph.D from National Institute of Technology, Tiruchirappalli. Presently he is working as Assistant Professor, Anna University of Technology Maduari, Ramanathapuram Campus. His areas of Interest includes Fuzzy logic, Operating Research, Manufacturing Technology, Optimization Techniques and Engineering Economics.


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12], [Figure 13]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10], [Table 11]



 

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