FEA Analysis Of The Lathe Machining On Machining Characteristics Towards Aluminium Alloy Using RSM Method
This project focuses on the simulation of the turning process by using the finite element analysis (FEA) machining Deform 3D software based on the Box-Behnken of response surface method (RSM) experimental matrix. Based on the Box-Behnken design matrix, there were 13 simulation runs with one centre p...
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This project focuses on the simulation of the turning process by using the finite element analysis (FEA) machining Deform 3D software based on the Box-Behnken of response surface method (RSM) experimental matrix. Based on the Box-Behnken design matrix, there were 13 simulation runs with one centre point in order to analyze the influence of the cutting parameters on the output responses of the turning process such as cutting temperature, effective stress and material removal rate. The selected cutting parameters in this turning simulation of the aluminium alloy 7075 were cutting speed (200 m/min – 250 m/min), feed rate (0.1 mm/rev – 0.25 mm/rev) and depth of cut (0.5 mm – 0.6 mm). The analysis of variance (ANOVA) was used to determine the most influential cutting parameters on the output responses. The Box-Behnken of response surface method was employed to investigate the interactions between the cutting parameters on the output responses and to optimize the cutting parameters setting of the turning process. From the results, it is found that the depth of cut is the most influential cutting parameter for the cutting temperature. Meanwhile, the feed rate is the most significant cutting parameter for effective stress. For the material removal rate, the most influential cutting parameter is the feed rate. Furthermore, the interaction between cutting speed and depth of cut is the predominant interaction that gives a significant effect on the cutting temperature, which shows that the cutting temperature increases with the increase in depth of cut and decrease in cutting speed. In the meantime, the interaction between cutting speed and feed rate is the major interaction that gives the most influential impact on the effective stress, which shows that the effective stress increases with the increase in both of the cutting speed and feed rate. The most influential interaction that gives a significant effect on the material removal rate is the interaction between feed rate and depth of cut, which shows that the material removal rate increases with the increase in both of the feed rate and depth of cut. Moreover, after the optimization process, the cutting temperature gives the minimum value of 401.89 ℃. Further, the effective stress gives the minimum value of 792.14 MPa. While the material removal rate gives the maximum value of 5126375 mm^3/s. Overall, all the objectives of this project are achieved. Thus, a decrease in both of the cutting temperature and effective stress with the increase of material removal rate, therefore the defect of the wear on the cutting tool can be reduced. |
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Thesis |
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Master of Philosophy (M.Phil.) |
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Master's degree |
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Liew, Yoong Ler |
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Liew, Yoong Ler FEA Analysis Of The Lathe Machining On Machining Characteristics Towards Aluminium Alloy Using RSM Method |
| author_facet |
Liew, Yoong Ler |
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Liew, Yoong Ler |
| title |
FEA Analysis Of The Lathe Machining On Machining Characteristics Towards Aluminium Alloy Using RSM Method |
| title_short |
FEA Analysis Of The Lathe Machining On Machining Characteristics Towards Aluminium Alloy Using RSM Method |
| title_full |
FEA Analysis Of The Lathe Machining On Machining Characteristics Towards Aluminium Alloy Using RSM Method |
| title_fullStr |
FEA Analysis Of The Lathe Machining On Machining Characteristics Towards Aluminium Alloy Using RSM Method |
| title_full_unstemmed |
FEA Analysis Of The Lathe Machining On Machining Characteristics Towards Aluminium Alloy Using RSM Method |
| title_sort |
fea analysis of the lathe machining on machining characteristics towards aluminium alloy using rsm method |
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Universiti Teknikal Malaysia Melaka |
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Faculty of Manufacturing Engineering |
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2020 |
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http://eprints.utem.edu.my/id/eprint/25361/1/FEA%20analysis%20of%20the%20lathe%20machining%20on%20machining%20characteristics%20towards%20aluminium%20alloy%20using%20RSM%20method.pdf http://eprints.utem.edu.my/id/eprint/25361/2/FEA%20analysis%20of%20the%20lathe%20machining%20on%20machining%20characteristics%20towards%20aluminium%20alloy%20using%20RSM%20method.pdf |
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my-utem-ep.253612021-10-05T13:43:40Z FEA Analysis Of The Lathe Machining On Machining Characteristics Towards Aluminium Alloy Using RSM Method 2020 Liew, Yoong Ler This project focuses on the simulation of the turning process by using the finite element analysis (FEA) machining Deform 3D software based on the Box-Behnken of response surface method (RSM) experimental matrix. Based on the Box-Behnken design matrix, there were 13 simulation runs with one centre point in order to analyze the influence of the cutting parameters on the output responses of the turning process such as cutting temperature, effective stress and material removal rate. The selected cutting parameters in this turning simulation of the aluminium alloy 7075 were cutting speed (200 m/min – 250 m/min), feed rate (0.1 mm/rev – 0.25 mm/rev) and depth of cut (0.5 mm – 0.6 mm). The analysis of variance (ANOVA) was used to determine the most influential cutting parameters on the output responses. The Box-Behnken of response surface method was employed to investigate the interactions between the cutting parameters on the output responses and to optimize the cutting parameters setting of the turning process. From the results, it is found that the depth of cut is the most influential cutting parameter for the cutting temperature. Meanwhile, the feed rate is the most significant cutting parameter for effective stress. For the material removal rate, the most influential cutting parameter is the feed rate. Furthermore, the interaction between cutting speed and depth of cut is the predominant interaction that gives a significant effect on the cutting temperature, which shows that the cutting temperature increases with the increase in depth of cut and decrease in cutting speed. In the meantime, the interaction between cutting speed and feed rate is the major interaction that gives the most influential impact on the effective stress, which shows that the effective stress increases with the increase in both of the cutting speed and feed rate. The most influential interaction that gives a significant effect on the material removal rate is the interaction between feed rate and depth of cut, which shows that the material removal rate increases with the increase in both of the feed rate and depth of cut. Moreover, after the optimization process, the cutting temperature gives the minimum value of 401.89 ℃. Further, the effective stress gives the minimum value of 792.14 MPa. While the material removal rate gives the maximum value of 5126375 mm^3/s. Overall, all the objectives of this project are achieved. Thus, a decrease in both of the cutting temperature and effective stress with the increase of material removal rate, therefore the defect of the wear on the cutting tool can be reduced. 2020 Thesis http://eprints.utem.edu.my/id/eprint/25361/ http://eprints.utem.edu.my/id/eprint/25361/1/FEA%20analysis%20of%20the%20lathe%20machining%20on%20machining%20characteristics%20towards%20aluminium%20alloy%20using%20RSM%20method.pdf text en public http://eprints.utem.edu.my/id/eprint/25361/2/FEA%20analysis%20of%20the%20lathe%20machining%20on%20machining%20characteristics%20towards%20aluminium%20alloy%20using%20RSM%20method.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119163 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Manufacturing Engineering Md Ali, Mohd Amran 1. Abhang, L.B., and Hameedullah, M., 2010. Chip-Tool Interface Temperature Prediction Model for Turning Process. International Journal of Engineering Science and Technology, 2 (4), pp. 382-393. 2.Agustina, B., Bernal, C., Camacho, A.M., and Rubio, E.M., 2013. Experimental Analysis of the Cutting Forces Obtained in Dry Turning Processes of UNS A97075 Aluminium Alloys. In: Department of Manufacturing Engineering, National University of Distance Education, Proceedings of the Manufacturing Engineering Society International Conference, Madrid, Spain, MESIC 2013. National University of Distance Education Publisher. 3. Allamraju, K.V., and Rao, K.S.S., 2017. Effect on Micro-Hardness and Residual Stress in CNC Turning of Aluminium 7075 Alloy. In: PG Student, Mechanical Engineering Department, National Institute of Technology, Proceedings of the 5th International Conference of Materials Processing and Characterizations, Warangal, India, ICMPC 2016. National Institute of Technology Publisher. 4. Arbizu, I.P., and Perez, C.J.L., 2003. Surface Roughness Prediction by Factorial Design of Experiments in Turning Process. Journal of Materials Processing Technology, 143-144, pp. 390-396. 5. Arunangsu, D., Sarkar, S., Karanjai, M., and Sutradhar, G., 2018. RSM Based Study on the Influence of Sintering Temperature on MRR for Titanium Powder Metallurgy Products using Box-Behnken Design. In: Department of Mechanical Engineering, Jadavpur University, Proceedings of the 7th International Conference of Materials Processing and Characterization, Kolkata, India, ICMPC2017. Jadavpur University Publisher. 6. Asilturk, I., and Neseli, S., 2012. Multi Response Optimization of CNC Turning Parameters via Taguchi Method-Based Response Surface Analysis. Journal of Measurement, 45, pp. 785-794. 7. Bag, R., Panda, A., Sahoo, A.K., and Kumar, R., 2020. Cutting Tools Characteristics and Coating Depositions for Hard Part Turning of AISI 4340 Martensitic Steel: A Review Study. In: School of Mechanical Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Proceedings of the 10th International Conference of Materials Processing and Characterization, Odisha, India, 31 December 2019. Kalinga Institute of Industrial Technology (KIIT), Deemed to be University Publisher. 8. Benardos, P.G., and Vosniakos, G.C., 2007. Predicting Surface Roughness in Machining. International Journal of Machine Tool and Manufacturing, 43, pp. 833-844. 9. Bhoyar, Y.R., and Kamble, P.D., 2013. Finite Element Analysis on Temperature Distribution in Turning Process using Deform 3D. International Journal of Research in Engineering and Technology, 2 (5), pp. 901-906. 10. Bobzin, K., 2016. High-Performance Coatings for Cutting Tools. CIRP Journal of Manufacturing Science and Technology, pp. 1-9. 11. Camposeco-Negrete, C., 2014. Optimization of Cutting Parameters Using Response Surface Method for Minimizing Energy Consumption and Maximizing Cutting Quality in Turning AISI 6061 T6 Aluminium. Journal of Cleaner Production, pp. 1-9. 12. Charitha, R., Shrikantha, R., and Mervin, H., 2018. Performance Improvement Studies for Cutting Tools with Perforated Surface in Turning of Titanium Alloy. In: Department of Mechanical Engineering, NITK University, MATEC Web of Conferences 144, Surathkal, India, RiMES 2017. NITK University Publisher. 13. Dabnun, M.A., Hashmi, M.S.J., and El-Baradie, M.A., 2005. Surface Roughness Prediction Model by Design of Experiments for Turning Machinable Glass-Ceramic (Macor). Journal of Materials Processing Technology, 164-165, pp. 1289-1293. 14. Das, D., Ali, R.F., Nayak, B.B., and Routara, B.C., 2018. Investigation on Surface Roughness and Chip Reduction Coefficient during Turning Aluminium Matrix Composite. In: School of Mechanical Engineering, KIIT Deemed to be University, Proceedings of the International Conference on Advances in Materials and Manufacturing Applications, Bhubaneswar, India, IConAMMA 2017. KIIT Deemed to be University Publisher. 15. 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, 116, pp. 305-308. 16. Dema, R.R., Shapovalov, A.N., Alontsev, V.V., and Kalugina, O.B., 2019. Computer Simulation and Research of the Hot Rolling Process in Deform-3D. In: National University of Science and Technology, Proceedings of the International Conference on Modern Trends in Manufacturing Technologies and Equipment, Moscow, Russia, 5 May 2019, National University of Science and Technology Publisher. 17. Ezilarasan, C., Kumar, V.S.S., and Velayudham, A., 2014. Theoretical Predictions and Experimental Validations on Machining the Nimonic C-263 Super Alloy. Journal of Simulation Modelling Practice and Theory, 40, pp. 192-207. 18. Ferreira, S.C., Bruns, R.E., Ferreira, H.S., Matos, G.D., David, J.M., Brandão, G.C., and DosSantos, W.N.L., 2007. Box–Behnken Design: Analternative for the Optimization of Analytical Methods, Anal.Chim. Acta597, pp. 179–186. 19. Ferreira, S.L.C., Bruns, R.E., Ferreira, H.S., Matos, G.D., David, J.M., Brandao, G.C., Silva, E.G.P., Portugal, L.A., Reis, P.S., Souza, A.S., and Santos, W.N.L., 2007. Box-Behnken Design: An Alternative for the Optimization of Analytical Methods. Journal of Analytical Chimica Acta, 597, pp. 179-186. 20. Francy, K.A., Rao, C.S., and Gopalakrishnaiah, P., 2019. Optimization of Direct Extrusion Process Parameter on 16MnCr5 and AISI1010 using Deform-3D. In: Department of Mechanical Engineering, Andhra University College of Engineering, Proceedings of the 14th Global Congress on Manufacturing and Management, Visakhapatnam, India, GCMM-2018, Andhra University College of Engineering Publisher. 21. Gupta, K.M., Ramdev, K., Dharmateja, S., and Sivarajan, S., 2018. Cutting Characteristics of PVD Coated Cutting Tools. In: School of Mechanical and Building Sciences, VIT University, Proceedings of the International Conference on Materials Manufacturing and Modelling, Chennai, India, ICMMM-2017. VIT University Publisher. 22. Han, J.H., Cao, K.W., Xiao, L., Tan, X.H., Li, T.X., Xu, L., Tang, Z.R., Liao, G.R., and Shi, T.L., 2020. In Situ Measurement of Cutting Edge Temperature in Turning using a Near-Infrared Fiber-Optic Two-Color Pyrometer. Journal of Measurement, 156, pp. 107595. 23. Horvath, R., and Dregelyi-Kiss, A., 2015. Analysis of Surface Roughness of Aluminium Alloys Fine Turned: United Phenomenological Models and Multi-Performance Optimization. Journal of Measurement, pp. 1-22. 24. Jayaraman, P., and Kumar, L.M., 2014. Multi-response Optimization of Machining Parameters of Turning of AA6063 T6 Aluminium Alloy using Grey Relational Analysis in Taguchi Method. In: Department of Mechanical Engineering, St. Peter’s University, Proceedings of the 12th Global Congress on Manufacturing and Management, Chennai, India, GCMM 2014. St. Peter’s University Publisher. 25. Kawin, N., Jagadeesh, D., Saravanan, G., and Periasamy, K., 2019. Optimization of Turning Parameters in Sugarcane Bagasse Ash Reinforced with Al-Si10-Mg Alloy Composites by Taguchi Method. In: Kongunadu College of Engineering and Technology, Proceedings of the International Conference on Recent Trends in Nanomaterials for Energy, Environmental and Engineering Applications, Trichy, Tamilnadu, India, 8 May 2019. Kongunadu College of Engineering and Technology Publisher. 26. Kiprawi, M.A., Yassin, A., Shazali, S.T.S., Islam, M.S., and Said, M.A.M., 2017. Study of Cutting Edge Temperature and Cutting Force of End Mill Tool in High Speed Machining. In: Department of Mechanical and Manufacturing Engineering, Universiti Malaysia Sarawak, MATEC Web of Conferences 87, Sarawak, Malaysia, ENCON 2016. Universiti Malaysia Sarawak Publisher. 27. Kiran, R.R.S., Madhu, G.M., Satyanarayana, S.V., Kalpana, P., and Rangaiah, G.S., 2017. Applications of Box-Behnken Experimental Design Coupled with Artificial Neural Networks for Biosorption of Low Concentrations of Cadmium Using Spirulina (Arthrospira) SPP. Journal of Resource-Efficient Technologies, 3, pp. 113-123. 28. Kumar, S.P.L., 2019. Measurement and Uncertainty Analysis of Surface Roughness and Material Removal Rate in Micro Turning Operation and Process Parameters Optimization. Journal of Measurement, 140, pp. 538-547. 29. 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In: Department of Mechanical Engineering, Amity University, Proceedings of the 10th International Conference of Materials Processing and characterization, Noida, India, 7 December 2019. Amity University Publisher. 57. Yanda, H., Ghani, J.H., Rodzi, M.N.A.M., Othman, K., and Haron, C.H.C., 2010. Optimization of Material Removal Rate, Surface Roughness and Tool Life on Conventional Dry Turning of FCD 700. International Journal of Mechanical and Materials Engineering, 5 (2), pp. 182-190. 58. Zhang, P., Li, Y., Liu, Y., Zhang, Y., and Liu, J., 2020. Analysis of the Microhardness, Mechanical Properties and Electrical Conductivity of 7075 Aluminium Alloy. Journal of Vacuum, 171, pp. 109005. |