Analysis of Variance for Surface Roughness Produced During Single Point Incremental Forming Process

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1 Analysis of Variance for Surface Roughness Produced During Single Point Incremental Forming Process Jigar R. Patel, Kaustubh S. Samvatsar, Haresh P. Prajapati, Umang M. Sharma Production Engineering Department, Birla Vishvakarma Mahavidyalaya, Anand, Gujarat, India Abstract: Attaining required surface roughness is predominantly important in case of any sheet metal component. Single point incremental forming (SPIF) is one of such processes used fo r stepwise forming of sheet component directed and controlled by a pre-defined computer aided program for obtaining desired geometry. To investigate the significance of design or input parameters on the characteristic and generating output of the response variables, analysis of variance (ANOVA) has been performed. It also gives a clear depiction of the contribution of parameters on generating the required response. Thus, a campaign of experiments with its corresponding statistical analysis has been carried. Response surface methodology is successfully applied for analyzing the effect of variation in process parameters like spindle speed variation, wall angle or incremental step on surface roughness. By this attempt, augmentation in the possibility of improved combination of process parameters can prolifically conduce to minimum surface roughness. Keywords: Sheet metal forming, surface roughness, SPIF, DOE, ANOVA. I. INTRODUCTION Single Point Incremental Forming (SPIF) process is provides its versatility and suitability for manufacturing sheet metal component of job and batch type governed by specifically defined computer aided manufacturing (CAM) program to form symmetrical parts or parts with complex geometry. The single point incremental forming process is based on principle of layered manufacturing, where division of model is done in the form of horizontal profiles by generating a tool path. The numerically controlled (NC) tool path is prepared using contours of the profiles required. SPIF process can be conducted in any numerically controlled 3-axis machine. Indeed, one of the most frequent resources used for SPIF is a CNC milling machine because it is available in most workshops, meaning that it is not necessary to make a large investment in machinery.[5] For controlling the quality of the product, surface roughness is a crucial factor. Ra value is the arithmetic mean of the roughness value of determined from the deviations about the centre line within the evaluation length of the work-piece. Using sophisticated surface roughness testers, Ra value can be detected easily. There are several factors which affect the generation of surface roughness during SPIF process. Hence, major factors with its sub factors have been depicted in Fig. 1 as follows. Hence, a fixture can be designed to hold the work-piece firmly and appropriate tool can be done for the forming process. A systematic approach can provide a unique and a well defined procedure to be followed for the process selection and its design. Appropriate vibration damping arrangement must be ensured. Machining parameters can be well adjusted as per the design of experiments. To verify the adequacy and agreeability of designed model, a corroborating evaluation has been performed. [16] Available 90

2 Figure 1. Factors affecting surface roughness during SPIF II. METHOD AND MATERIALS Besides the forming tool, a flexible clamping system has been designed to for arrangement SPIF set-up on a vertical milling machine.[6] The SPIF tool is made up of Stainless Steel (SS-304) having hardness 62 HRC and has a hemispherical tip of diameter 14 mm. To maintain the co-axiality, tool is held in a tool holder. Material used for work-piece is IS (an Aluminium alloy). The properties of work-piece are as shown in table 1. Figure 2. Experimental Set-up Table 1. Mechanical Properties of work-piece Mechanical Properties of Tempered Alloy IS Tensile strength 13,000 psi Yield strength (offset= 0.2%) 5,000 psi Elongation, in 2 in. Sheet specimen (1/16 in. Thick) 35% Shearing strength (psi) 9000 psi Weights per cm 3 Electrical conductivity compared to Cu standard 59% Specific gravity 2.71 Brinell hardness 500-kg load 10 mm ball 23 Table 2. Chemical Composition of work-piece Nominal Composition in % Alloy Code Mn Zn Cr Cu Si Al IS Available 91

3 III. EXPERIMENTAL PROCEDURE A. Design of Experiments A methodical approach is essential for planning and conduction of experiments so that useful consequences can be obtained. In this case, planning of experiments is done by using Taguchi technique so that minimum number of experiments has to be conducted. Appropriate data collection can result in the arrival at the most effective conclusions. Design of Experiments helps in the reduction of total number of experiments to be conducted and uniform data distribution can be obtained over the range of controllable factors for investigation. Also, establishing a correlation between different input variables becomes easier. Analysis of variance for Surface roughness along tool path and Surface roughness across tool path is carried out using statistical software for experimental. To analyze the experiments have been conducted considering full factorial design with four controllable input factors mainly spindle speed, feed, incremental depth and wall angle for application of grease and lubricating oil under at three different levels. Table 3. Orthogonal Array L9 for Taguchi Expt. No. Input 1 Input 2 Input Input 4 Table 4. Control Parameters and their Control Levels Level Spindle Speed (rpm) B. Surface Roughness Measurement Feed (mm/min) Incremental Depth (mm) Wall Angle (Degree ) Figure 3. Surface Roughness Measuring Instrument Based on the randomized experiments with designed input variables, the obtained surface roughness readings have been observed as follows. The Surface roughness values obtained in terms of micron (i.e. Available 92

4 μm) as shown in table 6 by using portable surface roughness tester "Hommel Tester LV 15". Expt. No. Spindle Speed (rpm) Feed (mm/min) Table 5. Dimensions of Sample Specimen Length Width Thickness 40mm 20mm 0.9mm Table 6. Observed Values of Surface Roughness Incremental Depth (mm) Wall Angle (Degree ) Ra (μm) Along Tool path Ra (μm) Across Tool path Ra (μm) Along Tool path Ra (μm) Across Tool path Grease Lubricating Oil Table 6 shows the values of surface roughness observed from sample specimens. From the observed values of surface roughness, graphical representations have been generated for analysis. Fig. 4 and Fig. 5 show the surface roughness obtained using grease and appropriate lubricating oil. Figure 4. Surface Roughness during use of Grease Figure 5. Surface Roughness during use of Lubricating Oil Available 93

5 IV. ANALYSIS OF VARIANCE An analysis of variance (ANOVA) is basically then carried out to compare the mean values of properties deduced from these tests. General linear model ANOVA technique is used for determining level of significance for individual parameters effect as well as interaction effect of combination of input parameters. Factor Table 7. ANOVA table for Surface Roughness Degree of Freedom Sum of Squares Mean of Squares F-Value P-Value % Contribution Spindle speed Feed Step size Wall angle Lubricating Condition Error Total Fig. 6 and Fig. 7 show the main effect plot for surface roughness generated during SPIF process along the tool path and across the tool path respectively. Figure 6. Significance of Surface Roughness along Tool Path Available 94

6 Figure 7. Significance of Surface Roughness across Tool Path Also, from the analysis interaction plot can be represented considering all the influencing factors. Fig. 8 and Fig. 9 depict the interaction plot for surface roughness along tool path and across tool path respectively. 1) Interaction Plot for surface roughness along Tool Path Figure 8. Interaction Plot for surface roughness across Tool Path V. RESULTS AND DISCUSSIONS The ANOVA analyses presented in this paper depicts a statistical evidence of significance spindle speed, feed, incremental depth and wall angle in SPIF process. The computational analyses demonstrates that during the measurement of surface roughness along the tool path, the input factors which affect the most are feed, incremental depth and wall angle. Likewise, during the measurement of Available 95

7 surface roughness along the tool path, the input factors which affect the most are depth, spindle speed and incremental depth. From the values of ANOVA, is the most significant factor which contributes to the surface roughness during SPIF process in this case is wall angle i.e % is contributed by wall angle. The second and third factors which contribute are the spindle speed and feed having % and % respectively. Also, lubricating condition contributes greatly for surface roughness. Step size contributes least as per the analysis performed. VI. CONCLUSION The analysis elaborates the importance and significance of the input factors on the desired output. It can help the factors that can be controlled for achieving the best parameters for development of such a model that assures effectively confirmed quality control. Extensions of the analysis by identifiable consideration of optimum conditions for the response can be efficaciously applied. The validation of this analysis shows that more emphasis must be exercised on spindle speed, wall angle, feed along with presence of most felicitous lubricant. Also, implementation of a strategy suitable for mass production can be easily facilitated. REFERENCES [1] Aruna M., Dhanalakshmi V., Response surface methodology in finish turning INCONEL 718, International Journal of Engineering Science and Technology, Vol. 2, Issue 9, pp , [2] Asiltürk I., Çunkas M., Modeling prediction of surface roughness in turning operations using artificial neural network and multiple regression method, Expert Systems with Applications, Vol. 38, Issue 5, pp , [3] Bradley C., Automated Surface Roughness Measurement, International Journal of Advanced Manufacturing Technology, Vol. 16, Issue 9, pp , [4] Palanikumar, L. Karunamoorthy, R. Krathikeyan, Assessment of factors influencing surface roughness on the machining of glass reinforced polymer composites, Journal of Materials and Design, Vol. 27 pp , [5] Hardik S Beravala, Jigar R Patel, Haresh P Prajapati, Rakesh S Barot, Setup Development and Feasibility Check of Single Point Incremental Forming (SPIF) Process on VMC for Al Alloy International Journal of Engineering Trends and Technology, Vol. 20 Issue 4, pp , Feb [6] Jigar Patel, Shyam Rangrej, Haresh prajapati, Umang Sharma, Design and Analysis of Flexible Fixture for Single Point Incremental Forming Process, International Journal of Advance Research In Engineering, Science & Technology, Vol. 2, Issue 5, pp , May [7] Ghulam Hussain, Gao Lin, Nasir Hayat, Improving profile accuracy in SPIF process through statistical optimization of forming parameters, Journal of Mechanical Science and Technology, Vol. 25, Issue 1, pp , [8] Jigar R. Patel, Kaustubh S. Samvatsar, Haresh P. Prajapati, Shyam S. Rangrej, Optimization of Process Parameters for Reducing Surface Roughness Produced During Single Point Incremental Forming Process, International Journal on Recent Technologies in Mechanical and Electrical Engineering, Vol. 2 Issue 9, pp , Sept [9] V.Mugendiran, A.Gnanavelbabu, R.Ramadoss, Parameter optimization for surface roughness and wall thickness on AA5052 Aluminium alloy by incremental forming using response surface methodology, 12th Global Congress on Manufacturing and Management, Procedia Engineering, Vol. 97, pp , Available 96

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