Loading...

Optimization and Analysis of Surface Roughness in CNC Turning Inconel 718 using Taguchi’s and ANOVA analysis

Research Paper (postgraduate) 2015 8 Pages

Materials Science

Excerpt

Table of Contents

1 Introduction

2 Taguchi’s Method

3 Design Of Experiment Using Taguchi Technique
3.1 Selection of response factor
3.2 Selection of process variables
3.3 Process variable’s level
3.4 Experimental Design
3.5 Design Of Orthogonal Matrix

4 Expermental Details
4.1 Experimental unit
4.2 Properties of inconel718 [5]
4.3 Chemical composition [6]
4.4 Size of work piece
4.5 Cutting tool and tool holder
4.6 Surface roughness measuring

5 Conducting Trials

6 Regression Analysis

7 Optimum Surface Roughness Predicted Value Calculation

8 Anova Analysis

9 Results And Discussions

10 Confirmation Test

11 Conclusions And Future Scope
11.1 Conclusions of research work
11.2 Futural scope for research work

12 References

Abstract

The purport of this experimentation was to fixate on the analysis of optimum cutting conditions to get the minimum surface roughness in CNC turning of Inconel 718 alloy steel by Taguchi method. The nine experiments were designed by utilizing Minitab17 software. In this research work all the tribulations were conducted at a constant spindle speed (2800 RPM) and in a dry environment. The results were analyzed utilizing analysis of variance (ANOVA) method. Taguchi method has shown that cutting speed has a consequential role to play in engendering lower surface roughness followed by aliment rate. The vibrations of the implement wear, implement life are the other factors which may contribute poor surface roughness to the results and such factors ignored in analyses. The results obtained by this research work may be subsidiary for another researcher for further study for other replications such as implement vibration, implement wear, implement life, cutting forces etc.

Keywords: Dry turning, Minitab17, Surface roughness, Taguchi’s method, ANOVA.

1 Introduction

Turning of Nickel predicated super alloys is a challenging job. These alloys are very famous in the industry due to their more preponderant properties. They possess excellent properties such as oxidation resistance, high corrosion as well as resistance to thermal fatigue, thermal shock, creep and erosion. Among Nickel predicated alloys, Inconel718 is utilized as construction material in the aerospace industry for sultry sections of gas turbine engines. Due to great shear vigor, low thermal conductivity, proclivity to compose Built Up Edge (BUE), chemical reaction proclivity at high temperatures and high abrasive, carbide particles in the micro structure and work hardening propensity ergo alloy is under the category of tough to machine material. During turning process, the interaction between the Implement and work piece causes plastic deformation in the local areas of the work piece and excruciating friction at the implement work interface causing in exorbitant implement wear, low productivity and more power consumption.

Tian-Syung Lan and Ming-Yung Wang [1] culled the L9 orthogonal array of a Taguchi’s experiment for four parameters (cutting depth, aliment rate, cutting speed& implement nasal perceiver runoff) with three levels in optimizing the finish turning parameters on an ECOCA-3807 CNC lathe. The surface roughness (Ra) and implement wear ratio (mm-2) are replication factors. They concluded that both implement wear ratio and MRR from our optimum competitive parameters are greatly advanced with a minor decrementation in the surface roughness in comparison to those of benchmark parameters.

W.H. Yang and Y.S. Tarng [2] utilized the Taguchi’s method. An orthogonal array, the signal-to-noise (S/N) ratio, and the analysis of variance (ANOVA) are employed to investigate the cutting characteristics of S45C steel bars utilizing tungsten carbide cutting implements. They concluded that implement life and surface roughness can be amended significantly for turning operations. The amelioration of implementing life and surface roughness from the initial cutting parameters to the optimum cutting parameters is about 250%.

L. B. Abhang and M. Hameedullah [3] optimized machining parameters in EN-31 steel turning operation by utilizing tungsten carbide inserts & Taguchi’s method. They found the optimal cumulation of process parameters predicated on S/N ratio & the consequentiality of each parameter by performing ANOVA analysis. In this research work control parameter was alimented rate, depth of cut & lubricant temperature. They concluded that the better surface finish is obtained by applying cooled lubricant. If the lubricant temperature is lowered, with higher depth of cut the surface finish is amended.

S. V. Alagarsamy & N. Rajakumar [4] investigated analysis of influence of turning process parameters on the Material abstraction rate & Surface roughness of AA7075 utilizing the Taguchi’s method & RSM. They used L27 orthogonal array & MINITAB 16 statistical software to engender the array. They have culled three machining parameters cutting speed, victual rate & depth of cut. They concluded that victual is the most influencing factor for surface roughness & depth of cut is the most influencing factor for material abstraction rate.

On the vigor of the review of research done by anterior investigators, it is found that an abundance of work has been carried out by antecedent investigators for modeling, simulation and parametric optimization of surface properties of the product in turning operation. But a very lesser work has been found in utilization of different geometries for optimizing the surface properties.

2 Taguchi’s Method

The Taguchi’s method is oldest method of optimization and is industrial accepted method. It is powerful tool for design high quality system. It follows systematic, simple and efficient approach to optimize designs for performance, quality and cost. Taguchi’s method is effective method for designing process that operates reliably and optimally over a variety of conditions. To determine the best design it requires the use of a designed experiment. Taguchi’s converts the objective function values to Signal-to-Noise ratio (S/N ratio) to measure the performance characteristics of the levels of control factors. In the present research work, following S/N ratio criteria is used.

Smaller the better type:

Abbildung in dieser Leseprobe nicht enthalten (1)

3 Design Of Experiment Using Taguchi Technique

3.1 Selection of response factor

In this research work our focus is to minimize surface roughness .The response factor is surface roughness.

3.2 Selection of process variables

The process variable involved in research work is cutting speed, feed rate, depth of cut and nose radius. The spindle speed is taken as constant (2800rpm) and all trials are conducted in dry environment.

3.3 Process variable’s level

The process parameters levels are finalized by referring material processing data books and catalogs.

Table 1.Parameters levels

illustration not visible in this excerpt

3.4 Experimental Design

The experimental work includes:

Number of parameters = 4

Number of levels = 3

Total degree of freedom (DOF) for4 parameters = 4× (3-1) = 8

Therefore Minimum number of experiment = Total DOF for parameters +1 = 8 + 1= 9 = 20 + 1

Minimum number of experiment = 9

In this research work, interactions between factors are not considered .By referring above formulation, L9 (34) orthogonal array of Taguchi’s is selected.

3.5 Design Of Orthogonal Matrix

In the present research work Minitab 17software is used to construct the orthogonal matrix. The orthogonal matrix is shown in following table:

Table2.Design of Orthogonal Matrix

illustration not visible in this excerpt

4 Expermental Details

4.1 Experimental unit

The experimental unit ACE Designers Ltd. CNC turning centre with Fanuc Oi-mate-TD controller is shown in Figure.1

Abbildung in dieser Leseprobe nicht enthalten

Figure1. Experimental Unit

The specification of the experimental unit is shown in Table 3.

Table 3.Specification of Experimental Unit

illustration not visible in this excerpt

4.2 Properties of inconel718 [5]

The important properties of selected material are shown in Table 4.

Table 4.Properties of Inconel 718

illustration not visible in this excerpt

4.3 Chemical composition [6]

The chemical composition of any material decides it’s mechanical, thermal etc. properties. The chemical analysis of selected material is shown in Table 5.

Table 5.Chemical analysis of Inconel718

illustration not visible in this excerpt

4.4Size of work piece

The size of work piece Ø 18× 125 is employed for experimentation purpose.

Abbildung in dieser Leseprobe nicht enthalten

Figure 2.Work Piece

4.5 Cutting tool and tool holder

The cutting tool selected for present research work is Tin coated Tungsten Carbide inserts. The inserts used in present work are: TNMG160404, TNMG160408, TNMG160412 Tegutek Company (ISO coding).The tool holder used is HCLNL 2525M0904.

4.6 Surface roughness measuring

The surface roughness value is recorded with help of Make–Strumentazione, Model RT10G and L.C.0.001µm.

Abbildung in dieser Leseprobe nicht enthalten

Figure 3: Surface roughness measurement

5 Conducting Trials

The trials are conducted as per design of matrix values. The relative response values are got after trials are as follows:

Table 6: Response value table

illustration not visible in this excerpt

Table 7.Mean value table

illustration not visible in this excerpt

[...]

Details

Pages
8
Year
2015
ISBN (eBook)
9783668155268
File size
884 KB
Language
English
Catalog Number
v316674
Grade
Tags
Dry turning Minitab17 Surface roughness Taguchi’s method ANOVA

Author

Share

Previous

Title: Optimization and Analysis of Surface Roughness in CNC Turning Inconel 718 using Taguchi’s and ANOVA analysis