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Minimization of Surface Roughness and Machining Deformation in Milling of Al Alloy Thin-Walled Parts

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Abstract

This study focused on investigating the surface roughness in the feed direction (Ra-Fd), surface roughness in the transverse direction (Ra-Td), and thin-walled parts deformation (TWD) during milling of Al alloy 5083. The response surface method (RSM) was used to conduct experiments and establish the models of Ra-Fd, Ra-Td, and TWD under various cutting parameters. The significance of cutting parameters on Ra-Fd, Ra-Td, and TWD was analyzed by analysis of variance. It was observed that the Ra-Fd and Ra-Td are mainly influenced by the spindle speed, depth of cut, transverse size and feed rate, while the TWD is mainly influenced by the depth of cut. A comparison of RSM-optimum function and artificial bee colony (ABC) algorithm optimum programming was conducted to obtain the best cutting conditions leading to minimum Ra-Fd, Ra-Td and TWD simultaneously. From the presented results, ABC algorithm was able to obtain the better cutting strategy. Finally, the performance of the proposed cutting strategy was verified by confirmation experiments.

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References

  1. Dejun, Y. A. N., Haiyang, L., Xiaoli, L., Meida, Z., Jiuqiang, L., & Zhimin, L. (2018). Microstructure and mechanical properties of variable polarity plasma arc welded joints of 5083 aluminum alloy for shipbuilding. Rare Metal Materials and Engineering, 47(10), 3161–3166.

    Google Scholar 

  2. Il, A., Chatelain, J.-F., Lalonde, J.-F., Balazinski, M., & Rimpault, X. (2018). An experimental investigation of the influence of cutting parameters on workpiece internal temperature during Al2024-T3 milling. The International Journal of Advanced Manufacturing Technology, 97(1–4), 413–426. https://doi.org/10.1007/s00170-018-1948-3.

    Article  Google Scholar 

  3. Hao, Y., & Liu, Y. (2017). Analysis of milling surface roughness prediction for thin-walled parts with curved surface. The International Journal of Advanced Manufacturing Technology, 93(5), 2289–2297. https://doi.org/10.1007/s00170-017-0615-4.

    Article  Google Scholar 

  4. Dikshit, M. K., Puri, A. B., & Maity, A. (2017). Modelling and application of response surface optimization to optimize cutting parameters for minimizing cutting forces and surface roughness in high-speed, ball-end milling of Al2014-T6. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(12), 5117–5133. https://doi.org/10.1007/s40430-017-0865-y.

    Article  Google Scholar 

  5. Li, J.-G., & Wang, S.-Q. (2017). Distortion caused by residual stresses in machining aeronautical aluminum alloy parts: recent advances. The International Journal of Advanced Manufacturing Technology, 89(1–4), 997–1012. https://doi.org/10.1007/s00170-016-9066-6.

    Article  Google Scholar 

  6. Bolar, G., Das, A., & Joshi, S. N. (2018). Measurement and analysis of cutting force and product surface quality during end-milling of thin-wall components. Measurement, 121, 190–204. https://doi.org/10.1016/j.measurement.2018.02.015.

    Article  Google Scholar 

  7. Qin, G., Wang, H., Lin, F., Sun, S., Guo, Y., Wu, T., et al. (2019). A new approach to deformation control of aeronautical monolithic components for aluminum alloy plates based on stress inverse and stress evaluation. SCIENTIA SINICA Technologica. https://doi.org/10.1360/SST-2019-0089.

    Article  Google Scholar 

  8. Cagan, S. C., Venkatesh, B., & Buldum, B. B. (2020). Investigation of surface roughness and chip morphology of aluminum alloy in dry and minimum quantity lubrication machining. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.01.547.

    Article  Google Scholar 

  9. Vakondios, D., Kyratsis, P., Yaldiz, S., & Antoniadis, A. (2012). Influence of milling strategy on the surface roughness in ball end milling of the aluminum alloy Al7075-T6. Measurement, 45(6), 1480–1488. https://doi.org/10.1016/j.measurement.2012.03.001.

    Article  Google Scholar 

  10. Wang, Z. H., Yuan, J. T., Liu, T. T., Huang, J., & Qiao, L. (2015). Study on surface roughness in high-speed milling of AlMn1Cu using factorial design and partial least square regression. The International Journal of Advanced Manufacturing Technology, 76(9), 1783–1792. https://doi.org/10.1007/s00170-014-6400-8.

    Article  Google Scholar 

  11. Khanghah, S. P., Boozarpoor, M., Lotfi, M., & Teimouri, R. (2015). Optimization of micro-milling parameters regarding burr size minimization via RSM and simulated annealing algorithm. Transactions of the Indian Institute of Metals, 68(5), 897–910. https://doi.org/10.1007/s12666-015-0525-9.

    Article  Google Scholar 

  12. Zeng, S., & Yuan, L. (2017). Optimization of milling process parameters based on real coded self-adaptive genetic algorithm and grey relation analysis. In Y. Huang, H. Wu, H. Liu, & Z. Yin (Eds.), Intelligent Robotics and Applications (pp. 867–876). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-65298-6_77.

    Chapter  Google Scholar 

  13. Vipindas, K., Kuriachen, B., & Mathew, J. (2019). Investigations into the effect of process parameters on surface roughness and burr formation during micro end milling of TI–6AL–4V. The International Journal of Advanced Manufacturing Technology, 100(5), 1207–1222. https://doi.org/10.1007/s00170-016-9210-3.

    Article  Google Scholar 

  14. Sahu, N. K., & Andhare, A. B. (2017). Modelling and multiobjective optimization for productivity improvement in high speed milling of Ti–6Al–4V using RSM and GA. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(12), 5069–5085. https://doi.org/10.1007/s40430-017-0804-y.

    Article  Google Scholar 

  15. Pinar, A. M., Filiz, S., & Ünlü, B. S. (2016). A comparison of cooling methods in the pocket milling of AA5083-H36 alloy via Taguchi method. The International Journal of Advanced Manufacturing Technology, 83(9), 1431–1440. https://doi.org/10.1007/s00170-015-7666-1.

    Article  Google Scholar 

  16. Karabulut, Ş., & Karakoç, H. (2017). Investigation of surface roughness in the milling of Al7075 and open-cell SiC foam composite and optimization of machining parameters. Neural Computing and Applications, 28(2), 313–327. https://doi.org/10.1007/s00521-015-2058-x.

    Article  Google Scholar 

  17. Tlhabadira, I., Daniyan, I. A., Machaka, R., Machio, C., Masu, L., & VanStaden, L. R. (2019). Modelling and optimization of surface roughness during AISI P20 milling process using Taguchi method. The International Journal of Advanced Manufacturing Technology, 102(9), 3707–3718. https://doi.org/10.1007/s00170-019-03452-4.

    Article  Google Scholar 

  18. Mahesh, G., Muthu, S., & Devadasan, S. R. (2015). Prediction of surface roughness of end milling operation using genetic algorithm. The International Journal of Advanced Manufacturing Technology, 77(1–4), 369–381. https://doi.org/10.1007/s00170-014-6425-z.

    Article  Google Scholar 

  19. Zheng, K., Yao, J., & Dong, S. (2018). Surface roughness modeling and milling parameter optimization of 15–5PH stainless steel in milling arc surface. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40(4), 192. https://doi.org/10.1007/s40430-018-1127-3.

    Article  Google Scholar 

  20. Lmalghan, R., Rao, K., ArunKumar, S., Rao, S. S., & Herbert, M. A. (2018). Machining parameters optimization of AA6061 using response surface methodology and particle swarm optimization. International Journal of Precision Engineering and Manufacturing, 19(5), 695–704. https://doi.org/10.1007/s12541-018-0083-2.

    Article  Google Scholar 

  21. Chen, Y., Sun, Y., Lin, H., & Zhang, B. (2020). Prediction model of milling surface roughness based on genetic algorithms. In Z. Xu, K.-K. R. Choo, A. Dehghantanha, R. Parizi, & M. Hammoudeh (Eds.), Cyber security intelligence and analytics (pp. 1315–1320). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-15235-2_179.

    Chapter  Google Scholar 

  22. Qu, S., Zhao, J., & Wang, T. (2017). Experimental study and machining parameter optimization in milling thin-walled plates based on NSGA-II. The International Journal of Advanced Manufacturing Technology, 89(5), 2399–2409. https://doi.org/10.1007/s00170-016-9265-1.

    Article  Google Scholar 

  23. Karkalos, N. E., Galanis, N. I., & Markopoulos, A. P. (2016). Surface roughness prediction for the milling of Ti–6Al–4V ELI alloy with the use of statistical and soft computing techniques. Measurement, 90, 25–35. https://doi.org/10.1016/j.measurement.2016.04.039.

    Article  Google Scholar 

  24. Gao, Y.-Y., Ma, J.-W., Jia, Z.-Y., Wang, F.-J., Si, L.-K., & Song, D.-N. (2016). Tool path planning and machining deformation compensation in high-speed milling for difficult-to-machine material thin-walled parts with curved surface. The International Journal of Advanced Manufacturing Technology, 84(9), 1757–1767. https://doi.org/10.1007/s00170-015-7825-4.

    Article  Google Scholar 

  25. Wang, J., Zhang, D., Wu, B., & Luo, M. (2018). Prediction of distortion induced by machining residual stresses in thin-walled components. The International Journal of Advanced Manufacturing Technology, 95(9), 4153–4162. https://doi.org/10.1007/s00170-017-1358-y.

    Article  Google Scholar 

  26. Wu, Q., Li, D.-P., Ren, L., & Mo, S. (2016). Detecting milling deformation in 7075 aluminum alloy thin-walled plates using finite difference method. The International Journal of Advanced Manufacturing Technology, 85(5), 1291–1302. https://doi.org/10.1007/s00170-015-8012-3.

    Article  Google Scholar 

  27. Yue, C., Chen, Z., Liang, S. Y., Gao, H., & Liu, X. (2019). Modeling machining errors for thin-walled parts according to chip thickness. The International Journal of Advanced Manufacturing Technology, 103(1), 91–100. https://doi.org/10.1007/s00170-019-03474-y.

    Article  Google Scholar 

  28. Song, Q., Shi, J., Liu, Z., & Wan, Y. (2017). A time-space discretization method in milling stability prediction of thin-walled component. The International Journal of Advanced Manufacturing Technology, 89(9), 2675–2689. https://doi.org/10.1007/s00170-016-9379-5.

    Article  Google Scholar 

  29. Jiang, X., Zhu, Y., Zhang, Z., Guo, M., & Ding, Z. (2018). Investigation of residual impact stress and its effects on the precision during milling of the thin-walled part. The International Journal of Advanced Manufacturing Technology, 97(1), 877–892. https://doi.org/10.1007/s00170-018-1941-x.

    Article  Google Scholar 

  30. Jiang, X., Wang, Y., Ding, Z., & Li, H. (2017). An approach to predict the distortion of thin-walled parts affected by residual stress during the milling process. The International Journal of Advanced Manufacturing Technology, 93(9), 4203–4216. https://doi.org/10.1007/s00170-017-0811-2.

    Article  Google Scholar 

  31. Guo, M., Jiang, X., Ye, Y., Ding, Z., & Zhang, Z. (2019). Investigation of redistribution mechanism of residual stress during multi-process milling of thin-walled parts. The International Journal of Advanced Manufacturing Technology, 103(1), 1459–1466. https://doi.org/10.1007/s00170-019-03640-2.

    Article  Google Scholar 

  32. Cerutti, X., & Mocellin, K. (2016). Influence of the machining sequence on the residual stress redistribution and machining quality: analysis and improvement using numerical simulations. The International Journal of Advanced Manufacturing Technology, 83(1), 489–503. https://doi.org/10.1007/s00170-015-7521-4.

    Article  Google Scholar 

  33. Li, B., Gao, H., Deng, H., Pan, H., & Wang, B. (2019). Investigation on the influence of the equivalent bending stiffness of the thin-walled parts on the machining deformation. The International Journal of Advanced Manufacturing Technology, 101(5), 1171–1182. https://doi.org/10.1007/s00170-018-2987-5.

    Article  Google Scholar 

  34. Wimmer, S., Hunyadi, P., & Zaeh, M. F. (2019). A numerical approach for the prediction of static surface errors in the peripheral milling of thin-walled structures. Production Engineering, 13(3), 479–488. https://doi.org/10.1007/s11740-019-00901-7.

    Article  Google Scholar 

  35. Wang, H., Zhou, M.-X., Zheng, W.-Z., Shi, Z.-B., & Li, H.-W. (2017). 3D machining allowance analysis method for the large thin-walled aerospace component. International Journal of Precision Engineering and Manufacturing, 18(3), 399–406. https://doi.org/10.1007/s12541-017-0048-x.

    Article  Google Scholar 

  36. Yan, C. Q., Zhao, J., Li, Y. E., & Han, S. G. (2009). Experimental research on surface roughness in high speed milling of complex surface mold steel. In (Vol. 626, pp. 123–128): Trans Tech Publication. https://doi.org/10.4028/www.scientific.net/MSF.626-627.123.

  37. Ren, W., Xu, J., Lin, J., Yu, Z., Yu, P., Lian, Z., et al. (2019). Research on homogenization and surface morphology of Ti-6Al-4V alloy by longitudinal-torsional coupled ultrasonic vibration ball-end milling. The International Journal of Advanced Manufacturing Technology, 104(1), 301–313. https://doi.org/10.1007/s00170-019-03668-4.

    Article  Google Scholar 

  38. Arruda, É. M., de Paiva, A. P., Brandão, L. C., & Ferreira, J. R. (2019). Robust optimisation of surface roughness of AISI H13 hardened steel in the finishing milling using ball nose end mills. Precision Engineering, 60, 194–214. https://doi.org/10.1016/j.precisioneng.2019.07.013.

    Article  Google Scholar 

  39. Zeroudi, N., & Fontaine, M. (2012). Prediction of machined surface geometry based on analytical modelling of ball-end milling. Procedia CIRP, 1, 108–113.

    Article  Google Scholar 

  40. Gang, L. (2009). Study on deformation of titanium thin-walled part in milling process. Journal of Materials Processing Technology, 209(6), 2788–2793. https://doi.org/10.1016/j.jmatprotec.2008.06.029.

    Article  Google Scholar 

  41. Ma, J.-W., Zhang, N., Chen, S.-Y., Su, W.-W., & Hu, G.-Q. (2018). Deformation analysing for thin-walled parts based on analysis of single-tooth or multi-tooth milling. International Journal of Machining and Machinability of Materials, 20(6), 575–593. https://doi.org/10.1504/IJMMM.2018.096383.

    Article  Google Scholar 

  42. Wang, L.-Y., Huang, H.-H., West, R. W., Li, H.-J., & Du, J.-T. (2018). A model of deformation of thin-wall surface parts during milling machining process. Journal of Central South University, 25(5), 1107–1115. https://doi.org/10.1007/s11771-018-3810-z.

    Article  Google Scholar 

  43. Han, Z., Jin, H., Fu, Y., & Fu, H. (2017). FEM numerical model and feedrate optimization based on-line deflection control of thin-walled parts in flank milling. Journal of Mechanical Engineering, 2017(21), 23. https://doi.org/10.3901/JME.2017.21.190.

    Article  Google Scholar 

  44. Senthil, V., Kumar, S., & Raman, B. (2018). An integrated approach of RSM and MOGA for the prediction of temperature rise and surface roughness in the end milling of Al 6061-T6. Transactions of FAMENA, 42(3), 115–128. https://doi.org/10.21278/TOF.42308.

    Article  Google Scholar 

  45. Bhushan, R. K., Kumar, S., & Das, S. (2010). Effect of machining parameters on surface roughness and tool wear for 7075 Al alloy SiC composite. The International Journal of Advanced Manufacturing Technology, 50(5), 459–469. https://doi.org/10.1007/s00170-010-2529-2.

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to the National Defense Basic Research Fund Project of China (Grant No. A0720133010) for supporting this research.

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Authors and Affiliations

  1. School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China

    De-Jun Cheng, Feng Xu, Sheng-Hao Xu, Chun-Yan Zhang & Sheng-Wen Zhang

  2. School of Mechanical Engineering, Gyeongsang National University, Jinju, 52828, Korea

    Su-Jin Kim

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  1. De-Jun Cheng
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Correspondence to Sheng-Wen Zhang.

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Cheng, DJ., Xu, F., Xu, SH. et al. Minimization of Surface Roughness and Machining Deformation in Milling of Al Alloy Thin-Walled Parts. Int. J. Precis. Eng. Manuf. 21, 1597–1613 (2020). https://doi.org/10.1007/s12541-020-00366-0

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