Optimization of Surface Roughness in CNC Turning of Mild Steel (1018) using Taguchi method

Optimization of Surface Roughness in CNC Turning of Mild Steel (1018) using Taguchi method

Prof. Sushil Kumar Sharma Er. Sandeep Kumar

Yamuna Institute of Engg. And Technology, Gadholi, Yamunanagar

Abstract

Mild steel 1018 has a wide variety of applications in electrical devices,, construction of pipelines, products, construction as structural steel, car manufacturing industry, railway parts and other major industries. There are a number of parameters like cutting speed, feed and depth of cut etc. which must be given consideration during the machining of this alloy. So it becomes necessary to find out the ways by which it can be machined easily and economically. For the present work the parameter to be optimised selected is surface roughness that is optimised by using selected combination of machining parameters by using taguchi orthogonal design.

1. Introduction

   For engineering applications a no of materials are in use Low carbon steel is one of among all the materials, also known as mild steel, containig 0.05 % to 0.26 % of carbon (e.g. AISI 1018, AISI 1020 steel). They cannot be modified by heat treatment. They are cheap, but engineering applications are restricted to non-critical components and general panelling and fabrication work. These steels cannot be effectively heat treated. Consequently, there are usually no problems associated with heat affected zones in welding process. The surface properties can be enhanced by carburizing and then heat treating the carbon-rich surface. Therefore the present work is focused on finding the optimal parameters combination of cutting speed, feed and depth of cut for lower surface roughness.

2. Experimental Setup

   1. Workpiece preparation

The specification of workpiece used is Mild Steel 1018 having diameter 25 mm and of 415 length.

Figure 1: Workpieces

1. Chemical Composition

   The chemical composition of the selected material is as:

   Table 1: Chemical composition

   Element	Percentage
   Iron, Fe	98.81 – 99.26%
   Carbon, C	0.26%
   Manganese, Mn	0.6 – 0.9%
   Phosphorus (P)	0.04% max
   Sulfur (S)	0.05% max

2. CNC Machine

CNC lathe machines today is used at very huge range over a vast applications in industries. Better machines are with broad bearing surfaces (slides or ways) for stability, and manufactured with great precision helps to ensure the components to meet the required

tolerances and repeatability. For present work the machine used is HMT Stallion 100 HS.

2.

Figure 2: CNC Machine

4 Tool Material

The coated carbide tool single point cutting tool is used of make SANDVIK. This selection of tool bit depends on many factors like workpiece hardness and tool life required and the operating conditions etc.

1. Selection of Cutting parameters

   The selection of the cutting parameters and design array needs very much attention in any experimental research work. The cutting parameters selected are as:

   1. Cutting speed

   2. Feed

   3. Depth of cut

1. Selection of Optimization parameter

   The parameter selected to be optimized is Surface Roughness.

2. Roughness measurement

Roughness is measured using a portable stylus-type surface roughness tester, Surftest SJ-301 (Mitutoyo, Japan). The measurement results are displayed digitally and graphically on the touch panel and output to the built in printer. The Mitutoyo instrument (Surftest SJ-

301) is a portable, self-contained instrument for the measurement of surface texture. It is equipped with a diamond stylus having a tip radius 5 m. The measuring stroke always starts from the extreme outward position. At the end of the measurement the pickup returns to the position ready for the next

measurement.

Figure 3: Surface Roughness Tester

Two readings are taken first then average of these two values are taken for optimization.

1. Design of Experiment

   The following are the most common DOE techniques given below:

   1. One Factor Designs

   2. Factorial Designs

   3. Taguchis Orthogonal Arrays

   4. Response Surface Method Designs

   5. Mixture Designs

   From all these the selected one is Taguchis orthogonal arrays.

   3.1 Taguchis orthogonal arrays

   Taguchis orthogonal arrays are highly fractional designs, used to estimate main effects using only few experimental runs. Designs are also available to investigate main effects for certain mixed level experiments where the factors included do not have the same number of levels. For example, a four-level full factorial design with five factors requires 1024 runs while the Taguchi orthogonal array reduces the required number of runs to 16 only.

2. Results

   1. Optimization of surface roughness using Taguchi method

      The observed data for surface roughness (Ra) has been

      Variable Factors	DF	SS	V	F	P	% Cont.
      Cutting speed	2	518.63	259.315	32.41	0	21.52***
      Feed rate	2	143.82	71.91	8.98	0.002	5.968
      Cutting speed*Feed	4	477.36	119.34	14.91	0.009	19.810**
      Depth of cut	2	312.54	156.27	19.53	1.07	12.978
      Cutting speed*DOC	4	189.75	47.437	5.929	0.754	7.874
      Feed rate*DOC	4	417.87	104.467	13.05	0.004	17.347
      Error	8	349.72	43.715			14.513*
      Total	26	2409.69				100

      analysed using the Taguchi optimization method and Analysis of Variance with the help of MINITAB 16 software. The Signal to noise ratio has been calculated based on Taguchis smaller the better approach as it aims to minimise the surface roughness by using the following relation:

      Table 3: ANOVA of S/N ratios of surface roughness

      =

      = -10/ /

      0.4

      S. No	CS (mm/min)	FR (mm/rev)	DOC (mm)	SR (µm)	S/N Ratio(dB)
      1	60	0.25	0.2	5.60	-14.96
      2	60	0.25	0.3	7.10	-17.02
      3	60	0.25	7.40	-17.38
      4	60	0.35	0.2	7.10	-17.02
      5	60	0.35	0.3	6.03	-15.60
      6	60	0.35	0.4	6.98	-16.87
      7	60	0.45	0.2	4.85	-13.71
      8	60	0.45	0.3	5.55	-14.88
      9	60	0.45	0.4	6.31	-16.00
      10	80	0.25	0.2	4.23	-12.52
      11	80	0.25	0.3	4.44	-12.94
      12	80	0.25	0.4	5.14	-14.21
      13	80	0.35	0.2	3.84	-11.68
      14	80	0.35	0.3	5.57	-14.91
      15	80	0.35	0.4	5.73	-15.16
      16	80	0.45	0.2	4.06	-12.17
      17	80	0.45	0.3	4.85	-13.71
      18	80	0.45	0.4	6.28	-15.95
      19	100	0.25	0.2	4.12	-12.29
      20	100	0.25	0.3	3.57	-11.05
      21	100	0.25	0.4	3.30	-10.37
      22	100	0.35	0.2	3.41	-10.65
      23	100	0.35	0.3	3.12	-09.88
      24	100	0.35	0.4	3.42	-10.68
      25	100	0.45	0.2	2.63	-08.39
      26	100	0.45	0.3	4.33	-12.72
      27	100	0.45	0.4	4.10	-12.25

      Table 2: Surface Roughness mean and S/N ratio

      Table 4: Response Table of S/N ratios of surface roughness

      Level	Cutting Speed (A)	Feed Rate (B)	Depth of cut (C)
      1	6.92	4.99	4.56
      2	4.98	5.0	4.96
      3	3.21	4.91	5.49
      Rank	4	1	3

      Figure 4: Residual Plot

      Figure 5: Main Effect Plot for S/N ratio

      Figure 6: Interaction Plot for S/N Ratio

   2. Determination of optimum condition

      Both the response and S/N ratio are used to derive the optimum conditions. Since for quality characteristic, surface roughness smaller the better approach is desirable, the smallest response is the ideal level for a parameter. The S/N ratio is always highest at the optimum condition. The graph of Figure (b) is used to determine the optimum process parameters combination. The optimum combination is therefore A3B3C1.

   3. Predictive equation and verification

      The predicted value of SR at the optimum levels is calculated by using the relation :

      Where the total mean S/N ratio is is the mean S/N ratio at optimal level and o is the number of main design parameters that affect the quality characteristic.

      Applying this relation predicted value of SRR at the optimum conditions is obtained as:

      The robustness of this parameter optimization is verified experimentally. This requires the confirmation run at the predicted optimum conditions. The experiment is conducted at the predicted optimum conditions and the average of the response is 6.56 µm. The error in the predicted and experimental value is only 3.2%, so good agreement between the actual and the predicted results is observed. Since the percentage error is less than 5%, it confirms excellent reproducibility of the results.

      The results show that using the optimal parameter setting (A3B3C1) a smaller surface roughness is achieved.

      Table 5: Comparison of Results

      Vari able s	Optimal values of responses	Optim al setting Level	Predicted optimal value	Optimal value Of SR()	Experime Ntal Values
      Cutting speed(A)	100 mm/min	A3 B3 C1	6.56	6.23 <SR> 6.56	6.23
      Feed rate(B)	0.45 mm/rev s
      Depth of cut(C)	0.20 mm

3. Conclusions

This study demonstrates that, when feasible process parameters are selected, mild steel (1018) could be

=

= m +

(im-m)

efficiently turned using coated carbide tool. The coated

carbide tool with better mechanical and thermal properties is proved to be a better choice for turning mild steel. The feed rate shows an impetus effect on surface roughness. The analysis of surface roughness further confirmed such results. The research into the machining of mild steel is continuing in several fronts, including turning and burnishing processes. An experimental approach to the evaluation of surface roughness in turning medium brass alloy by coated carbide using Taguchi method is presented in this study.

1. Effect of workpiece material: It can be noted that mild steel (1018), is a difficult to machine with a hardness rate (107.5 – 172.5 HV), tensile strength (345

   – 580 MPa), and density (7861.093 kg/m3).In this condition, when the process conditions are right, is easier to turn.

2. Effect of tool material: The coated carbide turning tool has a high elastic modulus. This leads to the more efficient turning of work material as compared to the tool material.

3. Effect of cutting speed, feed rate and depth of cut: Surface roughness increases when feed rate increases, however cutting speed also the influencing parameter followed by depth of cut.

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