727
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

A meta-reinforcement learning method by incorporating simulation and real data for machining deformation control of finishing process

, , , &
Pages 1114-1128 | Received 23 Aug 2021, Accepted 01 Jan 2022, Published online: 09 Feb 2022

References

  • Baalouch, M., M. Defurne, J. Poli, and N. Cherrier. 2019. “Sim-to-Real Domain Adaptation for High Energy Physics.” ArXiv, abs/1912.08001.
  • Botvinick, M., S. Ritter, J. X. Wang, Z. Kurth-Nelson, C. Blundell, and D. Hassabis. 2019. “Reinforcement Learning, Fast and Slow.” Trends in Cognitive Sciences 23: 408.
  • Cerutti, X., and K. Mocellin. 2016. “Influence of the Machining Sequence on the Residual Stress Redistribution and Machining Quality: Analysis and Improvement Using Numerical Simulations.” International Journal of Advanced Manufacturing Technology 83: 489.
  • D’Alvise, L., D. Chantzis, B. Schoinochoritis, and K. Salonitis. 2015. “Modelling of Part Distortion Due to Residual Stresses Relaxation: An Aeronautical Case Study.” Procedia CIRP 31: 447.
  • Dong, Y. B., W. Z. Shao, J. T. Jiang, B. Y. Zhang, and L. Zhen. 2015. “Minimization of Residual Stress in an Al-Cu Alloy Forged Plate by Different Heat Treatments.” Journal of Materials Engineering and Performance 24: 2256.
  • Eysenbach, B., J. Ibarz, A. Gupta, and S. Levine. 2019. “Diversity is All You Need: Learning Skills Without a Reward Function.” In 7th International Conference on Learning Representations, ICLR.
  • Finn, C., P. Abbeel, and S. Levine. 2017. “Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.” In 34th International Conference on Machine Learning, ICML.
  • Grant, E., C. Finn, S. Levine, T. Darrell, and T. Griffiths. 2018. “Recasting Gradient-Based Meta-Learning as Hierarchical Bayes.” ArXiv, abs/1801.08930.
  • Guo, J., Y. Cheng, D. Wang, F. Tao, and S. Pickl. 2021a. “Industrial Dataspace for Smart Manufacturing: Connotation, Key Technologies, and Framework.” International Journal of Production Research.
  • Guo, J., H. Fu, B. Pan, and R. Kang. 2021b. “Recent Progress of Residual Stress Measurement Methods: A Review.” Chinese Journal of Aeronautics 34: 54–78.
  • Gupta, A., B. Eysenbach, C. Finn, and S. Levine. 2018. “Unsupervised Meta-Learning for Reinforcement Learning.” ArXiv, abs/1806.04640.
  • Hao, X., Y. Li, G. Chen, and C. Liu. 2018. “6+X Locating Principle Based on Dynamic Mass Centers of Structural Parts Machined by Responsive Fixtures.” International Journal of Machine Tools and Manufacture 125: 112.
  • Hao, X., Y. Li, Z. Zhao, and C. Liu. 2019b. “Dynamic Machining Process Planning Incorporating In-Process Workpiece Deformation Data for Large-Size Aircraft Structural Parts.” International Journal of Computer Integrated Manufacturing 32: 136.
  • Huang, X., J. Sun, and J. Li. 2015. “Finite Element Simulation and Experimental Investigation on the Residual Stress-Related Monolithic Component Deformation.” International Journal of Advanced Manufacturing Technology 77: 1035.
  • Hurtado, J. F., and S. N. Melkote. 2002. “A Model for Synthesis of the Fixturing Configuration in Pin-Array Type Flexible Machining Fixtures.” International Journal of Machine Tools and Manufacture 42: 837.
  • Khayyati, S., and B. Tan. 2021. “A Machine Learning Approach for Implementing Data-Driven Production Control Policies.” International Journal of Production Research. Advance online publication. doi:10.1080/00207543.2021.1910872.
  • Krog, L. A., and N. Olhoff. 1999. “Optimum Topology and Reinforcement Design of Disk and Plate Structures with Multiple Stiffness and Eigen Frequency Objectives.” Computers and Structures 72: 535.
  • Lauro, C. H., L. C. Brandão, D. Baldo, R. A. Reis, and J. P. Davim. 2014. “Monitoring and Processing Signal Applied in Machining Processes – A Review.” Measurement: Journal of the International Measurement Confederation 58: 73.
  • Li, X., L. Li, Y. Yang, G. Zhao, N. He, X. Ding, Y. Shi, L. Fan, H. Lan, and M. Jamil. 2020. “Machining Deformation of Single-Sided Component Based on Finishing Allowance Optimization.” Chinese Journal of Aeronautics 33: 2434.
  • Li, C., T. Zhang, and D. I. Goldman. 2013. “A Terradynamics of Legged Locomotion on Granular Media.” Science 339: 1408.
  • Liu, C., Y. Li, J. Li, and J. Hua. 2021. “A Meta-Invariant Feature Space Method for Accurate Tool Wear Prediction Under Cross-Conditions.” IEEE Transactions on Industrial Informatics 18: 922.
  • Lu, L., J. Sun, X. Han, Q. Xiong, and G. Song. 2017. “Load Prediction Method of Rolling Distortion Correction for Monolithic Aeronautical Components Based on Energy Theory.” Acta Aeronautica et Astronautica Sinica 38 (12): 310–318. (in Chinese).
  • Panzer, M., and B. Bender. 2021. “Deep Reinforcement Learning in Production Systems: A Systematic Literature Review.” International Journal of Production Research. Advance online publication. doi:10.1080/00207543.2021.1973138.
  • Rai, R., M. K. Tiwari, D. Ivanov, and A. Dolgui. 2021. “Machine Learning in Manufacturing and Industry 4.0 Applications.” International Journal of Production Research 59: 4773–4778.
  • Ryll, M., T. N. Papastathis, and S. Ratchev. 2008. “Towards an Intelligent Fixturing System with Rapid Reconfiguration and Part Positioning.” Journal of Materials Processing Technology 201: 198.
  • Tobin, J., R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel. 2017. “Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World.” IEEE Int. Conf. Intell. Robot. Syst.
  • Vanschoren, J. 2018. “Meta-Learning: A Survey.” ArXiv, abs/1810.03548.
  • Volodymyr, M., K. Koray, S. David, A. Rusu  Andrei, V. Joel, G. Bellemare Marc, G. Alex, R. Martin, K. Fidjeland Andreas, and O. Georg. 2015. “Human-Level Control Through Deep Reinforcement Learning.” Nature 518 (7540): 529–533.
  • Wang, J. X., Z. Kurth-Nelson, H. Soyer, J. Z. Leibo, D. Tirumala, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick. 2017. “Learning to Reinforcement Learn.” ArXiv, abs/1611.05763.
  • Wang, M. H., Z. H. Liu, and H. J. Wang. 2011. “Study on residual stress for high-speed cutting titanium alloy based on finite element method.” Advanced Materials Research 188: 216.
  • Wang, Z., J. Sun, L. Liu, R. Wang, and W. Chen. 2019. “An Analytical Model to Predict the Machining Deformation of Frame Parts Caused by Residual Stress.” Journal of Materials Processing Technology 274: 116282.
  • Weber, D., B. Kirsch, C. R. Chighizola, C. R. D’Elia, B. S. Linke, M. R. Hill, and J. C. Aurich. 2021. “Analysis of Machining-Induced Residual Stresses of Milled Aluminum Workpieces, Their Repeatability, and Their Resulting Distortion.” International Journal of Advanced Manufacturing Technology 115: 1089–1110.
  • Xu, K., Y. Li, C. Liu, X. Liu, X. Hao, J. Gao, and P. G. Maropoulos. 2020. “Advanced Data Collection and Analysis in Data-Driven Manufacturing Process.” Chinese Journal of Mechanical Engineering. 33.
  • Xu, Z., H. van Hasselt, and D. Silver. 2018. “Meta-Gradient Reinforcement Learning.” In Advances in Neural Information Processing Systems.
  • Yang, S., and Z. Xu. 2021. “Intelligent Scheduling and Reconfiguration Via Deep Reinforcement Learning in Smart Manufacturing.” International Journal of Production Research 0: 1–18.
  • Zhang, D., Y. Hou, M. Yang, and B. Wu. 2014. “Intelligent Machining Process Leads Future Direction of Machine Tools.” Aeronautical Manufacturing Technology 11: 34–38. (in Chinese).
  • Zhang, Z., Y. Yang, L. Li, and J. Kong. 2021. “Experimental and Computational Modeling of Bulk Residual Stress for Aeronautical Components with Distinct Geometries.” Advances Mechanical Engineering 13: 1–14.
  • Zhao, Z., Y. Li, C. Liu, and X. Liu. 2021. “Predicting Part Deformation Based on Deformation Force Data Using Physics-Informed Latent Variable Model.” Robotics and Computer-Integrated Manufacturing 72: 102204.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.