This paper is intended to investigate the characteristic of S45C steel into optimizing the quality, while considering productivity, through the use of DOE Taguchi method. A turning operation is main subject of this study while the output parameter selected is surface roughness.
One of the widely used working processes in industries nowadays is turning process. The importance of the work process in modern technology is due to the ease with which metal may be formed into useful shape by turning process. The ability to produce a variety of shapes from S45C steel such as valve control timing(VCT) pulley inner plate has becoming one of the outstanding qualities of the working process. Turning has becoming the most common method for cutting and especially for the finishing parts. In a turning operation, it is becoming an important task in order to select cutting parameters for achieving high quality of cutting performance.
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The cutting parameters are reflected on surface roughness, surface texture and dimensional deviations of the product. The surface roughness is used to determine and to evaluate the quality of the product which is the major quality attributes to the turning products. In a technological quality of products, the surface roughness has becoming one of the greatly influences of manufacturing cost. It describes the geometry of the combinations of machines surfaces with surface texture. The mechanism of the formation of surface roughness is so complicated and processes dependent, in order to select the cutting parameters properly it required several mathematical models based on statistical regression or neural network technique have to be constructed to establish the relationship between the cutting performance and cutting parameter (M. Nalbant et.el, 2007).
However in practice, different material would be influenced by the different machining parameters. It was been reported that when turning S45C, SKD-61, 9SMnPb28k (DIN) and SUS303 where the most important factors affecting the surface roughness of the product were feed rate, insert nose radius and the cutting speed. By using the Taguchi method approach, the S45C steel were machined and the different weighting were used to define the function of the quality characteristic studied for tool life, cutting force and surface roughness of the test piece (Tzeng Y.F, 2006).
Some more of informative studies made use of various workpiece materials and controlled parameters to optimize surface roughness, dimensional accuracy or tool wear. Each of its has to be utilized with different of combinations and levels of cutting speed, feed rate, depth of cut, cutting time, workpiece length, cutting tool material, cutting tool geometry, coolant and other machining parameter. The studies has discovered clear and useful correlations between their control and response parameter, where it indicate the numbers of different parameter which is a unique combination of parameter that can be tailored in order to suit a given situation (Kirby, 2006).
1.1 Problem statement
In turning operation to establishing the operation would ideally consider other implications of setup parameters such as production schedules, processing time, and noise factors. A more methodical, or experimental approach to setting parameters should be used to ensure that the operation meets the desired level of quality with given noise conditions and without sacrificing production time. Rather than just setting a very low feed rate to assure a low surface roughness, for example: an experimental method might determine that a faster feed rate, in combination with other parameter settings, would produce the desired surface roughness and dimensional accuracy.
Nowadays the shorter process planning time can lead to the use of machining parameters that are no optimal which will lead to the greater cost of production. The process planner will select the proper machining parameters by using not only his own experience and knowledge but also from handbooks of technological requirements, machine tool, cutting tool and selected parts of material. The cutting parameter such as cutting depth, number of passes, feed rate and machining speed has influence on almost all success of machining operation therefore in order to conduct optimization, a mathematical model has to be defined (Z. Car, 2009). In turning process parameters such as tool materials, the depth of cut, feed rates, cutting speeds as well as the use of cutting fluids will impact the material removal rates and the machining qualities like the surface roughness, the roundness of circular and dimensional deviations of the products. The surface roughness of the cutting process has been studied mostly through experiments where the Taguchi method will be implementing to investigate the cutting characteristics of S45C steel bars using tungsten carbide tools. The optimal cutting parameters involve are cutting speed, feed rate and the depth of cut for turning operations with regard to performance indexes such as tool life and surface roughness are considered (Chong.J.T, 2009).
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The surface quality is an important requirement for many machine parts where the purpose of the metal cutting process is not only of the shape machined elements but it also focus how it is manufacture so that they can achieve their functions according to geometric, dimensional and surface considerations. Unfortunately, surface roughness cannot be controlled as accurately as geometrical form and dimensional quality as it fluctuates according to many factors such as machine tool materials, environment, etc. So in other words, surface quality is affected by the machining process such as by changes in the conditions of the workpiece, tool or machine tool use. Surfaces roughness can changes over a wide range in response to these parameter (Durmus karayel, 2009).
1.2 Objectives of research
To examine the significance differences to the surfaces roughness using various parameter.
To suggest the optimum parameter which will influence the surface roughness by using Taguchi methods.
To develop an empirical model of relationships between parameters for surfaces roughness.
1.3 Scope of research
Area of study that intend to cover in this research:
To study performance of Huvema Cu 310 x 500 lathe.
To measure the surface roughness.
To study the cutting parameter such as feed rate, depth of cut and cutting speed that will influence the surface roughness.
CHAPTER 2: LITERATURE REVIEW
In conducting an effective parameter design of experiments (DOE) requires review of literature regarding to understand the process in this study. Additionally, recent review of DOE studies by the previous researcher and professionals are most helpful in determining what aspects of this method of DOE work best. Recent studies that explore the effect of setup and input parameter on surface finish all find that the there is a direct in feed rate , that spindle speed's effect is generally nonlinear and often interactive with other parameters, and that depth of cut can have some effect due to heat generation or chatter (M. Nalbant et.el, 2007; Kirby.E.D. 2010). In some of work that been implemented, problems in the predictive models are simplified by considering only one variable, the cutting speed in which to maximize the economical machining performance. Unfortunately, not only the cutting speed but also many factors for example feed and depths of cut are contributing to machining performance measures such as tool-life, surface roughness, chip breakability, material removal rate and dimension accuracy (Hagiwara.M et.el, 2009).
2.2 Review on turning process
Turning is a material removal process and it is a very important in machining process in which a single point cutting tool removes material from the surface of a rotating cylindrical workpiece. The cutting tool is fed linearly in a direction parallel to the axis of rotation. Fig.1 show that the turning is then carried out on a lathe that provides the power to turn the workpiece at a given rotational speed and to feed the cutting tool at a specified rate and depth of cut. Therefore, there are three cutting parameters, i.e cutting speed, feed rate and depth of cut need to be determined in a turning process ( Nalbant.M. 2007).
Figure : Scheme Of Turning Operation (Chong.J.T et.el, 2009)
From the previous study by using cutting tool made of carbide and coated with titanium nitride (TiN) where the cutting speed, feed rate, the depth of cut and cutting fluids mixture ratio are regulated in the experiment of turning process (Chorng J.T et.el, 2009). Other suggestion of the turning process where the parameters included is spindle speed, feed rate and depth of cut. By considering that the literature has suggested that the feed rate has much higher effect on surface roughness than the two parameters, it was been determined that a robust but efficient experiment would include feed rate with more levels than other factors (Kirby E.D, 2006).
2.3 Review on surface roughness
The study has demonstrate that the insert radius and feed rate are the main parameters among the three controllable factors (insert radius, feed rate and depth of cut) which influence the surface roughness in turning AISI 1030 carbon steel where surface roughness can be improved simultaneously through this approach instead of by using engineering judgment. The improvement of surface roughness form initial cutting parameters to the optimal cutting parameters is about 335(M.Nalbant et.el, 2007). Other study also stated that the feed rate is a dominant parameter and the surface roughness increases rapidly with the increase in feed rate. Meanwhile the cutting speed has a critical value for which the best surface quality can be achieved. Below this critical value, the surface roughness decreases with increasing cutting speed and after this value, the surface roughness increases with increasing cutting speed. Study show that the effect of depth of cut on surface roughness is not regular and has a variable character (Durmus karayel, 2009).
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The feed rate provides primary contribution and influences most significantly on the surface roughness. The interaction between feed rate and depth of cut, quadratic effect of feed rate and interaction effect of speed and depth of cut provide secondary contribution to the surface roughness while cutting speed has no significant effect on cutting forces and surface roughness (D.I. Lalwani et.al, 2008). Additionally there are two machining performance that can be measures which is chip breakability and surface roughness. It is considered as optimization criteria due to their importance in finishing operations where chip breakability covers two major factors as chip shape and size (Hagiwara.M, 2009).
2.4 Review on method
Taguchi method was developed for statistical design of experiments and selection of the optimal conditions. The approach of this method has significantly improved with time as the contributions of many researches increased. It aims to keep number of experiments minimum, and soundness of the currently followed steps has been proven by the time and many successful applications (Tansel I.N et.el, 2011). The used of an orthogonal array to reduce the number of cutting experiments for determining the optimal cutting parameter where the results of the cutting experiments are then studied by using the S/N and ANOVA analyzed. Based on the results of the S/N and ANOVA analyses, optimal cutting parameter for surface roughness are obtained and verified (M.Nalbant et.el, 2007).
In order to achieve a robust system with the design parameters, the Taguchi method separates an orthogonal array into inner and the outer to become two sections. The control factors are allocated in the former while the selected input signal coupled with noise factor allocated at the outer section. Every combination of the values of the control factors can be tested under the listed conditions of the outer section (Tzeng Y.F, 2006). Studied listed in table show the different combinations and level of turning parameter, with the goal is to minimizing surface roughness. Usually it utilizing the lower-the-better S/N ratio. But some more advanced studies utilized the cutting force, material removal rate or tool life as a response factors simultaneously with surface roughness (Kirby.E.D. 2010).
Table : Reviewed Studies (Kirby E.D, 2010)
Gaitonde, Karnik, & Davim, 2008
Depth of cut
Hascalik & caydas, 2007
Depth of cut
Jayant & kumar, 2008
Depth of cut
Depth of cut
Lan & wang, 2009
Depth of cut
Tool nose runoff
The conclusions that can be determined from the literature review state that the cutting parameter of turning is important in order to provide maximum quality of finishing. Therefore the parameters have to be clear determined to get an optimum condition. For these reasons the powerful tools to design for optimization for quality is a Taguchi Method. It is used to find the optimal cutting parameters for turning operations where an orthogonal array, the signal-to-noise (S/N) ratio, and the analysis of variance (ANOVA) are employed in order to investigate the cutting optimize parameter of turning.
CHAPTER 3: MATERIALS AND METHODOLOGY
The cutting experiments will be carried out on an engine lathe of Huvema cu 310 x 501 using tungsten carbide with the grade P10 for the machining of S45C steel bars. The feasible of the cutting parameters was defined by varying the cutting speed in the range 85 - 2000 rpm, the feed rate range 0.006 - 1.77 mm/rev and the depth of cut in the range of 0.6 - 1.6 mm. While in the cutting parameters design, three levels of the cutting parameters were selected, shown in Table 1.
3.2 Taguchi methods descriptions
The Taguchi methods then is introduced to determine and analyze the optimum cutting parameters with regards to performance indexes such as surface roughness and dimensional accuracy are considered. There are three-step approaches in Taguchi methods which is system design, parameter design, and tolerance design. In system design, the engineer applies scientific and engineering knowledge to produce a basic functional prototype design, this design including the product design stage and the process design stage. In the product design stage, the selection of materials, components, tentative product parameter values are involved. As to the process design stage, the analysis of processing sequences, the selections of production equipment, tentative process parameter values are involved.
If the optimal process parameter values obtained from parameter design are insensitive to variation in the environmental conditions and other noise factors. Then tolerance design is used to determine and analyze tolerances around the optimal settings recommend by the parameter design. Tolerance design is required if the reduced variation obtained by the parameter design does not meet the required performance, and involves tightening tolerances on the product parameters or process parameters for which variations result in a large negative influence on the required product performance.
The Taguchi method uses a special design of orthogonal arrays to study the entire parameter space with a small number of experiments only. The experimental results are then transformed into a signal-to-noise (S/N) ratio. Taguchi recommends the use of the S/N ratio to measure the quality characteristics deviating from the desired values. Usually, there are three categories of quality characteristic in the analysis of the S/N ratio. The three categories are "the lower the better", "the higher the better" and "the nominal the better". Therefore, the optimal level of the process parameters is the level with the greatest S/N ratio. Furthermore, a statistical analysis of variance (ANOVA) is to perform which process parameters are statistically significant, with the S/N and ANOVA analyses, the optimal combination of the process parameters can be predicted. Finally, a confirmation experiment is conducted to verify the optimal process parameters obtained from the parameter design.
Quality characteristic :
Nominal is the best: S/NT = 10 log
Larger is the best: S/NL = -10 log
Smaller is the best: S/Ns = -10 log
where, is the average of observed data, is the variance of y, n is the number of observations and y is the observed data.
Summarize of the Taguchi methods that going to be implemented are as below:
Identify the quality characteristics and select the design parameters which are to be evaluated.
Determine the number of levels for the design parameters and possible interactions between the design parameters.
Select the appropriate orthogonal array and assignment of design parameters to the orthogonal array.
Conduct the experiments based on the arrangement of the orthogonal array.
Analyze the experimental results by using the S/N and ANOVA analyses.
Select the optimal levels of design parameters.
Verified the optimal design parameters through the confirmation experiment.
3.3 Turning cutting process experiment
Turning process is a single-point cutting tool removes material from the surface of a rotating cylindrical workpiece. The cutting tool is fed linearly in a direction parallel to the axis of rotation. Turning is then carried out on a lathe that provides the power to turn the workpiece at a given rotational speed and to feed the cutting tool at a specified rate and depth of cut. On these studies, three cutting parameters have been selected as parameter for turning operation. The parameters are cutting speed, depth of cut and feed rate.
The cutting experiments were carried out on a Huvema cu 310 x 501 lathe machine by using tungsten carbide as a cutting tool and S45C steel bars as a workpiece for machining. From the experiments, the surface roughness then is measured. Table.1 showed the selected cutting parameters and their levels.
Table : Cutting Parameter
Cutting speed (rpm)
Depth of cut (mm)
Feed rate (mm/rev)
3.4 Surface roughness performance measuring
There are various simple surface roughness amplitude parameters used in industry, such as roughness average(Ra), root-mean-square (rms) roughness (Rq), and maximum peak-to-valley roughness (Ry or Rmax). In this study, the parameter of average roughness (Ra) is selected and will be measured by a portable surface roughness analyzer of Mitutoyo Sj-301/0.75mn (178-955-2e). It is the area between the roughness profile and its mean line (Fig 1) and can be specified by equation:
Figure : Surface Roughness Profile (M.Nalbant et.el, 2007).
where Ra is the arithmetic average deviation from the mean line, L is the sampling length and Y is the ordinate of the profile curve.
Orthogonal array experiment
An orthogonal array to reduce the number of cutting experiments for determining the optimal cutting parameters is obtain where The degrees of freedom are defined as the number of comparisons between process parameters that need to be made to determine which level is better and specifically how much better it is. In this paper an L9 orthogonal array was used. This array has twenty six degrees of freedom and it can handle three-level process parameters. Each cutting parameter is assigned to a column and twenty seven cutting parameter combinations are available. Therefore, only twenty seven experiments are required to study the entire parameter space using the L9 orthogonal array. Experimental layout using an L9 orthogonal array is shown in Table 2.
Cutting parameter lever
Measured surface roughness
Calculated S/N ratio for surface roughness
Depth of cut
Table : L9 Orthogonal array experimental layout
To obtain optimal machining performance characteristic, the small-the-better performance characteristic for surface roughness should be taken for obtaining optimal machining performance.
3.5 Analysis of variance (ANNOVA)
ANNOVA will be used to investigate which of the process parameters significantly affect the performance characteristics. It is done by separating the total variability of the S/N ratios, which is measured by the sum of the squared deviations from the total mean of the S/N ratio. It can be calculated using equation:
SST = =
where m is the number of experiments in the orthogonal array, e.g., m = 9 and Æži is the mean S/N ratio for the ith experiment.
3.6 Confirmation tests
The final step is to predict and verify the improvement of the performance characteristic using the optimal level of the process parameters. The estimated S/N ratio using the optimal level of the process parameters can be calculated as:
where Æžm is the total mean of the S/N ratio, Æži is the mean S/N ratio at the optimal level, and q is the number of the process parameters that significantly affect the performance characteristic.
Frame work to conduct experiments: