NIRAJAN PUDASAINI 2VX12MPD15

1. “A Study On Machining Parameters Optimization Of Spheroidal Graphite Iron On A Vertical Machining Center” A Project on: NIRAJAN PUDASAINI 2VX12MPD15 PROF. R R MALAGI PROJECT GUIDE

2. Contents • Design matrix • Sequence of operation • Manufacture of workpiece • Machining • Measurement of SR • Observation table • Results • Conclusion • Future works • Abstract • Vertical milling • About SG iron • Input parameters • Response variables • Objective • Literature review • Problem statement • About DOE • RSM • Box- Behnken design

3. Abstract • This report presents an approach to predicting the surface roughness and material removal rate in milling of spheroidal graphite iron using tungsten carbide insert tool and its optimization by coupling the prediction model with response surface methodology. • In this work, experiments are carried out as per the Box-Behnkendesign and an L13 orthogonal array is used to study the influence of various combinations of process parameters on SR and MRR. • ANOVA test is conducted to determine the significance of each process parameter. Two sets of L13 OA are used each for tool orientation of 45 and 90 degrees. • This work may be useful in selecting optimum values of various process parameters that would maximize the MRR and minimize the SR in machining.

4. Vertical Milling • Machining Centers classified as: • Vertical Machining centers • Horizontal Machining centers • Universal Machining centers • VMC has spindle on vertical axis relative to work table • Used for flat works that require tool access from top • For e.g.: Mould and die cavities, large aircraft components Figure: A Vertical Milling Machine

5. Spheroidal Graphite Iron • Also called ductile iron • Characterized by graphite occurred in microscopic spheroids • Various grades, differed due to matrix (microstructure of metal around the graphite)

6. Cutting parameters • Cutting speed • It is the speed difference (relative velocity) between the cutting tool and the surface of the workpiece. • Cutting speed = π*D*N, D is diameter of workpiece • Feed rate • It is the feed of the tool against the workpiece in distance per time-unit. • FR (mm/min) = RPM * T * CL, T= No. of teeth, CL= Chip load • Depth of cut • It is how deep the tool is under the surface of the material being cut. This will be the height of the chip produced. • dcut = , D and d are initial and final weight of workpiece

7. Response variables • Surface roughness • It is a measure of the level of unevenness of the part's surface. • Measurement procedure • Surface inspection by comparison method • Direct instrument method • Parameters • Ra = arithmetic mean of departures of profile from mean line • Rq, Ry, Rz, Sm are other parameters

8. 2. Material removal rate • It is the volume of material removed divided by the machining time. • MRR can be expressed as the ratio of the difference between the weight of the work piece before and after machining to the machining time. • MRR= (Wb-Wa)/t • Where • Wb= Weight of work piece before machining. • Wa= Weight of work piece after machining. • t = Machining time

9. Objective The objective of this study is to find out the optimum levels for the process parameters so that the surface roughness value will be minimum and rate of material removal will be maximum in a vertical machining center and to check the optimality by developing empirical models.

10. Literature review • Pratyusha J et al. made a study for finding out optimum parameters for milling process using Taguchi methods. L9 array was used, parameters studied were speed, feed and depth of cut. They found that Taguchi method provides a systematic and efficient methodology for searching optimal milling parameters. • R. Suresh et al. made an attempt to analyse the influence of cutting speed, feed rate, DOC and machining time on machinability characteristics like SR and tool wear using RSM. The found that combination of low feed rate, low depth of cut and low machining time with high cutting speed is beneficial for minimizing the machining force and surface roughness

11. Balindersingh et al. carried out experiments for optimization of input parameters in the CNC milling on EN 24 steel. • Taguchi technique used • SR and MRR were response variables, speed, feed and DOC were control parameters • L27 array was used generated from MINITAB V15 • Confirmation runs was used to verify the experiment • Other research on VMC using Taguchi technique were done by Piyush Pandey et al., Avinash A. Thakre, Reddy Sreenivalsu and so on. • Milon D. Selvam et al., R. JaliliSaffar et al. used GA method.

12. Ahmad Hamdan et al. carried out experiments for high speed machining of stainless steel using L9 array and Taguchi method. Results showed a reduction of 25.5% in cutting forces and 41.3% in SR improvement. • NorfadzlanYusup et al. made a comparison of five year researches from 2007 to 2007 that used evolutionary techniques to optimize machining process parameters. They found that SR is mostly studied with GA. • A Kacal and M Gulesin studied optimal cutting condition in finish turning of of ductile iron using Taguchi method. ANOVA was used to identify significant factors affecting SR. They found that feed rate is most significant.

13. Problem statement • In machining operation, the quality of surface finish and the rate of material removal are important requirements. • The choice of optimized cutting parameters is very important for controlling the required surface quality and obtaining the maximum MRR. • In this study, the optimum machining parameters, for vertical milling of SGiron, are to be determined to increase MRR and reduce the SR.

14. DOE technique • Statistical design of experiments refers to the process of planning the experiments so that appropriate data that can be analysed by statistical methods will be collected, resulting in valid and objective conclusions.

15. Guidelines for designing an experiment

16. Response surface methodology • It is a collection of mathematical and statistical techniques useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response. • In Statistics, RSM explores relationship between several explanatory variables and one or more response variables. • Idea is to use a sequence of designed experiments to obtain an optimal solution. • Estimate first-degree polynomial by factorial experiments • Explains which explanatory variables have an impact on response variable of interest. • By Box-behnken method, 2nd degree polynomial model is estimated. • This second degree polynomial can be used to optimize.

17. Box-Behnken design • A useful method for developing second-order response surface models • Based on the construction of balanced incomplete block designs and requires at least three levels for each factor. • Requires only three levels to run an experiment. It is a special 3-level design because it does not contain any points at the vertices of the experiment region.

18. Number of trails and corresponding level • Geometric representation

19. Design matrix • Input parameters with levels

20. Design matrix for 45 degree tool orientation

21. Design matrix for 90 degree tool orientation

22. Sequence of operation

23. Manufacture of workpieces • Using a sand casting method. • 26 sets of workpieces were manufactured • Made up of SG Iron

24. Machining • Performed at a vertical machining center, Shradha enterprises, Udyambag, Belgaum • The machine used is TAKUMI MCV-1000 model Takumi MCV- 1000

25. Machining procedure Clamping of workpiece Milling cutter Machining with coolant

26. Machining outputs

27. Measurement of Surface roughness • Surface roughness measurement is done using the Surtronic 3+ device available at metrology laboratory of Gogte Institute of Technolgy, Belgaum.

28. Observation table • Observation table for 45 degree tool orientation

29. Observation table for 90 degree tool orientation

30. Results and Discussions • To find out which factors among the speed, feed and DOC is significant in increasing MRR and reducing the SR and at what level • Response surface analysis using MINITAB v16 • ANOVA to check adequacy of model • Confidence interval = 85 % • Only terms whose p < 0.15 is used to develop empirical model • Analysis done using coded units, so, empirical equation generated are expressed in coded units

31. Analysis of response for 45 degree tool orientation • Regression analysis for MRR

32. Analysis of Variance for MRR

33. Empirical model for MRR MRR = 0.090 + 0.060250* feed +0.013583* DOC+ 0.024993* speed* speed + 0.063085* speed * DOC

34. Main effect plot for MRR at 45 degree insert orientation

35. Interaction effect

36. Optimum machining parameters

37. Regression analysis for surface roughness for insert at 45 degree

38. Analysis of Variance for SR

39. Empirical model for SR SR = 5.7 – 0.36125 * feed – 0.7925 * speed +0.38750 *feed* speed

40. Main effect plot for SR at 45 degree insert orientation

41. Interaction effect

42. Optimum machining parameters

43. Analysis of response for 90 degree tool orientation • Regression analysis for MRR

44. Analysis of Variance for MRR

45. Empirical model for MRR MRR = 0.0865 + 0.044588 * feed + 0.03512 * DOC +0.05825 *speed * DOC

46. Main effect plot for MRR at 90 degree insert orientation

47. Interaction effect

48. Optimum machining parameters

49. Regression analysis for surface roughness

50. Analysis of Variance for SR