03-11-2016, 03:01 PM
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Abstract
The objective of the study is to optimize the process by applying the Taguchi method with
orthogonal array robust design. Taguchi Parameter Design is a powerful and efficient method for
optimizing the process, quality and performance output of manufacturing processes, thus a
powerful tool for meeting this challenge. Off-line quality control is considered to be an effective
approach to improve product quality at a relatively low cost. The Taguchi method is one of the
conventional approaches for this purpose. This procedure eliminates the need for repeated
experiments, time and conserves the material by the conventional procedure. Optimization of
process parameters is done to have great control over quality, productivity and cost aspects of the
process. Off-line quality control is considered to be an effective approach to improve product
quality at a relatively low cost. The Taguchi method is a powerful tool for designing high quality
systems. The approach is based on Taguchi method, the signal-to-noise (S/N) ratio and the analysis
of variance (ANOVA) are employed to study the performance characteristics.
Introduction
Metal cutting is one of the most important and widely used manufacturing processes in
engineering industries and in today’s manufacturing scenario, optimization of metal cutting
process is essential for a manufacturing unit to respond effectively to severe competitiveness and
increasing demand of quality which has to be achieved at minimal cost [1]. Machining is an
essential finishing process by which jobs of desired dimensions and surface finish are produced by
gradually removing the excess material from the performed blank in the form of chips with the
help of cutting tools moved past the work surfaces. This study helpful in evaluating optimum
machining parameter like cutting speed, feed and depth of cut for cutting force for turning AL on
CNC Lathe machine. The selection of optimal cutting parameter like the number of passes, depth
of cut for each pass, feed rate and speed is a very critical issue for every machining process.
Surface finish in turning has been found to be influenced in varying amounts bya number of
factors such as feed rate, work hardness, unstable built up edge, speed, depth of cut, cutting time,
use of cutting fluids etc. The three primary process parameters in any basic Turning operation are
speed, feed, and depth of cut. Speed always refers to the spindle and the work piece. Feed is the
rate at which the tool advances along its cutting path. Depth of cut is the thickness of the material
that is removed by one pass of the cutting tool over the work piece. After experimentally turning
sample work pieces using the selected orthogonal array and parameters, this study produced a
verified combination of controlled factors and a predictive equation for determining surface
roughness with a given set of parameters.
Surface Ruoghness :
The surface roughness of machined surface has been measured by a Surface Roughness
Measuring instrument. The Surtronic 3+ is a portable, self-contained instrument for the
measurement of surface texture and is suitable for use in both the workshop and laboratory
(Fig.4). Parameters available for surface texture evaluation are: Ra, Rq, Rz (DIN), Ry and Sm. The
parameter evaluation and other functions of the instrument are microprocessor based. The
measurement results are displayed on LCD screen and can be output to an optional printer.
TAGUCHI ANALYSIS
Overview of Taguchi method
Taguchi method (or approach) goes by the name of the author and researcher Dr.Genichi
Taguchi of Japan. As the head of research department for Electronic Control Laboratory of Nippon
Electric Company in the late 1940's, Dr. Taguchi performed extensive research utilizing the DOE
technique to improve the quality of manufactured products [1]. By early 1960's Dr. Taguchi
introduced a standardized version of DOE along with a definite set of guidelines for improving
consistency of performance and relating the same to cost savings through resulting reduced tool
for design of high quality systems. It introduces an integrated approach that is simple and efficient
to find the best range of designs for quality, performance and computational cost. This method
has been utilized widely in engineering analysis to optimize performance characteristics within the
combination of design parameters because of its proven success in greatly improving industrial
product quality.
Orthogonal Arrays (OA) are generally used for the experimentation in Taguchi analyses. It
is nothing but the shortest possible matrix of combinations in which all the parameters are varied
at the same time and their effect and performance interactions are studied simultaneously. These
are special matrices used as the design matrices in the fractional factorial design for the
estimation of the effect of several factors in a highlyefficient way. These designs are applicable
even when the factors have more than two levels and for mixed level experiments where that
factors do not have same number of levels. In general factorial design, when the number of
process parameter increases, large number of experiments has to be carried out. But in Taguchi’s
orthogonal array, carrying out small number of experiments in the specified range the effects of
process parameters can be studied.
In the present investigation, an L27 orthogonal array is chosen. To choose an appropriate
orthogonal array, the total number of degrees of freedom needs to be computed. An L27 OA has 27
rows corresponding to the number of tests and 26 degrees of freedom (DOFs) with 13 columns at
three levels (Table 2). To check the DOFs in the experimental design, for the three level test, the
three main factors take 6 [3 × (3 – 1)] DOFs. The DOF for three second-order interactions (S × F, S ×
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D, F × D) is 12 [3 × (3 – 1) × (3 – 1)] and the total DOFs required is 18. As per the Taguchi method,
the total DOFs of selected OA must be greater than or equal to the total DOFs required for the
experiment and hence the L27 OA has been selected. The degrees of freedom are defined as the
number of comparisons between process parameters that can be made to determine which level
is better. The degrees of freedom associated with interaction between two process parameters
are given by the product of degrees of freedom for two process parameters. Another selection
criterion is based on the condition that the degrees of freedom for the orthogonal array should be
greater than or equal to sum of those input parameters. The set of experiments where there are
three factors with three levels each, in the Taguchi orthogonal array, the 1st column is assigned to
load (S), 2nd column is assigned to speed (F) and the 5th column is assigned to time (D) and six
columns are assigned to the two-way interactions of the three factors while the remaining four
columns are for error terms. The main effects and the individual effects of the testing parameters
both the studies have been considered in this L27 Taguchi orthogonal array design.
Table 2 shows the OA with design factors and their interactions assigned. Here each
column represents a specific factor, each row represents an experimental run and the cell values
indicate the factor settings for the run. The cell values in the main factor columns (i.e. S, F and D)
indicate their levels (1, 2 or 3) while the same in interaction columns (two cell fields in two
columns for a particular interaction) indicate the combination of the levels of the main factors
concerned. For example, the interaction S × F occupies columns 3 and 4, and for trial no 1, the cell
fields show 1 in column 3 and 1 in column 4. Thus S × D has the value 11 which means it is the
combination of level 1 of S and level 1 of F. Similarly there are 9 such combinations (11, 22, 33, 12,
21, 23, 32, 13 and 31) for S × F interaction in columns 3 and 4. A similar procedure applies to other
interaction terms as well. However, the experimental run is controlled by the settings of the
controllable design factors, i.e. S, F and D and not by the interactions.
Conclusion
The research has demonstrated an application of the Taguchi method for investing the
effects of cutting parameters on surface smoothening in turning aluminium metal. With analysis of
results in this work using S/N ratio approach and ANOVA provides systematic and efficient
methodology for the optimization of surface roughness. The surface roughness is mainly affected
by cutting speed, depth of cut and feed rate, by changing any one the machining parameters. The
parameters considered in this experiment are optimized to attain minimum surface roughness.
From the above study shows that the optimal combination of parameters is found on S3F1D1.
That means minimum roughness will be obtained at higher spindle speed followed by lower feed
rate and less depth of cut. The best parameters for material removal rate has been found at
spindle speed of 2400 RPM in level 3, feed rate at 40 mm/rev in level 1 and depth of cut at 0.3 mm
in level 1 on the basis of main effect plot and also through ANOVA analysis. Surface plots are also
supports the combination of optimization levels.