Response Surface Methodology in the Study of Induced Machining Vibration and Work Surface Roughness in the Turning of 41Cr4 Alloy Steel

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1 Website: (ISSN 50-59, ISO 900:008 Certified Journal, Volume 3, Issue, December 03) Response Surface Methodology in the Study of Induced Machining Vibration and Work Surface Roughness in the Turning of Cr Alloy Steel C. O. Izelu, S. C. Eze, B. U. Oreko 3, B. A. Edward, D. K. Garba 5 Senior Lecturer, Lecturer, Department of Mechanical Engineering, COT, FUPRE, Effurun, Delta State, Nigeria PG Student, 5 Lecturer, Department of Mechanical Engineering, FOE, NDA, Kaduna, Kaduna State, Nigeria 3 Lecturer, Department of Welding Engineering and Offshore Technology, PTI, Effurun, Delta State, Nigeria Abstract In this research work, induced vibrations and surface roughness of a tool-work-piece system in a turning process and their effect on product quality are examined. Attempts have been made to use vibration signals in predicting surface quality of product from lathe machine operation using response surface methodology. The surface roughness of machined parts is also predicted using the response surface methodology. The data are generated by lathe turning of Cr Alloy steel samples at different levels of low, medium and high of selected machining parameters. Prediction equations for induced vibration and surface roughness for Cr alloy steel was generated. From the experimental results, it shows that increasing the acceleration of cutting tool with increase in the tool nose radius will cause an increase in surface roughness of work piece if induced vibration in the system is significant. This effect interacts with other independent variables such as depth of cut and cutting speed. Keyword Response surface methodology, induced vibration, surface roughness, alloy steel, Depth of cut, cutting speed and tool nose radius I. INTRODUCTION Machining Vibrations is a recurring problem which affects the quality of machining process particularly product surface quality. Machining vibrations are induced by the deformation of work piece during metal cutting process like turning, drilling, boring and milling (Thomas L. et al, []). Machine vibration exists throughout cutting operation which can be either forced vibrations as a result of forces created by unbalanced machine components, misalignment, bad gearing, electric motor and feed pump or Chatter caused by interaction between machine structure and chip removal process. Chatter also known as self exited vibration is associated with the machined surface roughness (Luke H. et al, []). Surface quality is of great importance for the functional behaviour of machined components and is generally associated with surface roughness. II. The surface roughness of machined components is known to have considerable effect on some material properties such as wear resistance and fatigue strength (Sahin Y. M. [3]). Several factors were said to affect the quality of surface finish in turning operations. These factors, referred to as cutting conditions include material properties of both work piece and cutting tool, depth of cut, cutting speed, feed rate and tool geometry, were reported as having significant effect on the quality of machined parts. Response Surface methodology (RSM) is being increasingly used in the industry. RSM are designs and models for working with continuous treatment when the optimal response is the goal (Oclert, G. W []). Several researches have been conducted to predict surface quality using various machining process parameters during turning of different materials. Kapac and Bahor [5] have examined the changes in surface roughness of AISI 060 and AISI 0 and analyze the effect of cutting parameters by using RSM. Sahin Y. M. [6]) has used RSM to model the surface roughness of machined hardened AISI 00 steel using triangular and square tools. Mansour et al [7]) have studied surface roughness utilizing RSM for milling steel in dry condition. Ozel and Karpat [8]) have used regression analysis to predict Artificial Neutral Network (ANN) for predictive modeling of surface roughness and tool wear in turning. Nalbant et al [9] have examined Taguchi method in optimizing cutting for surface roughness in turning. Choudhury and El-Baradie [0] have developed surface roughness prediction model in turning high strength steel by factorial design of experiments. In this study RSM is used in the study of induced machining vibrations and work surface roughness in the turning of Cr alloy steel. MATERIALS, EQUIPMENT AND METHOD OF ANALYSIS The following materials and equipment are used to perform the experiment. 3

2 Website: (ISSN 50-59, ISO 900:008 Certified Journal, Volume 3, Issue, December 03) Work-piece type Cr alloy steel round bar shape, of 5Ø x 50mm with the following specifications: the UTS of 90.83N/mmm ; the BHN of and Chemical composition of 0.%C, 0.5 S, 0.65 Mn and.0 Cr (Fig ). Lathe Machine with carbide tool of F30 Type, dimension of 5 x 5 x.5mm and HSS-78 tool type of 0º back rake angle, º side rake angle, 5º side relief angle and 5º side cutting edge angle (Fig ). Tables and summarize the hardness, tensile test and the chemical composition of the Cr alloy steel. Figure : Lathe machine with carbide (F30 type) cutting tool Figure : Cr alloy steel bars Table I Hardness Test and Tensile Test of cr Alloy Steel. Sample ID Diameter (mm) Area BHN Peak Load UTS (Mm ) (Kn) N/Mm Solid Round Table II Results of Chemical Analysis of cr Alloy Steel Average Elements % Quality Of Material Type of Material Carbon (C) Silicon (Si) Manganese (Mn) Chromium (Cr) Quenched and Tempered Steel Cr A. Induced Machining Vibration Self-excited or self- induced vibration, which is also known as chatter, is the basis of the vibration phenomenon under consideration. It is caused by the interaction of the chip removal process and the structure of the machine tool that results in disturbances within the cutting zone. Chatter always indicates defects on the machined surface. Vibration especially self-excited vibration is associated with the machined surface roughness. A Vibration Meter, type 908Be series (Fig 3), was used for measuring the amplitude and velocity of a point on the cutting tool. The tool vibration level was measured using a vertical data of a transducer mounted near to the tip and connected to the devices. The data include a displacement and velocity of the indicated point on the tool for each sample. The acceleration was calculated by using general equation between the amplitude and acceleration. Figure 3: A Vibration Meter type 908BE series B. Work Surface Roughness An inside surface Roughness tester, ISR-6 type instrument (Fig ) was used for the measurement of surface roughness.

3 Website: (ISSN 50-59, ISO 900:008 Certified Journal, Volume 3, Issue, December 03) Three different positions for each sample at 0º with each other and the average of the three reading are considered as surface texture of the turned surface, such as given in Fig 5. Figure : An In-size Surface Roughness Tester, Type ISR-6 series C. Response Surface Method A factorial design was chosen, so that different interactions between independent variables could be effectively investigated. The independent variables considered in the study are dept of cut, cutting speed, tool nose radius. The dependent variables are the resulting first cut surface finish and the acceleration in both radial and feed directions. The levels of the independent variables are as shown in Table 3 while the level of constants is given in Table. In order to minimize the effect of tool wear, which could affect the surface roughness quality, the tool was changed after cuts. The newly installed tool was run for a few machining times to eliminate rapid tool wear. Figure 5: Machined Surfaces Table III Levels of Independent Variables Levels S/No Factor Design Design Of Exp. Notation Symbol Unit Low Medium High Depth of cut F T mm Cutting speed F N rpm Feed rate F 3 F mm/rev Tool nose radius F R mm 0 5 Tool overhang F 5 h t mm Work-piece overhang F 6 h w mm Table IV Constant Variables S/No Parameter Level Unit Feed rate (F 3 ) 0.5 mm/rev Tool overhang (F 5 ) 50 mm 3 Work-piece overhang (F 6 ) 80 mm III. RESULTS AND DISCUSSION Tables 3 and show the cutting process parameters and the variables that are kept constant. Table 5 shows the results of the input and response parameters using the third composite factorial, and the predicted response parameters using the response surface methodology. 5

4 Website: (ISSN 50-59, ISO 900:008 Certified Journal, Volume 3, Issue, December 03) Experimental Run Table V Third Composite Factorial Design (RSM) Factor Induced Vibration Amplitude Surface Quality F F F Acceleration Predicted Ra Value (mm) Predicted Response Surface Methodology of the second order and composite factorial design are used to evaluate the effect of induced vibration amplitude on the surface quality as in shown in table 5. The design of experiment took into consideration twenty-seven (7) experimental run consisting of three (3) levels including low, medium, high (Table 3) of the following independent variable: depth of cut F (.0,.0, 3.0 ) mm, cutting speed F (60, 30, 00) rpm and tool nose radius F (0,, ) mm, while feed rate F 3 (0.5) mm/rev, tool overhanging F 5 (50) mm and workpiece overhang F 6 (80) mm were kept constant at the same values (Table ), to produce different response parameters as shown in Table (5). Prediction equations for induced vibration and surface roughness were generated using the response surface methodology, and respectively, given as: A F, F, F Ra F, F, F F 0.06F 0.09F 0.00F F 0.03F F 0.09F 0.300F 0.57F F 0.083F 0.09F F 0.99F F 0.09F 0.0F F.053F.5F.0F F 0.669F () () 6

5 Website: (ISSN 50-59, ISO 900:008 Certified Journal, Volume 3, Issue, December 03) The relationship between depth of cut, Cutting speed and tool nose radius (F ) with induced vibration and surface roughness of work-piece for cutting condition is plotted in Figs. 6 and 7, respectively. The actual amplitude of the induced vibrations between tool and work-piece were measured using a displacement sensor. Figure 6 and 7 has been constructed to illustrate the main effect of induced vibration on surface roughness of work piece using data generated by evaluating equations and. From the experimental results, it is shown that the acceleration of the cutting tool has significant effect on the surface roughness of the work-piece. It was observed that increasing the acceleration of cutting tool with increase in the tool nose radius will cause an increase in surface roughness of workpiece if induced vibration in the system is significant. It is also shown that the surface roughness of work-piece is directly proportional to the cutting tool acceleration. Hence, vibration, especially self-excited vibration, is associated with the machined surface roughness. Figure 6: Response Surface for Induced Vibration Figure 7: Response Surface for Surface Roughness IV. CONCLUSIONS In this research work, induced vibrations and surface roughness of a tool-work-piece system in a turning process were examined. Attempts have been made to use vibration signals in predicting surface quality of product from lathe machine operation using response surface methodology. The surface roughness of machined parts is predicted using the response surface methodology. From the study it is shown that induced vibration has significant effect on surface roughness of work-piece. The surface roughness of work piece is directly proportional to cutting tool acceleration. This effect interacts with other independent variables such as depth of cut, cutting speed and tool nose radius. REFERENCES [] Thomas L. Lago, Sven Olsson, L. Hakanson and I. Claesson, 00, Performance of Chatter Control System for Turning and Boring Applications, th GRACM Congress on Computational Mechanics, GRACM., Patras 7-9 June, PP.3- [] Luke H. Huang, J.C. Chen, 00, A multiple Regression Model to Predict In-Process Surface Roughness in Turning Operations Via Accelerometer, Journal of Industrial Technology, Vol 7, No., PP. -7. [3] Sahin Y. Motorcu A. R., 005, Surface Roughness Model for Machining Mild Steel with coated carbide tool, Materials and Design 6, PP [] Oclert, Gary W., 000, Design and Analysis of Experiments: Response Surface Design, W.H. Freeman and Sons, New York. [5] Kalpac J. and Bahor M., 999, Interaction of Technological History of a work piesce material and machining parameters on the desired qualityof surface roughness of a product, Journal of Materials Processing Technology, 5, PP [6] Sahin Y. Motorcu A. R., 008, Surface Roughness model in Machining hardened steel with cubic boron nitride tool, International Journal of refractory Metals and Hard Materials, 6, PP [7] Mansour A, Abdalla H, and Meche F., 00, Surface Roughness model for end milling a semi free cutting carbon caschardening steel (EN3) in dry condition, Journal of Materials Processing Technology, PP [8] Ozel T. and Karfat Y., 005, Predictive modeling of Surface roughness and tool Wear in hard turning using regression and neutral network, International Journal of Machine tools and Manufacture, 5 PP [9] Nalbant M, Gokkaya H, and Sur G, Applicacation of Taguchi method in optimizing cutting parameters for surface roughness in turning, Journal of Materials and design (in Press) [0] Choudhry I.A. and El-Baradie M.A., 997, Surface Roughness Prediction in the turning of high strength steelby factorial design in experiments of turning process, Journal of Materials Processing Technology, 67 PP