Experimental Investigation and Process Parameter Optimization on En353 with PCBN Inserts

Experimental Investigation and Process Parameter Optimization on En353 with PCBN Inserts

Manickavasaham G 1

P.G. Student,

Department of Mechanical Engineering, University College of engineering, BIT Campus, Tamil Nadu, India. 1

Sivakumar P 2,

Assistant Professor, Department of Mechanical Engineering,

University College of Engineering, BIT Campus, Tamil Nadu, India. 2

Dr Senthilkumar T 3,

Dean,

University College of Engineering, BIT Campus, Tamil Nadu, India. 3

Abstract – Machining of materials by super hard tool like PCBN is to reduce tool wear to obtain dimensional accuracy, smooth surface and more number of parts per cutting edge. Wear of tools inevitable due to rubbing action between work material and tool edge. However, the tool wear can be minimized by using super hard tools by enhancing the strength of the cutting inserts. Extensive study has been conducted in the past to optimize the process parameters in any machining process to have the best product. Current investigation on turning process is a Taguchi optimization technique applied on the most effective process parameters i.e. feed, cutting speed and depth of cut while machining the work piece with tool. The experiments were carried out by a CNC lathe, using PCBN tool for the machining of EN 353 steel. The Taguchi technique and ANOVA were used to obtain optimal Turning parameters in the Turning of SS420 under wet conditions. The optimal factor for Surface Roughness-A1(Speed – 1500)B2(Feed 0.04)C3(DOC 0.75), Machining Timing-A1(Speed- 1500)B2(Feed 0.04)C3(DOC 0.75), Material Removal Rate- A2(Speed-1750)B1(Feed 0.02)C3(DOC 0.75). The Percentage of contribution for each Process parameter is Surface Roughness- Speed 38.59%, Machining Timing – Speed 35.98%, Material Removal Rate- Feed 29.83%.

Keywords: Turning, EN 353 steel, PCBN inserts, Surface Roughness, MRR, and Machining time

1. INTRODUCTION

   Metal cutting is one of the vital processes and widely used manufacturing processes in engineering industries. Highly competitive market requires high quality products at minimum cost. Products are manufactured by the transformation of raw materials. Industries in which the cost of raw material is a big percentage of the cost of finished goods, higher productivity can be achieved through proper selection and use of the materials. To improve productivity with good quality of the machined parts is the main challenge of metal industry; there has been more concern about monitoring all aspects of the machining process. Surface finish is an important parameter in manufacturing engineering and it can influence the performance of mechanical parts and the production costs. The ratio of costs

   Dr Kumaragurubaran B4

   Assistant professor, Department of Mechanical Engineering,

   University College of Engineering, BIT Campus,

   Tamil Nadu, India.4

   and quality of products in each production phase has to be monitored and good corrective actions have to be taken in case of deviation from desired output. Surface roughness measurement presents an important task in many engineering applications. Many life attributes can be also determined by how well surface finish is maintained.

   Surface roughness is also a vital measure as it may influence frictional resistance, fatigue strength or creep life of machined components. As far as turned components are concerned, better surface finish (low surface roughness) is important as it can reduce or even completely eliminate the need of further machining. Many researchers have found that surface roughness has bearing on heat transmission, ability to hold lubricant, surface friction, wear etc. Despite the fact that surface roughness plays a very important role in the utility and life of a machined component due to its dependence on several process parameters and numerous uncontrollable factors machining process has no complete control over surface finish obtained. So the venture of controlling process parameters so as to produce best surface finish is an on-going process varying from various materials to tool combinations and the machining conditions. The present work is aimed at studying the influence of the three major process parameters in a turning operation namely, speed, feed and depth of cut and surface roughness for a predefined combination of material and tool under the given set of machining conditions.

2. RELATED WORK

   Literature is very rich in terms of turning operations owing to its importance in metal cutting. The three important process parameters in this research are speed, feed and depth of cut. Surface roughness of a turned work-piece is dependent on these process parameters and also on tool geometry. In addition, it is also depends on the several other exogenous factors such as: work piece and tool material combination and their mechanical properties, quality and type of the machine tool used.

   Sujan Debnath, Moola Mohan Reddy and Qua Sok Yi [1] studied the effect of various cutting fluid levels and cutting parameters on surface roughness and tool wear. R.K.Bharilya and Ritesh Malgaya [2] investigated the optimization of machining parameters for turning operation of given work piece, the material being carburized Mild steel (hard material), Aluminium alloys and Brass (soft material) which were machined on CNC machine and analysed through the cutting force dynamometer. V.Kryzhaniskyy and V.Bushlys [3] studied the cutting tool temperature that develops during rough turning of hardened cold-work tool steel is modeled on the basics of experimental data. Wojciech Zebala and Robert Kowalczyk [4] research the cutting forces (Ff, Fp, Fc) when machining of sintered carbides WC-Co (25% Co) with tools made of polycrystalline diamond PCD. Jinming Zho and Volodymyr Bushlya [5] analysis of subsurface microstructural alterations and residual stresses caused by machining significantly affect component lifetime and performance by influencing fatigue, creep, and stress corrosion cracking resistance. Rachid M Saoubi and Tobias Czotscher [6] focused on machinability of power metallurgy steel using PcBN inserts. Dipti Kanta Das and Ashok Kumar Sahoo [7] investigated on surface roughness during hard machining of EN 24 steel with the help of coated carbide insert. Harsh Y Valera and Sanket N Bhavsar [8] done an experimental study of power consumption and roughness characteristics of surface generated in turning operation of EN-31 alloy steel with TiN+Al203+TiCN coated tungsten carbide tool under different cutting parameters. S.A.Khan and S.L.Soo [9] done an experimental work on tool wear/life evaluation when finish turning Inconel 718 using PCBN tooling. Dr.C.J. Rao and Dr.D. Nageswara Rao [10] investigated the influence of speed, feed and depth of cut on cutting force and surface roughness while working with tool made of ceramic with an Al2O3+TiC matrix (KY1615) and the work material of AISI 1050steel (hardness of 484 HV). V.Bushlya and J.Zhou [11] studied the tool life, tool wear and surface integrity of superalloy Inconel 718 when machined with coated and uncoated PCBN tools, aiming on increased speed and efficiency. SU Honghua and LIU Peng [12] investigated the performance and wear mechanism of the tools (PCD and PCBN) for machining the TA15 alloy. J.Guddat and R.M Saoubi [13] investigating the effect of wiper PCBN inserts on surface integrity and cutting forces by hard turning of through hardened AISI 52100. Lin et al. [14] adopted an abdicative network to construct a prediction model for surface roughness and cutting force. Feng and Wang [15] investigated the influence on surface roughness in finish turningoperation by developing an empirical model through considering exogenous variables: work piece hardness, feed, cutting tool point angle, depth of cut, spindle speed, and cutting time. Suresh et al. [16] focused on machining mild steel by Tic coated tungsten carbide cutting tools for developing a surface roughness prediction model by using response surface methodology. Lee and Chen [17] have used ANN using sensing technique to monitor the effect of vibration produced by the motions of the cutting tool and work piece during the cutting process developed an on-line

   surface recognition system. Kirby et al. [18] developed the prediction model for surface roughness in turning operation.

   Ozel and Karpat [19] worked on the prediction of surface roughness and tool flank wear by utilizing the neural network model in comparison with regression model. Kohli and Dixit [20] proposed a neural network based methodology with the acceleration of the radial vibration of the tool holder as feedback. Pal and Chakraborty [21] studied on development of a back propagation neural network model for prediction of surface roughness in turning operation and used mild steel work piece with HSS as the cutting tool for performing a large number of experiments. Sing and Kumar [22] studied on optimization of feed force through setting of optimal value of process parameters namely speed, feed and depth of cut in turning of EN24 steel with TiC coated tungsten carbide inserts. Ahmed [23] developed the methodology required for obtaining optimal process parameters for prediction of surface roughness in A1 turning. Zhong et al. [24] predicated the surface roughness of turned surfaces using networks with seven inputs namely tool inserts grade, work piece material, tool nose radius, rake angle, depth of cut, spindle speed, feed rate.

3. RESEARCH METHODOLOGY

   The research is basically a hypotheses testing research making use of design of experiments based on Taguchi method. Hypotheses have been constituted for testing the main effect of the cutting parameters based on the literature review.

   1. Machine and the Material

      The turning operation was conducted using LMW Smarturn Industrial type CNC lathe machine with a range of spindle speed from 50 rpm to 3500 rpm, and a 10 KW motor drive. The cutting tool is PCBN insert, which is designated by KB5610. The material used was EN 353 steel (hardness of

      64 HRC). These bars (32mm in diameter and 75mm in length) were machined under wet condition. The work material bars were turned, centred and cleaned by removing a 1mm depth of cut from the outside surface, prior to the actual machining tests.

   2. Surface roughness measurement

      The instrument used to measure surface roughness was Qualitest TR200. For a probe movement of mm, surface roughness readings were recorded at three locations on the work piece and average value is used for analysis.

      • Ra Range: 0.01 40 m

      • Tracing Length Lt: (1 5 cut-off) + 2 cut-off

      • Detector: Diamond tip radius 5 m

   3. Cutting conditions and experimental procedure

   Among the speed, feed rate, and depth of cut combinations available on the lathe, three levels of cutting parameters were selected based on similar earlier studies (Table-1)

   Table-1: Factors and their Levels

   Factor	Level 1	Level 2	Level 3
   A: Speed (rpm)	1500	1750	2000
   B: Feed (mm/rev)	0.20	0.04	0.06
   C: Depth of Cut (mm)	0.25	0.50	0.75

   Taguchi design L-9 for three levels and three factors yielded 9 experiments were carried out. The experimental data is given in table-2.

   Table-2: Experimental data

   Sl no	Designation	Speed (rpm)	Feed (mm/rev)	Depth of Cut (mm)	Machining Time (sec)	Weight Before Machining (g)	Weight After Machining (g)	Material Removal Rate (g/sec)	Surface Roughness (microns)
   01	A1B1C1	1500	0.02	0.25	1.47	393	380	8.84	0.947
   02	A1 B2 C2	1500	0.04	0.50	1.36	392	380	8.82	0.452
   03	A1 B3 C3	1500	0.06	0.75	1.12	394	391	11.60	0.854
   04	A2 B1 C2	1750	0.02	0.50	1.34	392	391	8.20	0.194
   05	A2 B2 C3	1750	0.04	0.75	0.38	392	391	28.94	0.428
   06	A2 B3 C1	1750	0.06	0.25	1.23	392	391	8.94	0.656
   07	A3 B1 C3	2000	0.02	0.75	1.23	392	391	8.94	0.336
   08	A3 B2 C1	2000	0.04	0.25	0.47	393	391	25.53	0.376
   09	A3 B3 C2	2000	0.06	0.50	0.35	393	391	33.33	0.659

4. RESULT ANALYSIS

   1. Surface Roughness analysis

      The response table for Signal to Noise ratio results is very clear to support the optimum control factors A1, B2 and C3 (table 3). This can be seen in the main effect plot for SN ratio (figure 1). The analysis of variance (ANOVA) result gives the percentage contribution of process parameter for speed as 38.59% (table 4).

      Table-3: Response Table for Signal to Noise Ratios Smaller is better

      Level	Speed	Feed	DOC
      1	2.914	8.063	4.210
      2	8.426	7.588	8.254
      3	7.197	7.588	6.072
      Delta	5.512	5.178	4.044
      Rank	1	2	3

      Table-4: ANNOVA for Surface Roughness

      Source	DF	Seq SS	Adj SS	Adj SS	F	P	Percentage of contribution
      SPEED	2	0.19302	0.19302	0.09651	2.41	0.293	38.59
      FEED	2	0.15125	0.15125	0.07563	1.89	0.346	30.24
      DOC	2	0.07584	0.07584	0.03792	0.95	0.514	15.16
      Error	2	0.08007	0.08007	0.04003	–	–	16.01
      Total	8	0.50018	–	–	–	–	100
      S = 0.200083 R-Sq = 83.99% R-Sq(adj) = 35.97%

      Main Effects Plot for SN ratios

      Data Means

      Main Effects Plot for SN ratios

      Data Means

      9.0

      7.5

      6.0

      4.5

      3.0

      SPEED

      FEED

      9.0

      7.5

      6.0

      4.5

      3.0

      SPEED

      FEED

      0.25

      0.50

      0.75

      0.25

      0.50

      0.75

      Signal-to-noise: Smaller is better

      Signal-to-noise: Smaller is better

      1500

      1500

      1750

      DOC

      1750

      DOC

      2000

      2000

      0.02

      0.02

      0.04

      0.04

      0.06

      0.06

      9.0

      7.5

      6.0

      4.5

      3.0

      9.0

      7.5

      6.0

      4.5

      3.0

      Mean of SN ratios

      Mean of SN ratios

      Figure 1 Main Effects Plot for SN Ratios

   2. MRR Analysis

      The response table for Signal to Noise ratio results is very clear to support the optimum control factors A2, B1, and C3 (table 5). This can be seen in the main effect for SN ratio (figure 2). The analysis of Variance (ANOVA) result gives the percentage contribution of process parameter for Feed as 29.83% (table 6).

      Table-5: Response Table for Signal to Noise Ratios Larger is better

      Level	Speed	Feed	DOC
      1	19.17	18.74	22.03
      2	22.18	25.43	22.55
      3	25.87	23.59	23.18
      Delta	6.17	6.68
      Rank	2	1	3

      Table-6 ANOVA for MRR

      Source	DF	Seq SS	Adj SS	Adj SS	F	P	Percentage of Contribution
      SPEED	2	248.9	248.9	124.4	0.75	0.571	29.59
      FEED	2	251.0	251.0	125.5	0.76	0.569	29.83
      DOC	2	9.8	9.8	4.9	0.03	0.971	1.17
      Error	2	331.5	331.5	165.8			39.41
      Total	8	841.2					100
      S = 12.8753 R-Sq = 60.59% R-Sq(adj) = 0.00%

      Main Effects Plot for SN ratios

      Data Means

      Speed

      26

      24

      Mean of SN ratios

      Mean of SN ratios

      22

      20

      Feed

      1500

      1750

      2000

      0.02

      0.04

      0.06

      DOC

      DOC

      26

      24

      22

      20

      0.25 0.50 0.75

      Signal-to-noise: Larger is better

   3. Machining Time Analysis

   Figure 2 Main effect plots for SN ratio

   The response table for Signal to Noise ratio results is very clear to support the optimum control factors A1, B2 and C3 (table 7). This can be seen in the main effect plot for SN ratio (figure 3). The analysis of variance (ANOVA) result gives the percentage contribution of process parameter for speed as 35.98% (table 8).

   Table-7: Response Table for Signal to Noise Ratios Smaller is better

   Level	Speed	Feed	DOC
   1	-2.3338	-2.5622	0.4712
   2	1.3547	4.0972	1.3019
   3	4.6262	2.1121	1.8740
   Delta	6.9600	6.6594	1.4028
   Rank	1	2	3

   Table-8 ANOVA for Machining Time

   Source	DF	Seq SS	Adj SS	Adj SS	F	P	Percentage of Contribution
   SPEED	2	0.6022	0.6000	0.3011	1.37	0.422	35.98
   FEED	2	0.5983	0.5983	0.2991	1.36	0.433	35.75
   DOC	2	0.0345	0.0345	0.0172	0.08	0.927	2.06
   Error	2	0.4388	0.4388	0.2194			26.21
   Total	8	1.6738					100
   S = 0.468413 R-Sq = 73.78% R-Sq(adj) = 0.00%

   Main Effects Plot for SN ratios

   Data Means

   SPEED

   4

   2

   Mean of SN ratios

   Mean of SN ratios

   0

   -2

   FEED

   1500

   1750

   2000

   0.02

   0.04

   0.06

   DOC

   DOC

   4

   2

   0

   -2

   0.25

   0.50

   0.75

   Signal-to-noise: Smaller is better

5. CONCLUSION

Figure-3 Main effect plots for SN ratios

In this study, the Taugchi technique and ANOVA were used to obtain optimal Turning parameters in the Turning of EN 353 steel under wet conditions. The experimental results were evaluated using Taguchi technique. The following conclusion can be drawn.

1. Optimal Control Factor

   1. Surface Roughness

      A1 (Speed – 1500), B2 (Feed 0.04), C3 (DOC 0.75)

   2. Machining Timing

      A1 (Speed-1500), B2 (Feed 0.04), C3 (DOC 0.75)

   3. Material Removal Rate

   A2 (Speed-1750), B1 (Feed 0.02), C3 (DOC 0.75)

2. Percentage of Contribution of Process Parameter

1. Surface Roughness – Speed 38.59%

2. Machining Timing – Speed 35.98%

3. Material Removal Rate – Feed 29.83%

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