Experimental investigation and modelling of residual stresses in face milling of Al-6061-T3 using neural network

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.267032

Keywords:

face milling, X-ray diffraction (XRD), residual stress (RS), aluminum alloy (AA 6061-T3), artificial neural network (ANN)

Abstract

Milling process is a common machining operation that is used in the manufacturing of complex surfaces. Machining-induced residual stresses (RS) have a great impact on the performance of machined components and the surface quality in face milling operations with parameter cutting. The properties of engineering material as well as structural components, specifically fatigue life, deformation, impact resistance, corrosion resistance, and brittle fracture, can all be significantly influenced by residual stresses. Accordingly, controlling the distribution of residual stresses is indeed important to protect the piece and avoid failure. Most of the previous works inspected the material properties, tool parameters, or cutting parameters, but few of them provided the distribution of RS in a direct and singular way. This work focuses on studying and optimizing the effect of cutting speed, feed rate, and depth of cut for 6061-T3 aluminum alloy on the RS of the surface. The optimum values of geometry parameters have been found by using the L27 orthogonal array. Analysis and simulation of RS by using an artificial neural network (ANN) were carried out to predict the RS behavior due to changing machining process parameters. Using ANN to predict the behavior of RS due to changing machining process parameters is presented as a promising method. The milling process produces more RS at high cutting speed, roughly intermediate feed rate, and deeper cut, according to the results. The best residual stress obtained from ANN is ‒135.204 N/mm2 at a cutting depth of 5 mm, feed rate of 0.25 mm/rev and cutting speed of 1,000 rpm. ANN can be considered a powerful tool for estimating residual stress

Supporting Agency

  • We sincerely thank the Al-Khwarizmi College of Engineering and the mechanical applied Laboratory of the Department of Automated Manufacturing Engineering for their assistance in carrying out this work.

Author Biographies

Basma L. Mahdi, Al-Khwarizmi College of Engineering, University of Baghdad

Master of Automated Manufucturing Engineering

Department of Automated Manufacturing Engineering

Huda H. Dalef, Al-Khwarizmi College of Engineering, University of Baghdad

PhD of Automated Manufucturing Engineering

Department of Automated Manufacturing Engineering

Hiba K. Hussein, Al-Khwarizmi College of Engineering, University of Baghdad

Master of Automated Manufucturing Engineering

Department of Automated Manufacturing Engineering

References

  1. Treuting, R. G., Read, W. T. (1951). A Mechanical Determination of Biaxial Residual Stress in Sheet Materials. Journal of Applied Physics, 22 (2), 130–134. doi: https://doi.org/10.1063/1.1699913
  2. Lucca, D. A., Brinksmeier, E., Goch, G. (1998). Progress in Assessing Surface and Subsurface Integrity. CIRP Annals, 47 (2), 669–693. doi: https://doi.org/10.1016/s0007-8506(07)63248-x
  3. Tang, Z. T., Liu, Z. Q., Pan, Y. Z., Wan, Y., Ai, X. (2009). The influence of tool flank wear on residual stresses induced by milling aluminum alloy. Journal of Materials Processing Technology, 209 (9), 4502–4508. doi: https://doi.org/10.1016/j.jmatprotec.2008.10.034
  4. Masmiati, N., Sarhan, A. A. D., Hassan, M. A. N., Hamdi, M. (2016). Optimization of cutting conditions for minimum residual stress, cutting force and surface roughness in end milling of S50C medium carbon steel. Measurement, 86, 253–265. doi: https://doi.org/10.1016/j.measurement.2016.02.049
  5. Wu, Q., Li, D.-P., Zhang, Y.-D. (2016). Detecting Milling Deformation in 7075 Aluminum Alloy Aeronautical Monolithic Components Using the Quasi-Symmetric Machining Method. Metals, 6 (4), 80. doi: https://doi.org/10.3390/met6040080
  6. Hlembotska, L., Melnychuk, P., Balytska, N., Melnyk, O. (2018). Modelling the loading of the nose-free cutting edges of face mill with a spiral-stepped arrangement of inserts. Eastern-European Journal of Enterprise Technologies, 1 (1 (91)), 46–54. doi: https://doi.org/10.15587/1729-4061.2018.121712
  7. Huang, X., Sun, J., Li, J. (2015). Experimental investigation of the effect of tool geometry on residual stresses in high speed milling 7050-T7451 aluminium alloy. International Journal of Surface Science and Engineering, 9 (4), 359. doi: https://doi.org/10.1504/ijsurfse.2015.070813
  8. Mia, M., Bashir, M. A., Khan, M. A., Dhar, N. R. (2016). Optimization of MQL flow rate for minimum cutting force and surface roughness in end milling of hardened steel (HRC 40). The International Journal of Advanced Manufacturing Technology, 89 (1-4), 675–690. doi: https://doi.org/10.1007/s00170-016-9080-8
  9. Mumali, F. (2022). Artificial neural network-based decision support systems in manufacturing processes: A systematic literature review. Computers & Industrial Engineering, 165, 107964. doi: https://doi.org/10.1016/j.cie.2022.107964
  10. Faizin, A., I Made Londen, B., Pramono, A. S., Wahjudi, A. (2021). Determination of the effect of thickness reduction ratio, die angle, and coefficient of friction on residual stresses in ironing process: an analysis using computer simulation. Eastern-European Journal of Enterprise Technologies, 5 (1 (113)), 70–78. doi: https://doi.org/10.15587/1729-4061.2021.243245
  11. Yao, C., Dou, X., Wu, D., Zhou, Z., Zhang, J. (2016). Surface integrity and fatigue analysis of shot-peening for 7055 aluminum alloy under different high-speed milling conditions. Advances in Mechanical Engineering, 8 (10), 168781401667462. doi: https://doi.org/10.1177/1687814016674628
  12. Jiang, X., Zhang, Z., Ding, Z., Fergani, O., Liang, S. Y. (2017). Tool overlap effect on redistributed residual stress and shape distortion produced by the machining of thin-walled aluminum parts. The International Journal of Advanced Manufacturing Technology, 93 (5-8), 2227–2242. doi: https://doi.org/10.1007/s00170-017-0693-3
  13. Ji, C., Sun, S., Lin, B., Fei, J. (2018). Effect of cutting parameters on the residual stress distribution generated by pocket milling of 2219 aluminum alloy. Advances in Mechanical Engineering, 10 (12), 168781401881305. doi: https://doi.org/10.1177/1687814018813055
  14. Perez, I., Madariaga, A., Cuesta, M., Garay, A., Arrazola, P. J., Ruiz, J. J. et al. (2018). Effect of cutting speed on the surface integrity of face milled 7050-T7451 aluminium workpieces. Procedia CIRP, 71, 460–465. doi: https://doi.org/10.1016/j.procir.2018.05.034
  15. Mohammadpour, M., Razfar, M. R., Jalili Saffar, R. (2010). Numerical investigating the effect of machining parameters on residual stresses in orthogonal cutting. Simulation Modelling Practice and Theory, 18 (3), 378–389. doi: https://doi.org/10.1016/j.simpat.2009.12.004
  16. Schajer, G. S. (1981). Application of Finite Element Calculations to Residual Stress Measurements. Journal of Engineering Materials and Technology, 103 (2), 157–163. doi: https://doi.org/10.1115/1.3224988
  17. Afazov, S. M., Becker, A. A., Hyde, T. H. (2012). Mathematical Modeling and Implementation of Residual Stress Mapping From Microscale to Macroscale Finite Element Models. Journal of Manufacturing Science and Engineering, 134 (2). doi: https://doi.org/10.1115/1.4006090
  18. Fuh, K.-H., Wu, C.-F. (1995). A residual-stress model for the milling of aluminum alloy (2014-T6). Journal of Materials Processing Technology, 51 (1-4), 87–105. doi: https://doi.org/10.1016/0924-0136(94)01355-5
  19. Zhou, R., Yang, W. (2019). Analytical modeling of machining-induced residual stresses in milling of complex surface. The International Journal of Advanced Manufacturing Technology, 105 (1-4), 565–577. doi: https://doi.org/10.1007/s00170-019-04219-7
  20. Huang, X., Sun, J., Li, J., Han, X., Xiong, Q. (2013). An Experimental Investigation of Residual Stresses in High-Speed End Milling 7050-T7451 Aluminum Alloy. Advances in Mechanical Engineering, 5, 592659. doi: https://doi.org/10.1155/2013/592659
  21. El-Axir, M. H. (2002). A method of modeling residual stress distribution in turning for different materials. International Journal of Machine Tools and Manufacture, 42 (9), 1055–1063. doi: https://doi.org/10.1016/s0890-6955(02)00031-7
  22. Cheng, M., Jiao, L., Yan, P., Feng, L., Qiu, T., Wang, X., Zhang, B. (2021). Prediction of surface residual stress in end milling with Gaussian process regression. Measurement, 178, 109333. doi: https://doi.org/10.1016/j.measurement.2021.109333
  23. Davis, J. R. (2001). Aluminum and Aluminum Alloys.‏ ASM Internationa. Available at: https://materialsdata.nist.gov/bitstream/handle/11115/173/Aluminum%20and%20Aluminum%20Alloys%20Davis.pdf
  24. Standard test methods for tension testing wrought and cast aluminum- and magnesium-alloy products (Metric) (2015). ASTM International.
  25. Baden, A. S. (2017). Prediction the effect of milling parameters upon the residual stresses through using taghuchi method. Iraqi journal of mechanical and material engineering, 17 (2), 211–222. Available at: https://www.iasj.net/iasj/download/bdfade35166a8370
  26. Muñoz-Escalona, P., Maropoulos, P. G. (2015). A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools. Journal of Manufacturing Systems, 36, 216–223. doi: https://doi.org/10.1016/j.jmsy.2014.06.011
  27. Sova, O., Shyshatskyi, A., Zhuravskyi, Y., Salnikova, O., Zubov, O., Zhyvotovskyi, R. et al. (2020). Development of a methodology for training artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 2 (4 (104)), 6–14. doi: https://doi.org/10.15587/1729-4061.2020.199469
  28. Silva, D. P., Bastos, I. N., Fonseca, M. C. (2020). Influence of surface quality on residual stress of API 5L X80 steel submitted to static load and its prediction by artificial neural networks. The International Journal of Advanced Manufacturing Technology, 108 (11-12), 3753–3764. doi: https://doi.org/10.1007/s00170-020-05621-2
  29. Nouioua, M., Laouissi, A., Yallese, M. A., Khettabi, R., Belhadi, S. (2021). Multi-response optimization using artificial neural network-based GWO algorithm for high machining performance with minimum quantity lubrication. The International Journal of Advanced Manufacturing Technology, 116 (11-12), 3765–3778. doi: https://doi.org/10.1007/s00170-021-07745-5
  30. Jebaraj, M., Pradeep Kumar, M., Yuvaraj, N., Mujibar Rahman, G. (2019). Experimental study of the influence of the process parameters in the milling of Al6082-T6 alloy. Materials and Manufacturing Processes, 34 (12), 1411–1427. doi: https://doi.org/10.1080/10426914.2019.1594271
  31. Reimer, A., Luo, X. (2018). Prediction of residual stress in precision milling of AISI H13 steel. Procedia CIRP, 71, 329–334. doi: https://doi.org/10.1016/j.procir.2018.05.036
Experimental investigation and modelling of residual stresses in face milling of Al-6061-T3 using neural network

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Published

2022-12-30

How to Cite

Mahdi, B. L., Dalef, H. H., & Hussein, H. K. (2022). Experimental investigation and modelling of residual stresses in face milling of Al-6061-T3 using neural network. Eastern-European Journal of Enterprise Technologies, 6(1 (120), 16–24. https://doi.org/10.15587/1729-4061.2022.267032

Issue

Vol. 6 No. 1 (120) (2022): Engineering technological systems

Section

Engineering technological systems

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Copyright (c) 2022 Basma L. Mahdi, Huda H. Dalef, Hiba K. Hussein

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