Manufacturing Engineering

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APPLICATION OF TAGUCHI AND ANOVA METHODSIN SELECTION OF PROCESS

PARAMETERSFOR SURFACE ROUGHNESS IN PRECISION TURNING OF TITANIUM

ADVANCES IN MANUFACTURING SCIENCE AND TECHNOLOGY Vol. 38, No. 2, 2014

DOI:10.2478/amst-2014-0008

1. INTRODUCTION

The objective of this study is the selection of cutting data (such as nc, f, ap) and tool

materials (PCD,ceramic, CBN and carbide cutting tools) in order to improve the surface

roughness in precision turningoperation of parts made of pure titanium (GRADE 2). Machining

parameters and tool materials areconsidered as input parameters. The surface roughness is

selected as the process output measure ofperformance. A Taguchi approach is employed to

gather experimental data. Then, based on signal-tonoise(S/N) ratio, the best sets of cutting

parameters and tool materials specifications have been determined.

2. Taguchi experiment: design and analysis

The Taguchi method uses a special design of orthogonal arrays to study the entire parameter space with

only a small number of experiments.Taguchi methods have been widely utilized in engineering analysis

and consist of a plan of experiments with the objective of acquiring data in a controlled way, in order to

obtain information about the behavior of a given process The greatest advantage of this method is

saving of effort in conducting experiments; saving experimental time, reducing costs, and discovering

significant factors quickly.

3. Machining conditions and experimental design

The material used in this work was a pure titanium, Ti (Grade 2).The test sample was prepared in the form of shaft, 28 mm diameter with separated parts.Turning tests were carried out on a Masterturn 400 with different cutting speeds, feed, depth of cut and cutting tools; in accordance with the Taguchiexperiment design. The lathe equipped with variable spindle speed from 1 to 3000 rpm, and a 7.5 kW motor drive was used for the tests.

4. Results and analysis of experiment

In the Taguchi method, the term ‘signal’ represents the desirable value(mean) for the output characteristics and the term ‘noise’ represents theundesirable value for the output characteristics. Taguchi uses the S/N ratio tomeasure the quality characteristic deviating from the desired value. There areseveral S/N ratios available, depending on the type of characteristics: lower isbetter (LB), nominal is the best (NB), or higher is better (HB) [13]. Smaller isbetter S/N ratio was used in this study, because lower parameters of surfaceroughness (Ra and Rz) were desirable.Quality characteristics of the smaller is better is calculated according to the following equation:

η = –10 × log10 [(1/n) × S(yi2)] (1)

where n is the number of measurements in a trial/row and yi is the measured value in a run/row. The S/N ratio values were listed in Table 6 for parameters of surface roughness (Sa and Sz). Table 7 shows the response table for S/N ratio of Sa for “smaller is better” obtained for different parameter levels.

From the S/N ratio analysis (Fig. 2 and 3) the optimal machining conditions were 100 m/min cutting speed (level 2), 0.038 mm/rev feed rate (level 1),0.1 mm depth of cut (level 3), PCD cutting tools (level 1) for parameter Sa.150 m/min cutting speed (level 4), 0.048 mm/rev feed rate (level 2), 0.125 mm depth of cut (level 4), PCD cutting tools (level 1) for parameter Sz. respectively.

5. Conclusion

Surface roughness is an important measure of performance in machining operations. This study

investigates the overall effects of turning parameters (cutting speed. depth of cut and feed rate) and kind

of cutting tool geometry specifications on surface roughness of Ti (Grade 2) parts. To model the process. Taguchi method has been employed for experimental tests. surface methodolog was used to determine optimal values of input parameters to achieve the minimum surface roughness; as the process output characteristics.

The optimal machining conditions were 100 m/min cutting speed (level 2), 0.038 mm/rev feed rate(level 1), 0.1 mm depth of cut (level 3), PCD cutting tools (level 1) for parameter Sa. 150 m/min cutting speed (level 4), 0.048 mm/rev feed rate (level2), 0.125 mm depth of cut (level 4), PCD cutting tools (level 1) for parameter Sz, respectively.

In addition the effects (in percent) of each these parameters on the output have been determined. It is shown that the kind of the cutting tool has the most significant effect on the surface roughness