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Now a days, due to the increasing demand of higher precision components for its functional aspect, surface roughness of a machine part plays an important role in the modern manufacturing process. Surface finish is a key machining process integral to evaluating the quality of a particular product. Many different attributes of the product, including surface friction, wearing, heat transmission, the ability to distribute and hold a lubricant, the ability to accept a coating, and the ability to resist fatigue, are at least partially distinguished by how well the surface finish is produced, due to the fact that surface roughness affects several functional attributes of products. Consequently, the desired surface roughness value is usually specified for an individual part, and specific processes are selected in order to achieve the specified finish.
Surface specification can also be a good reference point in determining the stability of a production process, because the stability of the machine is contingent on the quality of the operating part. Being one of the most basic and traditional metal removal processes, single-point turning operations have historically received a great deal of attention in research relating to surface roughness recognition.In this research, surface finish in turning operations is influenced in varying amounts by a number of factors, such as feed rate, workpiece material characteristics, cutting speed, depth of cut, tool wear, side and end cutting edge angles of the tool, and use of coolants, among others. Thus, predicting surface finish during the machining operation is extremely difficult unless some measurement of the product finish can be obtained from experiments.
Manufacturers focus on developing manufacturing systems that produce superior quality products with on-time delivery at minimum cost. Research directed at this goal has attempted to develop robust and accurate in-process machine monitoring systems with good defect prevention capabilities. One of the most important machine-monitoring strategies is monitoring product-quality during the machining process in study done by J.Kopac, A.Stoic, M.Lucic, (2006).
Producing a desired surface roughness, which is one of the most important factors in measuring the quality of machined products, presents mechanical and economic issues in manufacturing environments. Not only does producing the appropriate surface roughness affect the functional attributes of products, it also affects manufacturing costs according to reserch W.S. Lin, (2008). Engineers set cutting parameters to attain very specific surface roughness values for most parts produced in turning operations (which is one of the most important machining processes for material removal). Achieving these specifications is a major goal of the turning machining process.
The challenge of modern machining industries is mainly focused on the achievment of hight quality in terms of workpiece dimension accuracy, surface intergrity, and high production rate. Surface roughness plays an important role in many area and is a factor of great importance in the evolution of machining accuracy. In this research, Taguchi Method will be used to determine the optimum cutting condition in turning process. Taguchi method is a traditional approach for robust experimental design that seeks to obtain a best combination set of factors/levels with the lowest societal cost solution to achieve customer requirements according to J.Ross, (1996) on his book "Taguchi techniques for quality engineering", McGraw Hill, Singapore.
1.2 Reserch Questions
The research questions such as :-
What are the variables that effect surface roughness in turning process?
What is the significant difference to the surface roughness using various parameters during turning process?
What is the optimum surface roughness with particular combination of cutting parameter?
The objective of this research such as:-
To examine the significant difference of various parameters during turning process to the surface roughness.
To demonstrate the use of Taguchi parameter design in order to identify the optimum surface roughness with particular combination of cutting parameters.
To suggest the optimum cutting parameter that effect surface roughness in turning process.
1.4 Importance of Study
1. To study the cutting parameter as feed rate, cutting speed, and depth of cut will effect the surface roughness on S50C Medium Carbon Steel work peace.
2. To measure the surface roughness.
1.5 Research Limitation
This study focused only to the surface roughness quality in turning operations in the machine shop. Cutting speed, feed rate and depth of cut were the variable that will be examine in these research that effect surface roughness using Taguchi method.
2.0 LITERATURE REVIEW
Taguchi's approach to design of experiments is easy to adopt and apply for users with limited knowledge of statistics. R. Jeyapaul, P. Shahabudeen, K. Krishnaiah,(2006) in their study state that in the Taguchi design method the design parameters (factors which can be controlled) and noise factors (factors which can't controlled), which influence product quality, are considered. The main trust of the Taguchi technique is the use of parameter design, which is an engineering method for product or process design that focuses on determining the parameter (factor) settings producing the best levels of quality characteristic with minimum variation. According to Ersan Aslan et al (2006), Taguchi design provides a powerful and efficient method for designing processes that operate consistently and optimally over a variety of conditions. According to P.J.Ross,(1996), experimental design methods were developed in the early of 20th century and have been extensively studied by the statistician since then, but they were not easy to use by practitioners. This statement has been supported by J.A.Ghani et al (2004) in their study that Taguchi recommended using solutions in metal cutting problems to optimize the parameters.
Nalbant et al (2007), in their study use Taguchi method to fine the optimal cutting parameters for surface roughness in turning. Considering the surface roughness, they optimized three cutting parameter namely insert radius, feed rate and depth of cut. Dagwa (2009) in his research optimized cutting parameters (cutting speed,depth of cut and feed rate) in conventional turning operation using taguchi method in surface roughness optimization of a solod round bar of mild steel material. Thamizhmanii et al (2007) analyzed the optimum cutting condition to get the lowest surface roughness in turning SCM440 aloy steel by taguchi method. The deepth of cut was found to be playing a significant role in producing lower surface roughness followed by feed rate. The cutting speed had the least role on surface roughness.
Due to the widespread use of highly automated machine tools in the industry, manufacturing requires reliable models and methods for the prediction of output performance of machining processes. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning. Previouse work by Reddy et al (2005) has development of a surface roughness prediction model for machining mild steel, using Response Surface Methodology (RSM). The experimentation was carry out with TiN-coated tungsten carbide (CNMG) cutting tools, for machining mild steel work-pieces covering a wide range of machining conditions. According Suresh et al(2002), a second order mathematical model, in terms of machining parameters, is developed for surface roughness prediction using RSM. This model give prediction the factor effects of the individual process parameters. An attempt also been made to optimize the surface roughness prediction model using Genetic Algorithms (GA) to optimize the objective function. The GA program gives minimum and maximum values of surface roughness and their respective optimal machining conditions.
Surface roughness and tolerances are among the most critical quality measures in many mechanical products. As competition grows closer, customers now have increasingly high demands on quality, making surface roughness become one of the most competitive dimensions in today's manufacturing industry research done by Feng, C.X. (Jack), (2001). Surfaces of a mechanical product can be created with a number of manufacturing processes. In this research Feng, C.X. (Jack), (2001) applies the fractional factorial experimentation approach to studying the impact of turning parameters on the roughness of turned surfaces. Analysis of variances is used to examine the impact of turning factors and factor interactions on surface roughness. Finally, contributions are summarized and future research directions are highlighted.
Surface roughness plays an important role in product quality. Reserch by Feng, C.X. (Jack) X. Wang, in 2002 focuses on developed an empirical model for the predict of surface roughness in finish turning using nonlinear regression analysis with logarithmic data transformation. In their research their considered six working parameters,work piece hardness (material), feed,cutting tool point angle, depth of cut, spindle speed, and cutting time to develop the model. Hadi and Ahmed (2006) in their research focused on developing an empirical model for prediction of surface roughness in finish turning. The model considered feed, depth of cut and spindle speed as working parameters. They applied nonlinear regression analysis with logarithamic data transformation in developing the empirical model.
Optimum selection of cutting conditions importantly contribute to the increase of productivity and the reduction of costs, therefore utmost attention is paid to this problem in this contribution. Sahin and Motorcu (2005) has developed a surface roughness model for turning of mild steel with coated carbide tools in terms of process control parameters like cutting speed, feed rate and depth of cut using response surface methodology. The feed reat was found to be the most influencing factor on surface roughness. The surface roughness increased when feed rate increased meanwhile surface roughness decreased when cutting speed and depth of cut increased.
Zhang et al (2007) in their paper, they present a study of the Taguchi design application to optimize surface quality in a CNC face milling operation. Maintaining good surface quality usually involves additional manufacturing cost or loss of productivity. The Taguchi design is an efficient and effective experimental method in which a response variable can be optimized, given various control and noise factors, using fewer resources than a factorial design. This study included feed rate, spindle speed and depth of cut as control factors, and the noise factors were the operating chamber temperature and the usage of different tool inserts in the same specification, which introduced tool condition and dimensional variability. Reserch done by P. Thangavel, V.Selladurai, 2008 state that an orthogonal array of L9 be use; ANOVA analyses were carried out to identify the significant factors affecting surface roughness, and the optimal cutting combination will determined by seeking the best surface roughness (response). Finally, confirmation tests verified that the Taguchi design will successful in optimizing turning parameters for surface roughness.
A considerable amount of work has been carried out by previous investigators for modeling,simulation and parametrict optimization of surface properties of the product in turning process. The process control parameters from previous investigators was cutting speed, feed, depth of cut,tool geometry and tool life have been addressed. A ccording to the literature, the Taguchi design has proven to be practical and effective for use, so it will utilizes in this study to quantify the effect of the turning process factors on the surface roughness. In this study cutting speed, feed rate and depth of cut will be the control parameters.
3.1 Design of Experiment
This study will going to uses Taguchi method to optimizes cutting parameters (cutting speed, feed rate and depth of cut) on a surface roughness for a turning operation S50C steel. The Taguchi design is an efficient and effective experimental method in which a response variable can be optimized, given various control and noise factors, using fewer resources than a factorial design. This study included feed rate, spindle speed and depth of cut as control factors, same tool inserts in the same specification. An ANOVA analyses will carries out to identify the significant factors affecting surface roughness, and the optimal cutting combination will determine by seeking the best surface roughness (response).
Test for significance of the regression model is performed as an ANOVA procedure by calculating the F- ratio, which is the ratio between the regression mean square and the mean square error. This ratio is used to measure the significance of the model under investigation. Y is the logarithmic transformation of the surface roughness. X1, X2, X3, and the constraints are in the logarithmic transformed form of the depth of cut, feed rate and speed in P. Thangavel, V.Selladurai, (2008) reserch.
Most analyses of roughening behavior are derived from linear profiles from study by J.Kopac, A.Stoic, M.Lucic, (2006). In addition, the assessments of the surface roughness in many analyses are based on the arithmetic mean of the height profile, or the Ra parameter:
3.2 Work Material
Medium carbon steel, S50C will be uses in this study as the work piece. This medium carbon steel has good machinability and mechanical strength. The general application of this material is punch holders, die holders, guide plates, backing plates jigs and fixtures. Table 3.1shows the chemical composition of the work material meanwhile table 3.2 shows physical properties of work material S50C.
Table 3.1 Chemical composition of work material S50C. (Sources : T. R. Anbarasan Thangavelu, 2007)
Table 3.2 Physical properties of work material S50C. (Sources : T. R. Anbarasan Thangavelu, 2007)
Hardness, Rockwell C
58 Hardness after tempering at 200Â°C
Tensile strength, Ultimate
Tensile strength, Yield
Elongation at Break
Reduction at Break
Modulus of elasticity
Modulus of elasticity at elevated Temp
3.3 Cutting Tool And Machine
The cutting tool use in this experiment are tungsten carbide. The tests will Conduct experiment using Huvema Cu 310 x 500 lathe machine. This machine is having fixed revolution per minute and fixed feed. Three levels of cutting speed, three levels of feed and three levels of depth of cut were used and are shown in the Table 3.3.
Table 3.3 Levels and factors in machining
Cutting speed (m/min)
Depth of cut(mm)
Portable surface roughness tester model Mitutoyo sj-301/0.75MN (178-955-2E) is going to be use in this study. This tester will give data that needed in this study on surface roughness during turning process.
3.4 Orthogonal Array Experiment
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
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 will be use. This array has twenty six degrees of freedom and it can handle three-level process parameters (cutting speed, feed and depth of cut) . 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 are shown in Table 3.4. To obtain optimal machining performance characteristic, the small-the-better performance characteristic for surface roughness should be taken for obtaining optimal machining performance
Table 3.4 L9 Orthogonal array experimental layout
Cutting Parameters Lavel
Depth of Cut (DOC)
Frame work to conduct experiments :