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  1. 1. Optimization of Boring Parameters V Sai Nitish Amit Shiva Kumar Chiranjeevi Chennu UNDER THE GUIDANCE OF Dr. BADARI NARAYANA KANTHETI
  2. 2. BUCKET ORIELS SPECIFICATIONS AND IMPORTANCE • Bucket oriels are welded supports which act as a connection between the arm of the earth moving machine and the bucket which carries the load. • Bucket oriels bear huge amounts of load, which makes it one of the most crucial components of the earth moving machine. • Minute changes in diameter and surface finish might result in excess friction, vibrations, wear and tear.
  3. 3. BUCKET ORIELS ARE ATTACHED AT THE BACK OF THE BUCKET BY METAL INERT GAS WELDING
  4. 4. Objectives • To obtain surface finish of the bore to be less than 2 micro meters • To achieve a dimensional conformance of diameter of the hole of 70 mm. • To find the parameters influencing the boring operation.
  5. 5. • In many manufacturing organizations experiments are performed to increase the understanding and knowledge of various manufacturing processes. For continuous improvement in product/process quality, it is fundamental to understand the process behaviour, the amount of variability and its impact on product / processes. The present work being optimization of boring parameters, the literature related to this subject area is briefly covered. • The study of cutting forces of boring process is important for selecting appropriate proper cutting conditions. A predictive cutting force model has been developed and influence of cutting parameters on force components has been studied by Alwarsamy et al., 2011. In their work a new analytical cutting force model obtain from the information related to the work piece and tool signature for a boring operation. • The optimization work done by Gaurav Vohra et al 2013, of the boring parameters for a CNC turning centre such as speed, feed rate and depth of cut on aluminium to achieve the highest possible Material removal rate and at minimum surface roughness by using the Taguchi method. In their work it was found that the depths of cut and cutting speed to be the most influencing parameter, followed by the feed rate. • Kanase Sandip et al 2013 proposed an innovative method to reduce tool chatter and enhance surface finish in boring operation. Their results showed the use of passive damping technique has vast potential in the reduction of tool chatter. Literature Review
  6. 6. • The optimum conditions to obtain better damping in boring process for chatter reduction is identified for various cutting conditions by Prasanna venkadesan et al, 2015. • Lee and Tarng 2001 used computer vision techniques to inspect surface roughness of a work piece under a variation of turning operations and developed a relationship between the feature of the surface image and the actual surface roughness under a variation of turning operations for predicting surface roughness with reasonable accuracy. • Davim 2003 studied the influence of cutting condition and cutting time on turning metal matrix composites and used Taguchi method and (ANOVA) for the analyses. They obtained a correlation between cutting velocity, feed and cutting time with tools wear, the power required and the surface roughness of the job. • Balamuruga and Sugumaran 2013 developed a Gaussian process regression model to predict the surface roughness based on tool post vibration and cutting conditions. In their work the data obtained from their experiments and collected from previous studies, have been used to validate the model. • Badadhe et. al., 2012, optimized combination of cutting parameters to achieve the minimum value of surface roughness. In their work, the experimentation was carried out with the help of lathe machine, with three machining parameters such as spindle speed, feed and depth of cut taken as control factors. During the machining, the boring bar is subjected vibrations in different directions; the impact of these vibrations on the surface roughness which is an important quality parameter of the bored surface was estimated.
  7. 7. Analysis and Optimization of Parameters Affecting Surface Roughness in a Boring Process using DOE • Manufacturing industries are continuously demanding for higher production rate and improved machinability as quality and productivity play significant role in the manufacturing environment. • Higher production rate can be achieved at high cutting speed, feed and depth of cut • The quality of the final product is limited by capability of tooling and surface finish. • Over all performance of the operation depends on the selection of right cutting parameters and is generally a compromise between several variables.
  8. 8. • Depth of cut at 1 mm (1 level) • Feed rate is varied at 40, 80, 120, 160, 200 and 240 in mm/min ( 6 levels) • Speed of cut 800, 900 and 1200 rpm ( 3 levels) • The number of possible iteration using a full factorial will be 6*3 = 18. In the present program, as iterations required are small all possible combinations are used. • The responses measured for each iteration are: a) surface roughness (RA) and b) hole diameter. • To analyse the data , a statistical tool DESIGN OF EXPERIMENTS (Mini TAB Ver 16) is used to arrive at proper improvement plan of the manufacturing process parameters. The experimentation plan is designed using design of experiment (DOE). • It is possible to determine these parameters using ANOVA, Response Surface Methodologies including optimization. THE PLAN
  9. 9. A sample data of inputs and response parameters is shown below Sl. No Factor 1 Depth of cut , mm Factor 2 Feed rate mm/min Factor 3 Speed of cut RPM Surface finish RA µ m Diameter of hole, mm 1 1 40 800 2.320 70.025 2 1 40 900 2.306 70.010 3 1 80 900 1.618 70.015 4 1 80 1200 1.968 70.000 . . . . . . . . . . . . 18 1 120 1200 2.306 69.999
  10. 10. EQUIPMENT USED FOR BORING Tool holder TOOLS JIGS & FIXTURES MX-5 MACHINE CONTROL UNIT
  11. 11. EQUIPMENT USED FOR BORING TOOL HOLDER AUTOMATIC TOOL CHANGER CONTROL UNIT
  12. 12. Calibration of surface tester Surface Roughness Tester LC : 1 nm MEASURING INSTRUMENT USED A Mitutoyo SJ-201P apparatus was used. For each specimen three readings were taken at approximately 1200 angles apart and the average value was used for the subsequent analysis. BORE GAUGE LC: 1 MICRON
  13. 13. Bore Gauge Dial Attachments
  14. 14. DOE ANALYSIS IS DONE USING MINITAB VER 16.0 Steps involved in statistical analysis: 1. Statistical analysis -> DOE -> Factorials -Table of influencing factors will be generated 2. Statistical analysis -> ANOVA -> Balanced ANOVA -> Main Effect plot and Interaction Effect Plot 3. Statistical analysis -> DOE -> Response Surface -> Surface and Contour plots 4. Statistical analysis -> DOE -> Selection of optimal design -> Response optimization 5. Model validation -> Use optimal factors, repeat the boring operation and measure the respective hole diameter and surface roughness
  15. 15. Full Factorial Design • Full factorial designs include all possible combinations of every level of every factor. • Full factorial designs can require a lot of trials, which can take a lot of time and cost. • Requires at least one observation for every combination of factors and levels. • Allows for the measurement of all possible interactions. • Ex1. a design of 4 factors with 3 levels each would be: 3 x 3 x 3 x 3 = 3^4 = 81 observations. • Ex 2. 4 factors (A=3, B = 2, C=5, D= 4 levels). 3 x 2 x 5 x 4 = 120 observations.
  16. 16. TABLE REPRESENTING FULL FACTORIAL DATA
  17. 17. SPEED (RPM) DEPTH OF CUT mm FEED RATE mm/min HOLE DIAMETER mm SURFACE FINISH (RA) µ m 800 1 40 70.025 2.320 900 1 40 70.010 2.306 1200 1 40 70.015 1.618 800 1 80 70.000 1.968 900 1 80 69.999 1.954 1200 1 80 70.025 1.709 800 1 120 70.015 2.766 900 1 120 70.020 2.306 1200 1 120 70.020 1.742 800 1 160 70.015 2.616 900 1 160 70.020 2.566 1200 1 160 69.980 2.320 800 1 200 70.010 2.814 900 1 200 69.985 2.667 1200 1 200 69.980 2.592 800 1 240 70.012 4.902 900 1 240 70.006 3.306 1200 1 240 70.025 2.742 READINGS TAKEN FOR VARYING SPEED AND FEED RATE
  18. 18. Effect of speed and feed on surface finish
  19. 19. Effect of speed and feed on diameter
  20. 20. Surface plot for Surface Finish
  21. 21. Contour plot for Surface Finish
  22. 22. Surface plot for Diameter
  23. 23. Contour plot for Diameter
  24. 24. Optimization plot
  25. 25. Model Validation -> Use optimal factors, repeat the boring operation and measure the respective hole diameter and surface roughness Optimal values Speed 1000 rpm , Feed 100 mm/min Expected Dia 70.005 mm and RA = 1.843 µ m Sl.no RPM Feed Dia Ra edia ddia eRa dRa 1 800 40 70.03 2.32 70.02 0.005 2.3098 0.01 2 900 40 70.01 2.306 70.016 -0.006 2.0103 0.296 3 1200 40 70.02 1.618 70.022 -0.007 1.8553 -0.237 4 800 80 70 1.968 70.015 -0.015 2.3068 -0.339 5 900 80 70 1.954 70.01 -0.011 1.9751 -0.021 6 1200 80 70.03 1.709 70.014 0.011 1.7232 -0.014 7 800 120 70.02 2.766 70.011 0.004 2.4824 0.284 8 900 120 70.02 2.306 70.006 0.014 2.1184 0.188 9 1200 120 70.02 1.742 70.008 0.012 1.7698 -0.028 10 800 160 70.02 2.616 70.011 0.004 2.8366 -0.221 11 900 160 70.02 2.566 70.004 0.016 2.4403 0.126 12 1200 160 69.98 2.32 70.004 -0.024 1.9949 0.325 13 800 200 70.01 2.814 70.012 -0.002 3.3694 -0.555 14 900 200 69.99 2.667 70.005 -0.02 2.9409 -0.274 15 1200 200 69.98 2.592 70.003 -0.023 2.3987 0.193 16 800 240 70.01 4.902 70.016 -0.004 4.0808 0.821 17 900 240 70.01 3.306 70.009 -0.003 3.62 -0.314 18 1200 240 70.03 2.742 70.005 0.02 2.981 -0.239 19 1000 100 70.0045 1.711 70.005 0.0005 1.843 0.132 Confirmatory experimental values Dia mm Ra µm 70.013 1.616 69.985 1.999 70.015 1.517 Confidence interval ( with 95% confidence) = mean +/- SD =70.0045+/- 0.0173 = 70.021, 69.987 Predicted value 70.005 is with in the optimal settings hence the predicted model is sound.
  26. 26. Validation Of Results
  27. 27. Upper limit = 2 µm Avg value = 1.7609 µm
  28. 28. Target value = 70.00 mm mm Avg value = 70.0132 mm
  29. 29. Conclusion In this project, we have selected the combination of optimum cutting parameters which will result in better surface finish and diameter of the bore. Three parameters viz. spindle speed, feed and depth of cut of the MX-5 machine were taken as control factors. The cutting trials were performed as per full factorial method to deal with the response from multi-variables. Carbon steel was used as a job material which was cut by using carbide tool. DOE and Analysis of Variance (ANOVA) was carried out to find the significant factors and their individual contribution in the response function i.e. surface roughness and diameter accuracy of the bore. After the validation, we have determined that the most optimal values for our given machining conditions of speed, feed rate and depth of cut are 1000 rpm, 100 mm/min and 1 mm respectively.
  30. 30. Future scope of study The selection of optimal cutting parameters to obtain specified surface roughness is very difficult for any work piece material. In the current scenario, desired surface roughness can be achieved by trial and error method. But it is time consuming and material is unnecessarily wasted for this purpose. So, there is a need to find a technique that can predict cutting parameters for the desired surface roughness. Following are the recommendations for continuing the research work in this area. 1. Improved method for surface roughness measurement with higher sampling rate can be helpful to locate the principal frequencies of boring vibrations in the surface roughness spectrum. 2. In depth study of dynamic cutting force can be carried out regards to frequency content. 3. An intelligent machine controller can be developed as per the technique proposed in the present work for automatic adjustment of the machining parameters. 4. Detail analysis of time delays in control action and stability analysis of the control system can be carried out.
  31. 31. • S. Vetrivel et al., “Theoretical Cutting Force Prediction and Analysis of Boring Process Using Mathcad”, World Applied Sciences Journal 12 (10): 1814-1818, 2011 • Gaurav Vohra et al., “Analysis and Optimization of Boring Process Parameters by Using Taguchi Method”, International Journal of Computer Science and Communication Engineering IJCSCE Special issue on “Recent Advances in Engineering & Technology” NCRAET-2013. • Kanase Sandip et. al., “Improvement Of Ra Value Of Boring Operation Using Passive Damper”, The International Journal Of Engineering And Science (IJES), Vol. 2, Iss 7, P 103- 108, 2013. • V. Prasanna Venkadesan, et al., “Chatter Suppression in Boring Process Using Passive Damper”, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering Vol: 9, No: 11, 2015. • Yang WH, Tarng YS ,1998, “Design optimization of cutting parameters for turning operations based on the Taguchi method”, J. of Materials Processing Technology 84:122- 129. REFERENCES
  32. 32. • Davim, J.P., 2001, "A Note on the Determination of Optimal Cutting Conditions for Surface Finish Obtained in Turning Using Design of Experiments", Journal of Materials Processing Technology, Vol. 116, Iss. 23, pp. 305-308. • Lee B.Y., Tarng Y.S., 2001, “Surface roughness inspection by computer vision in turning operations”, International Journal of Machine Tools & Manufacture 41 (2001): 1251 • Davim J. P., “Design of optimization parameters for turning metal matrix composites based on the orthogonal arrays”, Journal Processing Technology, 132, 2003, pp. 340 • Balamuruga Mohan Raj. G, Sugumaran. V, 2013, “Developing Gaussian Process Model to Predict the Surface Roughness in Boring Operation”, International Journal of Engineering Trends and Technology- Vol. 4 Iss.3, 2013 • A. M. Badadhe et. al., 2012, “Optimization of Cutting Parameters in Boring Operation”, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), P 10-15.
  33. 33. PUBLICATION This work has been accepted to be presented as a paper at International Conference on Recent Advances in Engineering Sciences (ICRAES) in MS Ramaiah Institute of Technology, Bangalore on 8th of September 2016.
  34. 34. Thank you

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