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IJAT Vol.18 No.3 pp. 406-416
doi: 10.20965/ijat.2024.p0406
(2024)

Research Paper:

Evaluation Approach for Residual Stress in Drilling of Aluminum Alloy

Takashi Matsumura ORCID Icon, Yusuke Akao, and Shoichi Tamura ORCID Icon

Tokyo Denki University
5 Senju Asahi-cho, Adachi-ku, Tokyo 120-8551, Japan

Corresponding author

Received:
November 24, 2023
Accepted:
March 6, 2024
Published:
May 5, 2024
Keywords:
drilling, residual stress, cutting force, simulation, neural network
Abstract

Residual stress is one of the critical evaluation factors as well as machining accuracy and surface finish in cutting. From the perspective of the fastening strength of parts, residual stress in drilling has an influence on the product or part life. Especially in the aircraft manufacturing, the tensile residual stress is crucial as it can cause major accidents. However, few studies have focused on residual stress in drilling so far. Most of them investigated the characteristics of residual stress based on the experimental data. This paper discusses residual stress in drilling in terms of its mechanical effect. The residual stresses in the inner surfaces of holes were measured in the depth direction from the top of the plate. The change in the residual stress has good correlation with the chip flow direction and the load applied to the inner surface, which acts as a counter force to the cutting force near the outermost ends of the lips. The cutting force is determined using an analytical model based on the minimum cutting energy. The correlation between the residual stress and the direction of the load applied to the inner surface depends on the margin width and back taper on the peripheral sides of a drill. The effect of the margin contact on the residual stress is characterized by linear regression analysis. Finally, an analysis-neural network hybrid system was developed to estimate the residual stresses in drilling. In the system, the mechanical effects of the drilling process, which are the magnitude and direction of the load applied to the inner surface, are obtained through analytical simulation. Meanwhile, the margin effects are expressed as the coefficients in the linear functions, which are associated with the margin width and back taper angle by a neural network. Then, another neural network, working as a post process, estimates the residual stresses using the information of mechanical and margin effects. The developed system is validated by presenting a comparison between the estimated and the actual residual stresses.

Cite this article as:
T. Matsumura, Y. Akao, and S. Tamura, “Evaluation Approach for Residual Stress in Drilling of Aluminum Alloy,” Int. J. Automation Technol., Vol.18 No.3, pp. 406-416, 2024.
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References
  1. [1] P. Dahlman, F. Gunnberg, and M. Jacobson, “The influence of rake angle, cutting feed and cutting depth on residual stresses in hard turning,” J. of Materials Processing Technology, Vol.147, Issue 2, pp. 181-184, 2004. https://doi.org/10.1016/j.jmatprotec.2003.12.014
  2. [2] Z. Fangyuan, D. Chunzheng, S. Wei, and J. Kang, “Effects of cutting conditions on the microstructure and residual stress of white and dark layers in cutting hardened steel,” J. of Materials Processing Technology, Vol.266, pp. 599-611, 2019. https://doi.org/10.1016/j.jmatprotec.2018.11.038
  3. [3] D. Umbrello, G. Ambrogio, L. Filice, and R. Shivpuri, “An ANN approach for predicting subsurface residual stresses and the desired cutting conditions during hard turning,” J. of Materials Processing Technology, Vol.189, Issues 1-3, pp. 143-152, 2007. https://doi.org/10.1016/j.jmatprotec.2007.01.016
  4. [4] D. Nespor, B. Denkena, T. Grove, and V. Böß, “Differences and similarities between the induced residual stresses after ball end milling and orthogonal cutting of Ti–6Al–4V,” J. of Materials Processing Technology, Vol.226, pp. 15-24, 2015. https://doi.org/10.1016/j.jmatprotec.2015.06.033
  5. [5] A. Reimer and X. Luo, “Prediction of residual stress in precision milling of AISI H13 steel,” Procedia CIRP, Vol.71, pp. 329-224, 2018. https://doi.org/10.1016/j.procir.2018.05.036
  6. [6] M. Girinon, F. Dumont, F. Valiorgue, J. Rech, E. Feulvarch, F. Lefebvre, H. Karaouni, and E. Jourden, “Influence of lubrication modes on residual stresses generation in drilling of 316L, 15-PH and Inconel 718 alloys,” Procedia CIRP, Vol.71, pp. 41-46, 2018. https://doi.org/10.1016/j.procir.2018.05.020
  7. [7] K. C. Ee, O. W. Dillon, and I. S. Jawahir, “Finite element modeling of residual stresses in machining induced by cutting using a tool with finite edge radius,” Int. J. of Mechanical Science, Vol.47, Issue 10, pp. 1611-1628, 2005. https://doi.org/10.1016/j.ijmecsci.2005.06.001
  8. [8] H. Sasahara, T. Obikawa, and T. Shirakashi, “FEM analysis of cutting sequence effect on mechanical characteristics in machined layer,” J. of Materials Processing Technology, Vol.62, No.4, pp. 448-453, 1996. https://doi.org/10.1016/S0924-0136(96)02451-X
  9. [9] M. Dumas, D. Fabre, F. Valiorgue, G. Kermouche, A. V. Robaeys, M. Girinon, A. Brosse, H. Karaouni, and J. Rech, “3D numerical modelling of turning-induced residual stresses – A two-scale approach based on equivalent thermo-mechanical loadings,” J. of Materials Processing Technology, Vol.297, Article No.117274, 2021. https://doi.org/10.1016/j.jmatprotec.2021.117274
  10. [10] J. Hua, D. Umbrello, and R. Shivpuri, “Investigation of cutting conditions and cutting edge preparations for enhanced compressive subsurface residual stress in the hard turning of bearing steel,” J. of Materials Processing Technology, Vol.171, Issue 2, pp. 180-187, 2006. https://doi.org/10.1016/j.jmatprotec.2005.06.087
  11. [11] S. Q. Wang, J. G. Li, H. L. He, and R. A. Laghari, “An analytical model of residual stress in orthogonal cutting based on the radial return method,” J. of Materials Processing Technology, Vol.273, 116234, 2019. https://doi.org/10.1016/j.jmatprotec.2019.05.015
  12. [12] X. Liang, Z. Liu, B. Wang, Q. Song, Y. Cai, and Y. Wan, “Prediction of residual stress with multi-physics model for orthogonal cutting Ti-6Al-4V under various tool wear morphologies,” J. of Materials Processing Technology, Vol.288, Article No.116908, 2021. https://doi.org/10.1016/j.jmatprotec.2020.116908
  13. [13] B. Toubhans, G. Fromentin, F. Viprey, H. Karaouni, and T. Dorlin, “Machinability of Inconel 718 during turning: Cutting force model considering tool wear, influence on surface integrity,” J. of Materials Processing Technology, Vol.285, Article No.116809, 2020. https://doi.org/10.1016/j.jmatprotec.2020.116809
  14. [14] Y. Ma, P. Feng, J. Zhang, Z. Wu, and D. Yu, “Prediction of surface residual stress after end milling based on cutting force and temperature,” J. of Materials Processing Technology, Vol.235, pp. 41-48, 2016. https://doi.org/10.1016/j.jmatprotec.2016.04.002
  15. [15] X. Huang, X. Zhang, and H. Ding, “An enhanced analytical model of residual stress for peripheral milling,” Procedia CIRP, Vol.58, pp. 387-392, 2017. https://doi.org/10.1016/j.procir.2017.03.245
  16. [16] E. Usui, A. Hirota, and M. Masuko, “Analytical prediction of three dimensional cutting process: Part 1 basic cutting model and energy approach,” J. Manuf. Sci. Eng., Vol.100, Issue 2, pp. 222-228, 1978. https://doi.org/10.1115/1.3439413
  17. [17] T. Matsumura and J. Leopold, “Simulation of Drilling Process for Control of Burr Formation,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.4, No.5, pp. 966-975, 2010. https://doi.org/10.1299/jamdsm.4.966
  18. [18] T. Matsumura, T. Shirakashi, and E. Usui, “Adaptive Cutting Force Prediction in Milling Processes,” Int. J. Automation Technol., Vol.4, No.3, pp. 221-228, 2010. https://doi.org/10.20965/ijat.2010.p0221
  19. [19] S. Tamura, K. Sekigawa, and T. Matsumura, “Monitoring of Tool Wear Distribution with Cutting Force Measurement in Drilling,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.15, No.4, Article No.20-00480, 2021. https://doi.org/10.1299/jamdsm.2021jamdsm0047

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Last updated on May. 19, 2024