COMPOSITE MATERIAL - COMPOSITE MATERIAL.pptx

COMPOSITE MATERIAL

1. Empirical Modelling and Optimization of Process Parameters in Turning of Polycarbonate Rods Sathyanathan M, Associate Professor /Department of Mechanical Engineering, Knowledge Institute of Technology,salem-637 504 Tamil Nadu-India.

2. INTRODUCTION LITERATURE REVIEW DESIGN IMPROVEMENT DRAWING FORMULA TABULATION RESULTS & DISCUSSION CONCLUSIONS CONTENT

3. Turning is an engineering machining process in which a cutting tool, typically a non-rotary tool bit, describes a helical tool path by moving more or less linearly while the work piece rotates. A work piece such as this which is relatively short compared to its diameter is stiff enough that we can safely turn it in the three jaw chuck without supporting the free end of the work. INTRODUCTION

4. To determine the optimal process parameters (speed, feed, depth of cut) in Turning Operation using Response Surface Method & Genetic Algorithm Method To attain high MRR with Less Surface Roughness Response: Surface Roughness Material Removal Rate (MRR) OBJECTIVES

5. S.NO AUTHOR TITLE MATERIAL METHOD RESULT 1. SURENDRA KUMAR SAINI (2014) Optimization of Multi-objective Response During CNC Turning Using Taguchi-Fuzzy Application Aluminum 8011 Alloy Taguchi Method Spindle Speed Rate give Good Surface Finish.  2. KALTRINE JAKUPI (2015) Effect of Machining Parameters and Machining Time on Surface Roughness in Dry Turning Process Cold Rolled Steel C62D Taguchi Method Increasing of Nose Radius with the Lowest Feed Rate and Cutting Time LITERATURE REVIEW

6. S.NO AUTHOR TITLE MATERIAL METHOD RESULT 3 SUNIL DAMBHARE (2015) SUSTAINABILITY ISSUES IN TURNING PROCESS:A STUDY IN INDIAN MACHINING INDUSTRY AISI 1040 Carbon Steel ANOVA Method The values of R^2=98.05% & R^2( adj )=94.36% Reveal Significance of the Model 4 S.K.CHOUDHURY (2014) Evaluation of Chip-tool Interface Temperature: Effect of Tool Coating and Cutting Parameters During Turning Hardened AISI 4340 Steel Hardened AISI 4340 Steel Surface Response Methodology Feed has Variable Effect on Surface Roughness Cutting Speed and Depth of Cut an Approximately Decreasing in trend

7. S.NO AUTHOR TITLE MATERIAL METHOD RESULT 5 MARCO SORTINIO (2014) Robust Analysis of Stability in Internal Turning INCONEL 718 Minitab software , CNC turning lathe , Ansys 6.1 By this method cutting speed-79.99 m/min , feed rate-0.25mm/rev , depth of cut-0.1mm , best fitness value-2122.23mm3/min 6 PIOTR KISZKA (2014) Comparison of Surface Textures Generated in Hard Turning & Grinding Operation 41cr4 steel Taguchi method The Importance in Permitting High Quality & Functional Properties Required for Turning & Grinding Operation

8. S.NO AUTHOR TITLE MATERIAL METHOD RESULT 7 K.PALANIKUMAR (2013) Investigation on The Turning Parameters for Surface Roughness Using Taguchi Analysis CFRP Material Taguchi Method Surface Roughness=2.7572-(0.0034*Cutting Speed) 8 ROLF MAHNKEN (2015) A Novel Finite Element Approach to Modeling Hard Turning in due consideration of the viscoplastic Assymetry Effect Polyethylene Finite Element Method Cutting Speed –213.88m/min Feed rate – 0.049mm/rev Depth of cut – 2mm Tool nose radius – 0.8mm

9. S.NO AUTHOR TITLE MATERIAL METHOD RESULT 9 A.DEL PRETE (2013) SUPER-NICKEL Orthogonal Turning Operations Optimization Super Nickel Alloys Response Surface Roughness Method The Average Error Deducted Ranged From 3.5% of RSM. 10 G.URBICAIN(2013) Stability Prediction Maps in Turning of Difficult –to-Cut Materials Nickel Based Alloy Chebyshev collocation method Vibration Frequency of 1870[ hz ] for f=0.1mm/rev

10. Manufacturing industries are crucial in a country’s economy. The effect of process parameter (Speed / Feed / Depth of Cut, Surface Finish, MRR),the Machining Environment (dry / wet) and the type of cutting tool on the response was observed. Analysis of Variance ( ANOVA ) and Surface Roughness method was applied to test the data. SUMMARY

11. SCOPE OF THE PAPER To determine the process parameters such as Speed, Feed Rate, Depth Of Cut in Turning To attain Maximum material removal with Less Surface Roughness. Response Made In Surface Roughness And Cutting Force Using ANOVA Method ,Response Surface Method

12. Venue: Manufacturing Technology Lab. Operation: Turning Operation. Material: HYLAM & DERLIN DETAILS REGARDING MACHINING PROCESS

13. DRAWING Do-Di/2 = Depth of Cut L+2L = Length of Tool Feed rate =L+2L/rev FORMULA

14. The selection of optimal cutting parameters is a very important issue for every machining process in order to enhance the quality of machining product & reduce the machining costs. At present, the cutting parameters (Spindle speed, Feed rate, Depth of cut) have to optimized for better expected results. Upon analysis, it is a fact that, the machining of the material is yet to be decided PROBLEM IDENTIFICATION

15. Response Surface Methodology ANOVA Analysis METHODOLOGIES

16. EXPERIMENTAL INVESTIGATION: The machining experiments were conducted using the Design of Experiments (D.O.E.) approach. The input parameters—Spindle Speed (N), Feed Rate (f), and Depth of Cut (d)—were tested at various levels. A total of 27 experiments were performed on work pieces with dimensions of ᴓ20 x 100mm .

17. SPINDLE SPEED S3=320.45 rpm (S3=Low speed). S2=543.6 rpm (S2=Medium speed). S1=830.32 rpm (S1=High speed). INPUT PARAMETERS

18. S.No . SPINDLE SPEED S1 FEED RATE D.O.C Work Piece weight M\c Time(sec) TEMP(*C) FORCE(KGF) SURFACE FINISH(RA) BEFORE M\C AFTER M\C FX FY Fz 1 S3 1 1 0.067 0.064 38.97 41.2 1 1 2 0.49 2 S2 1 1 0.053 0.051 24.95 42.4 13 2 5 0.49 3 S1 1 1 0.041 0.040 18.13 40.6 12 2 6 0.44 4 S1 1.25 1 0.067 0.064 14.27 38.2 -1 -1 2 0.79 5 S2 1.25 1 0.051 0.048 25.46 39.6 1 2 1.49 6 S3 1.25 1 0.036 0.052 30.12 40.4 12 7 2 4.10 7 S3 1.5 1 0.072 0.069 33.73 43.2 1 4 1 2.59 8 S2 1.5 1 0.056 0.054 25.83 43.2 14 4 1 3.50 9 S1 1.5 1 0.042 0.039 13.15 43.28 2 2 -1 0.49 Tabulation 1 ( Derlin Material )

19. S.NO SPINDLE SPEED FEED RATE D.O.C WORKPIECE WEIGHT M/C TIME(SEC) TEMP(‘C) FORCE(KGF) SURFACE FINISH (Ra) BEFORE M/C AFTER M/C Fx Fy Fz 1 S3 1 2 0.072 0.067 38.50 41.6 1 1 2 0.43 2 S2 1 2 0.058 0.053 25.42 44.0 10 4 2 0.60 3 S1 1 2 0.044 0.041 17.84 44.6 12 7 2 0.48 4 S1 1.25 2 0.071 0.067 28.76 40.6 -1 -1 2 0.34 5 S2 1.25 2 0.057 0.051 21.30 42.4 3 4 1 1.84 6 S3 1.25 2 0.040 0.036 32.08 41.4 9 6 2 1.12 7 S3 1.5 2 0.078 0.072 31.70 46.6 1 2 1 3.47 8 S2 1.5 2 0.060 0.056 25.42 43.8 12 7 1 2.77 9 S1 1.5 2 0.046 0.042 11.53 42.0 1 3 5.37 Tabulation 2

20. SNO SPINDLE SPEED FEED RATE D.O.C W/P WEIGHT M/C TIME (SEC) TEMP (‘C) FORCE( kgf ) SURFACE FINISH BEFORE M/C AFTER M/C Fx Fy Fz 1 S3 1 3 0.080 0.072 37.90 43.48 2 2 1.30 2 S2 1 3 0.064 0.058 25.25 43.6 1 -1 2 0.66 3 S1 1 3 0.051 0.049 17.37 40.8 6 2 2 0.85 4 S1 1.25 3 0.079 0.071 25.80 39.6 -1 -1 2 0.34 5 S2 1.25 3 0.064 0.057 20.68 47.1 1 3 0.17 6 S3 1.25 3 0.048 0.043 28.12 37.5 1 3 1 2.47 7 S3 1.5 3 0.087 0.078 31.49 39.6 1 3 1.33 8 S2 1.5 3 0.060 0.060 30.58 42.6 1 3 1 2.56 9 S1 1.5 3 0.046 0.046 15.26 41.8 1 3 4.34 Tabulation 3

21. S.NO SPINDLE SPEED FEED RATE D.O.C W/P WEIGHT M/C TIME(SEC) TEMP(‘C) FORCE( kgf ) SURFACE FINISH(Ra) BEFORE M/C AFTER M/C Fx Fy Fz 1 S1 1.5 1 0.055 0.053 12.98 41.6 2 2 1.88 2 S2 1.5 1 0.046 0.041 13.48 40.2 2 2 0.61 3 S3 1.5 1 0.033 0.029 33.42 41.6 2 2 3.50 4 S3 1.25 1 0.055 0.053 29.79 38.4 2 2 2.88 5 S2 1.25 1 0.049 0.047 21.10 39.9 2 2 0.49 6 S1 1.25 1 0.040 0.039 12.66 41.2 2 2 0.97 7 S1 1 1 0.050 0.048 13.93 40.4 2 2 0.57 8 S2 1 1 0.041 0.040 22.86 37 2 2 1.91 9 S3 1 1 0.034 0.031 33.16 37.8 2 2 0.86 Tabulation 1( Hylam Material)

22. S.NO SPINDLE SPEED FEED RATE D.O.C W/P WEIGHT M/C TIME (SEC) TEMP (‘C) FORCE( kgf ) SURFACE FINISH(Ra) BEFORE M/C AFTER M/C Fx Fy Fz 1 S1 1.5 2 0.053 0.050 12.64 42.2 3 5 0.92 2 S2 1.5 2 0.041 0.038 19.03 44.2 2 5 -1 3.76 3 S3 1.5 2 0.029 0.027 30.01 44.0 3 6 1 3.48 4 S3 1.25 2 0.053 0.050 27.14 42.0 3 3 -1 2.85 5 S2 1.25 2 0.047 0.043 19.92 41.6 3 5 1.23 6 S1 1.25 2 0.039 0.037 13.32 43.2 3 4 2.16 7 S1 1 2 0.048 0.044 12.92 44.18 3 5 -1 0.65 8 S2 1 2 0.039 0.035 22.46 44.18 3 5 -1 3.89 9 S3 1 2 0.031 0.030 33.65 37.2 4 5 -2 2.02 Tabulation 2

23. S.NO SPINDLE SPEED FEED RATE D.O.C W/P WEIGHT M/C TIME (sec) TEMP ( Deg.C ) FORCE(kgf) SURFACE FINISH (Ra) BEFORE M/C AFTER M/C Fx Fy Fz 1 S1 1.5 3 0.050 0.046 13.60 46.6 4 8 -1 3.38 2 S2 1.5 3 0.038 0.033 24.41 44.4 5 9 -1 3.76 3 S3 1.5 3 0.027 0.023 30.35 47.2 4 9 -1 3.45 4 S3 1.25 3 0.050 0.049 27.66 48 4 8 -1 4.07 5 S2 1.25 3 0.043 0.040 19.77 44.2 5 8 0.49 6 S1 1.25 3 0.037 0.035 12.26 41.6 5 7 -1 3.38 7 S1 1 3 0.044 0.041 11.83 41.6 5 6 -1 1.83 8 S2 1 3 0.035 0.034 22.67 46.4 5 6 -1 5.97 9 S3 1 3 0.030 0.029 32.79 35.8 3 5 -1 0.45 Tabulation 3

24. MEASURING INSTRUMENTS 1. Lathe Tool Force Indicator: Cutting Force is measured Capacity X, Y, Z - Force 500kg Connected to Lathe Tool Dynamometer

25. MEASURING INSTRUMENTS 2. Infrared Thermometer: Indirect contact Accurate point of measurement Degree C or F (Max upto 2000 degree C)

26. MEASURING INSTRUMENTS 3 . Surface roughness testing device: Measures roughness in micrometre. LCD display Suitable for surface including cylindrical & sloping surfaces

27. MEASURING INSTRUMENTS 4. Weighing Machine: Upto 75Kg Industrial usage Weighing scale type: Platform scale type

28. HYLAM BEFORE MACHINING:

29. HYLAM AFTER MACHINING:

30. DERLIN MATERIAL BEFORE MACHINING:

31. AFTER MACHINIG

32. DURING MACHINING PROCESS

33. S.NO CHARACTERISTICS DESCRIPTION 1. Control Type Conventional 2. Spindle Orientation Horizontal 3. Number of axes 3 4. Diameter 356 (mm) 5. Spindle Speed Maximum: 546 RPM Minimum: 30 RPM 6. Chuck used 3 Jaw chuck 7. Lathe bed material Cast Iron LATHE MACHINE SPECIFICATION

34. Operation : Turning Operation Material : Hylam & Derlin Material Diameter: : 20 mm Tool & Insert : : Carbide Tool Grade: : SSDCR 16 4D Insert Grade: : Carbide 0.4 Machine: : Conventional Lathe Venue: : KIOT – M.T Lab CONSUMABLE DETAILS FOR THE MACHINING PROCESS:

35. EFFECT OF PROCESS PARAMETERS ON SURFACE ROUGHNESS:

36. EFFECT OF PROCESS PARAMETERS ON MATERIAL REMOVAL RATE:

37. The experimental data were analyzed using statistical methods to investigate the impact of parameters on the responses, such as surface roughness and material removal rate. Design Expert software was used for the analysis. ANOVA was applied to identify the influence and interaction of parameters, while regression analysis was used to create an empirical model. The findings and discussions are based on this analysis. RESULTS AND DISCUSSION

38. CONCLUSION This study developed a response surface regression model to analyze surface roughness in centerless grinding based on key parameters: regulating wheel speed, angle, and depth of cut. The model can predict and monitor surface roughness based on these factors. Increasing the regulating wheel speed and angle leads to a higher material removal rate. Lower regulating wheel speed and angle result in increased surface roughness. For example, at 12 rpm and a 1° angle, surface roughness is 0.57µm (experimental) and 0.52µm (predicted). Higher depth of cut combined with lower regulating wheel speed decreases the material removal rate and worsens surface roughness. For better surface roughness and high material removal rate with low depth of cut, use lower regulating wheel speed and angle. Experimental values closely match predicted values. Optimal surface roughness and material removal rate are achieved with higher depth of cut and higher wheel angle.

39. Surendra Kumar Saini ., (2014). Optimization Of multi-Objective Response During CNC Turning Using Taguchi-Fuzzy Application. Vol 1, Issue 3, PP.141 – 149. Kaltrine Jakupi ., Nexhat Qehaja ., (2015). Effect of Machining Parameters and Machining Time on Surface Roughness in Dry Turning Process. Vol 4, Number 3, PP. 135– 140. Sunil Dambhare .,& Atul Borade , (2015). Sustainability Issues in Turning Process: A study in Indian Machining Industry,Vol . 4, PP. 379- 384. REFERENCES

40. S.K.Choudhury ., Satishchinchanikar ., (2014). Evalution of Chip-tool Interface Temperature: Effect Coating and Cutting Parameters During Turning Hardened aisi 4340 steel. Vol. 2, Issue 9, PP. 996-1005. Marco Sortino ., Giovanni totis ., (2014). Robust Analysis of Stability in Internal Turning. Vol. 2, Issue 7 , PP.1306-1315 Piotr Kiszka ., Krzysztof zak .,(2014). Comparison of Surface Textures Generated in Hard Turning and Grinding Operation. Vol. 2, Issue 9, PP. 84 – 89. REFERENCES

41. K.Palanikumar ., & T.Rajasekaran ., (2013). Investigation on the Turning Parameters for Surface Roughness Using Taguchi Analysis. Vol. 4, Issue 5, PP. 781-790. Rolf Mahnken .,& Chun Cheng., (2015). A Noval Finite Element Approch to Modeling Hard Turning in due Consideration of the Viscoplastic Asymmetry effect .vol. 2, No. 4, PP. 471-476 A.Del prete ., T.Primo ., (2013). Super-Nickel Orthogonal Turning Operation Optimization. Vol. 5. PP. 164-169 G.Urbicain ., A.Rodriguez ., (2013).Stability Prediction Maps in Turning of Difficult-to-cut Meterials . Vol 20. Issue 1 – 2. PP. 514-522. REFERENCES

42. THANK YOU