Chinese Journal of Mechanical Engineering >

2022 , Vol. 35 >Issue 5: 82 - 82

DOI: https://doi.org/10.1186/s10033-022-00746-9

An Initial Residual Stress Inference Method by Incorporating Monitoring Data and Mechanism Model

  • Shuguo Wang ,
  • Yingguang Li ,
  • Changqing Liu ,
  • Zhiwei Zhao
Expand
  • Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

Received date: 2021-08-20

  Revised date: 2021-11-17

  Online published: 2023-04-24

Supported by

Supported by National Natural Science Foundation of China (Grant No. 51775278) and National Science Fund of China for Distinguished Young Scholars (Grant No. 51925505).

Fold

Abstract

Initial residual stress is the main reason causing machining deformation of the workpiece, which has been deemed as one of the most important aspects of machining quality issues. The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control. Due to the principle error of existing residual stress detection methods, there are still challenges in practical applications. Aiming at the detection problem of the initial residual stress field, an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper. Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress, and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish; the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model. Finally, the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model. Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.

Key words： Initial residual stress; Inference; Monitoring data; Mechanism model; Policy gradient

Cite this article

Shuguo Wang , Yingguang Li , Changqing Liu , Zhiwei Zhao . An Initial Residual Stress Inference Method by Incorporating Monitoring Data and Mechanism Model[J]. Chinese Journal of Mechanical Engineering, 2022 , 35(5) : 82 -82 . DOI: 10.1186/s10033-022-00746-9

References

[1] B Denkena, D Boehnke, L D León. Machining induced residual stress in structural aluminum parts. Production Engineering, 2008, 2(3): 247-253.
[2] D Chantzis, S Van-Der-Veen, J Zettler, et al. An industrial workflow to minimise part distortion for machining large monolithic components in aerospace industry. Procedia CIRP, 2013, 8: 281-286.
[3] M C Santos, A R Machado, W F Sales, et al. Machining of aluminum alloys: A review. International Journal of Advanced Manufacturing Technology, 2016, 86: 3067-3080.
[4] S Masoudi, S Amini, E Saeidi, et al. Effect of machining-induced residual stress on the distortion of thin-walled parts. The International Journal of Advanced Manufacturing Technology, 2014, 76(1-4): 597-608.
[5] Z Guimu, Y Chao, S R Chen. Experimental study on the milling of thin parts of titanium alloy (TC4). Journal of Materials Processing Technology, 2003, 138(1-3): 489-493.
[6] X Cerutti, K Mocellin. Prediction of post-machining distortion due to residual stresses using fem and a massive removal approach. Key Engineering Materials, 2014, 611-612: 1159-1165.
[7] Y Yang, L Jin, J Du, et al. Residual stress relaxation of thin-walled long stringer made of aluminum alloy 7050-t7451 under transportation vibration. Chinese Journal of Mechanical Engineering, 2020, 33: 158-167.
[8] J Sun. Study on machining distortion of unitization airframe due to residual stress. Journal of Mechanical Engineering, 2005, 41(2): 117-122. (in Chinese)
[9] X M Yuan, J Zhang, Y Lian, et al. Research progress of residual stress determination in magnesium alloys. Journal of Magnesium and Alloys, 2018, 6(3): 238-244.
[10] A Yazdanmehr, H Jahed. On the surface residual stress measurement in magnesium alloys using X-ray diffraction. Materials, 2020,13(22): 5190.
[11] Y Zhan, C Liu, J J Zhang. Measurement of residual stress in laser additive manufacturing tc4 titanium alloy with the laser ultrasonic technique. Materials Science and Engineering: A, 2019, 762: 138093.
[12] W T Song, C G Xu, Q X Pan, et al. Nondestructive testing and characterization of residual stress field using an ultrasonic method. Chinese Journal of Mechanical Engineering, 2016, 29(2): 365-371.
[13] Q Pan, R Pan, C Shao, et al. Research review of principles and methods for ultrasonic measurement of axial stress in bolts. Chinese Journal of Mechanical Engineering, 2020, 33: 44-59.
[14] D Kaisheva, G Bokuchava, I Papushkin, et al. Neutron diffraction measurement of residual stresses in electron beam welded low carbon steel. Comptes Rendus de l'Académie Bulgare des Sciences: Sciences Mathématiques et Naturelles, 2020, 73(4): 475-484.
[15] Y K Zhang, A X Feng, J Z Lu, et al. Nondestructive measurement of the residual stress tin thin film coated on aisi 304 substrate by X-ray stress analyzer. Proceedings of SPIE - The International Society for Optical Engineering, 2006.
[16] M R Viotti, A Albertazzi, P Staron, et al. Measurement of residual stress fields in FHPP welding: A comparison between DSPI combined with hole-drilling and neutron diffraction. SPIE Optical Metrology, 2013.
[17] H Y Liu, C G Xu, W T Song, et al. Research on making constant valve block of residual compressive stress based on ultrasonic testing. Applied Mechanics and Materials, 2014, 494-495: 655-658.
[18] P K Taraphdar, J G Thakare, C Pandey, et al. Novel residual stress measurement technique to evaluate through thickness residual stress fields. Materials Letters, 2020, 277: 128347.
[19] A S Maxwell, A Turnbull. Measurement of residual stress in engineering plastics using the hole-drilling technique. Polymer Testing, 2003, 22(2): 231-233.
[20] L B Liu, J F Sun, W Y Chen, et al. Study on distribution of initial residual stress in 7075T651 aluminium alloy plate. China Mechanical Engineering, 2016, 27(4): 537-543. (in Chinese)
[21] T Tokuda, R G Wang, M Kido, et al. Measuring residual stress with the indentation method in structural ceramics. Key Engineering Materials, 2005, 297-300(Pt1): 515-520.
[22] P Pagliaro, M B Prime, H Swenson, et al. Measuring multiple residual-stress components using the contour method and multiple cuts. Experimental Mechanics, 2010, 50(2): 187-194.
[23] F Wang, K Mao, B Li. Prediction of residual stress fields from surface stress measurements. International Journal of Mechanical Sciences, 2018: 68-82.
[24] F Wang, K Mao, S Wu, et al. Bivariate Fourier-series-based prediction of surface residual stress fields using stresses of partial points. Mathematics and Mechanics of Solids, 2018, 24(4): 979-995.
[25] M I Hatamleh, J Mahadevan, A Malik, et al. Prediction of residual stress random fields for selective laser melted A357 aluminum alloy subjected to laser shock peening. Journal of Manufacturing Science and Engineering, 2019, 141(10): 609-636.
[26] J R Chukkan, G Y Wu, M E Fitzpatrick, et al. An iterative technique for the reconstruction of residual stress fields in a butt-welded plate from experimental measurement, and comparison with welding process simulation. International Journal of Mechanical Sciences, 2019,160: 421-428.
[27] G H Farrahi, S A Faghidian, D J Smith. An inverse method for reconstruction of the residual stress field in welded plates. Journal of Pressure Vessel Technology, 2010, 132(6): 1521-1523.
[28] O Ozmen, C Sinanolu, A Caliskan, et al. Prediction of leakage from an axial piston pump slipper with circular dimples using deep neural networks. Chinese Journal of Mechanical Engineering, 2020, 33: 119-129.
[29] B M Lake, R Salakhutdinov, J B Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 2015, 350(6266): 1332-1338.
[30] K Xu, Y G Li, C Q Liu, et al. Advanced data collection and analysis in data-driven manufacturing process. Chinese Journal of Mechanical Engineering, 2020, 33: 40-60.
[31] N Ruiz, S Schulter, M Chandraker. Learning to simulate. International Conference on Learning Representations, 2019.
[32] M Raissi, P Perdikaris, G Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, 378: 686-707.
[33] M Lutter, C Ritter, J Peters. Deep Lagrangian networks: Using physics as model prior for deep learning. https://arxiv.org/abs/1907.04490 (2021-08-03).
[34] S Greydanus, M Dzamba, J Yosinski. Hamiltonian neural networks. https://arxiv.org/abs/1906.01563 (2021-08-03).
[35] Z W Zhao, Y G Li, C Q Liu, et al. Predicting part deformation based on deformation force data using Physics-informed Latent Variable Model. Robotics and Computer-Integrated Manufacturing, 2021, 72: 102204.
[36] X Huang. Deformation mechanism and prediction of aluminum alloy monolithic component in the milling. Jinan: Shandong University, 2015. (in Chinese)
[37] R Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 1992, 8(3-4): 229-256.
Options
Outlines

/

〈 〉

　 Copyright © Chinese Journal of Mechanical Engineering, All Rights Reserved.
Powered by Beijing Magtech Co. Ltd,.