Optimization of turning parameters for machinability using Taguchi method An experimental investigation

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1 Optimization of turning parameters for machinability using Taguchi method An experimental investigation N B DoddaPatter* 1, H M Somashekar 1, Dr. N. Lakshmana swamy 2, Dr. Y.Vijayakumar 3 1 Research Scholar, U V C E, Bangalore University, Bangalore, Karnataka, India 2 Professor, Department of Mechanical Engineering, U V C E, Bangalore University, Bangalore University, 3 Principal, S B M Jain College of Engineering, Jain University, Bangalore Abstract: Today, the Industry is expecting excellence in manufacturing to face the stiff competition and to survive in ever growing markets. In this scenario, the authors made an attempt to determine the effect of various cutting parameters on the surface roughness of Aluminium alloy using a CNC machine for turning operation. The Experiment was designed using the Taguchi technique, in which the parameters were selected, based on the importance. For the first phase of the experiments plain aluminium was chosen as the material for the work piece and carbide tips were used for the tool. Based on the literature survey it was determined that the parameters which mainly affect the surface roughness are feed, spindle speed, depth of cut and tool nose radius. The surface roughness was measured on 4 sides of the cylindrical work piece and the average value was calculated for greater accuracy. This observation was used for the analysis. ANOVA was used to analyse the observations. It was found from the analysis that feed was the most important parameter affecting the surface roughness and consequently the machinability. However it was also found that nose radius of the tool used is an important parameter. Although cutting speed and DOC did not independently have as much influence on the quality characteristics in question, as feed and nose radius, the level of influence of its interactions with other factors and each other were determined to be high enough to consider these factors for the next phase or experimentation. Keywords: Machinability, Optimization, Taguchi method, Surface Roughness, Cutting parameters *Corresponding author narayan.pattar@yahoo.co.in Ph: INTRODUCTION: Turning operation is a very important machining process in which a single point cutting tool removes material from the surface of a rotating cylindrical work piece. The cutting tool is fed linearly in a direction parallel to the axis of rotation In this work turning operation is carried out on a machining centre that provides the power to turn the work piece at a given rotational speed and to feed the cutting tool at a specified rate and depth of cut. In a turning operation, it is an important task to select cutting parameters for achieving high cutting performance. Usually the 194

2 desired cutting parameters are determined based on experience or by use of a hand book. However, this does not ensure that the selected cutting parameters have optimal or near optimal cutting performance for a particular machine and environment. Therefore, three important cutting parameters i.e cutting speed, feed rate and depth of cut, need to be determined in a turning operation. Since turning operations are accomplished using a cutting tool, the high forces and temperature during machining create a very harsh environment for the cutting tool. Therefore, tool life is an important index to evaluate cutting performance in a turning operation. In addition, the purpose of turning operations is to produce a low surface roughness of the machined work piece. Therefore, surface roughness is another important index to evaluate cutting performance. Tool life and surface roughness are correlated strongly with cutting parameters such as cutting speed, feed rate and depth of cut[2]. Proper selection of the cutting parameters can secure longer tool life and better surface roughness. Hence Design optimization of the cutting parameters based on the Taguchi method is adopted to improve the tool life and the surface roughness in a turning operation[3]. There are a number of reported studies to ascertain the influence of machining variables and tool geometry on the resulting surface finish values. However, studies on coated tools especially to investigate the effect of these variables on the resulting surface finish are lacking. H.L. Fischer et.al investigates the theoretical relationships between surface finish, the geometric characteristics of a single point turning tool and the feed of the tool. The theoretical base will provide a bench mark and guide for studying the effect of other variables. The purpose of their investigation is to furnish the charts in concise and usable form, covering all of the more common feeds, tool nose radii and angles of approach of the tools to the work piece and to give the depths of scallop, arithmetic average and root mean square values for these conditions. SundaramR.M. et.al made the experimental development of mathematical models for predicting the surface finish of AISI 4140 steel in fine turning operation using TiC Coated tungsten carbide throw-away tools. Rotatable design for the experimental procedures was adopted for their research work. The variables like cutting tool material, Machine vibration, Non Rigidity of the tool are not considered in their work. Hence, an attempt has been made to use the various parameters in our study. From the literature it is learnt that limited efforts have been made to optimize the machinability parameters such as Cutting speed, feed, depth of cut and time of cut of the tool. In view of the previous discussion, an attempt has been to determine the effect of various cutting parameters on the surface roughness of Aluminium alloy using a CNC machine for turning operation. 1.2 surface roughness Surface roughness is one of the prime factors in evaluating the quality of a component as it affects all the dimensions of quality mentioned above. Since it is a very important factor of quality it has received serious attention for many years. It has formulated an important design feature in many situations such as parts subject to fatigue loads, precision fits, fastener holes and aesthetic requirements. Surface roughness imposes one of the most critical constraints for selection of machines and cutting parameters in process planning. Surface Roughness is generally the vertical elevations made on the surface of the metal. Roughness is typically considered to be the high frequency, short wavelength component of a 195

3 measured surface. Although roughness is usually undesirable, it is difficult and expensive to control in manufacturing. Decreasing the roughness of a surface will usually increase exponentially its manufacturing costs. This often results in a tradeoff between the manufacturing cost of a component and its performance in application. In some cases the surface roughness is a necessary factor, but it has to conform to certain conditions and has to be within a certain limit. According to Kromanis, A. and Krizbergs, J [2], the quality of surface plays a very important role in functionality of produced part. Therefore, it is necessary to develop methods, which can be used for the prediction of the surface roughness according to technological parameters. Measuring surface roughness is a crucial concern for an immense range of industries and applications, from auto component wear to medical implant efficacy, from micro-electro-mechanical systems (MEMS) inspection to semiconductor thinfilm uniformity. The performance and value of thousands of products and systems from designer sunglass to advanced personal digital devices is governed by characterizing and controlling microscale surface features, including micro-inch surface roughness. Many of the major advances in science and industry over the past half century would not have been possible without accurate surface roughness metrology[4,6-8]. The parameter most used for general surface roughness is Ra. It measures average roughness by comparing all the peaks and valleys to the mean line, and then averaging them all over the entire cut-off length. 1.3 Taguchi techniques Dr. Taguchi started to develop new methods to optimise the process of engineering experimentation. He believed that the best way to improve the quality of a design was to design and build it into the product. He developed techniques which are now known as the Taguchi Methods. His main contribution to the field is not mathematical, but rather the Philosophy. His concepts produced a unique and powerful quality improvement technique that differed from traditional practices. His philosophy had far reaching consequences, yet it is founded on three very simple concepts. His techniques arise entirely out of these three ideas[1]. The concepts are: Quality should be designed into the product and not inspected into it. Quality is better achieved by minimising the deviation from a target. The product should be so designed that it is immune to uncontrollable environmental factors. The cost quality should be measured as a function of deviation from the standard and the losses should be measured systemwide. Taguchi viewed quality improvement as an ongoing effort. He continually strived to reduce the variation around the target value. The first step towards improving quality is to achieve the population distribution as close to the target value as possible. To accomplish this, Taguchi designed experiments using especially constructed tables known as Orthogonal Arrays (OA). The use of these tables makes the design of experiments very easy and consistent. The Taguchi Method is applied in four steps: 1. Brainstorm the quality characteristics and design parameters important to the product/process. 2. Design and conduct the experiments. 3. Analyse the results to determine the optimum conditions. 196

4 4. Run a confirmatory test using the optimum conditions. 1.4 Machinability The term Machinability is the property of the material which governs the ease or difficulty with which it can be machined under a given set of conditions. In other words, the most machinable metal is one which will permit the fastest removal of the largest amount of material per cut of the tool with satisfactory finish. The operational characteristics of a cutting tool are generally described by its machinability and it has three main aspects, tool life, surface finish and power required to cut. Machinability could also be considered as the ease with which a given material can be machined and it is affected by machine variables like cutting speed, feed and depth of cut, tool form, tool material, cutting fluid, rigidity of machine tools, shape and size of work, nature of engagement of tool with work. For a given set of machine conditions[5], ease of machining is affected by the properties of the work material like hardness, tensile properties, chemical composition, microstructure, degree of cold work and strain hardenability. 1.5 Cutting parameters Cutting Speed Cutting speed, as a variable, has a greatest influence on tool life. Normally, the surface finish improves with the cutting speed. This is mainly due to the continuous reduction of the built-up edge. After the built-up edge becomes insignificant, the surface finish is not improved with further increase in cutting speed Depth of Cut The tool life at a given cutting speed is also influenced by the dimensions of cut, namely, the feed and the depth of cut. Surface finish is also affected to a large extent by the dimensions of cut, especially by the feed rate. With increase in feed rate, the surface finish deteriorates rapidly. Large dimensions of the cut increase the cutting force and deflection and hence deteriorate the surface finish Feed Feed is the motion of the tool parallel to the axis of the work. It is the ratio of distance travelled to the time taken, expressed in mm/min or it is also the distance travelled by the tool per revolution of the workpiece i.e mm/rev. Feed is the most important single factor which affects surface roughness. As the feed increases the quality of the surface roughness decreases and vice versa. 2. EXPERIMENTAL PROCEDURES AND CONDITIONS: In the present study, Al6351and 75 mm long with 25 mm diameter was used as work material for experimentation using a CNC machine. Cutting speed, feed rate and depth of cut were selected as the machining parameters to analyze their effect on surface roughness and work piece surface temperature as well. A total of 31 experiments based on Taguchi s L31 orthogonal array were carried out with different combinations of the levels of the input parameters. 197

5 Fig.1:Cutting forces on work piece Fig.2: Machining of Al 6351 specimen TABLE 1: STANDARD L31 ARRAY FOR 4 FACTORS Trail No. Nose Radius(mm) Feed(mm/min) Speed(rpm) DOC(mm)

6 TABLE 2: STANDARD L31 ARRAY FOR 4 FACTORS WITH VALUES Trail No. Speed (rpm) D O C (mm) Feed (mm/min) Nose Radius(mm)

7 TABLE 3: L31 ARRAY OBSERVATIONS Trial No. Ra - 1 Ra 2 Ra - 3 Ra - 4 Average Ra

8 TABLE.4. GENERAL LINEAR MODEL: RA - 1 VERSUS NOSE RADIUS, FEED, SPEED, DOC Factor Type Levels Values Nose Radius fixed Feed fixed Speed fixed DOC fixed TABLE.5. ANALYSIS OF VARIANCE FOR RA - 1, USING ADJUSTED SS FOR TESTS Source DF Seq SS Adj SS Adj MS F P Nose Radius Feed Speed DOC Error Total S = R-Sq = 32.73% R-Sq(adj) = 20.77% speed Vs Avg Ra Avg Ra

9 Feed Vs Avg Ra Avg Ra Nose Radius Vs Avg Ra Conformation Test The optimum conditions suggested by the analysis is nose radius = 0.2mm, feed = 10 mm/min, speed =1500rpm and DOC = 0.2 mm. However since L16 is a full factorial experiment the above mentioned combination already exists and two trials for the combination has been conducted. Therefore separate confirmation test is not required. 3. CONCLUSIONS Based on the analysis feed is seen to be the most important single factor affecting the surface roughness. Based on the analysis it can be seen that interactions have a very important role to play in the determination of the surface roughness. The interaction between feed and speed is statistically most influential term. Following the interaction between feed and speed the next most statistically important term is again an interaction i.e. interaction between nose radius and feed. Only after the above two terms does the factor feed influence the surface roughness. Again another interaction between nose radius and speed is also found to be influential. Of the 4 statistically verified terms which influence the surface roughness 3 are interactions. This shows that interactions between the factors are in fact more important than any single factor in determination of surface roughness. Although the 2 nd phase has shown that the nose radius in itself is not a very important factor, its interaction with feed and speed is influential. Depth of Cut is shown not to be of very high importance in itself or in interactions. 202

10 REFERENCES: [1]. Douglas C-Wiley Publications, Introduction to Statistical Quality Control, Montgomery, 4 th Edition, [2]. Kromanis, A.; Krizbergs, 3D Surface Roughness Prediction Technique in End- Milling using Regression Analysis, 6 th International DAAAM Baltic Conference, April [3]. Phillip.J.Ross, Taguchi Techniques for Quality Engineering Second Edition, [4]. Karmakar, A. Factors influencing surface finish during fine turning, Proceedings of 4 th All India Machine Tool Design and Research Conference, India, (1970) PP123 [5]. Boothroyd, G. and Knight, W. A. Fundamentals of Machining and Machine Tool Marcel Dekker, New York. (1989) [6]. Selvam, M. S., and Radhakrishnan, V Characteristics of a surface machined with a single point tool. Tribology, 6. (1973): [7]. Ravindra Thamma Comparison between Multiple Regression Models to Study Effect of Turning Parameters on the Surface Roughness Proceedings of the 2008 IAJC-IJME International Conference, ISBN [8]. Chang-Xue Feng and Xianfeng Wang Development of Empirical Models for Surface Roughness Prediction in Finish Turning, International Journal of Advanced Manufacturing Technology, Vol.20, (2002), 52. [9] MTAB india Mr. N B DODDAPATTAR, has received the B.E degree in Mechanical Engineering from Bagalkot Engineering College, Karnataka University, India in 1986, and obtained M.E. degree in Manufacturing Science & Engineering from University Visvesvaraya College of Engineering, Bangalore University, Bangalore, in Currently, he is working as Assistant Professor and Head, RNS Institute of Technology, Bangalore, India. His research interest areas are Quality management, Material characterisation, Composites and CAD/CAM. 203