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Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems (2309.10193v1)

Published 18 Sep 2023 in cs.LG, cs.SY, and eess.SY

Abstract: The modeling of multistage manufacturing systems (MMSs) has attracted increased attention from both academia and industry. Recent advancements in deep learning methods provide an opportunity to accomplish this task with reduced cost and expertise. This study introduces a stochastic deep Koopman (SDK) framework to model the complex behavior of MMSs. Specifically, we present a novel application of Koopman operators to propagate critical quality information extracted by variational autoencoders. Through this framework, we can effectively capture the general nonlinear evolution of product quality using a transferred linear representation, thus enhancing the interpretability of the data-driven model. To evaluate the performance of the SDK framework, we carried out a comparative study on an open-source dataset. The main findings of this paper are as follows. Our results indicate that SDK surpasses other popular data-driven models in accuracy when predicting stagewise product quality within the MMS. Furthermore, the unique linear propagation property in the stochastic latent space of SDK enables traceability for quality evolution throughout the process, thereby facilitating the design of root cause analysis schemes. Notably, the proposed framework requires minimal knowledge of the underlying physics of production lines. It serves as a virtual metrology tool that can be applied to various MMSs, contributing to the ultimate goal of Zero Defect Manufacturing.

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References (49)
  1. Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research. International journal of production research, 58(1):1–17, 2020.
  2. Zero-defect manufacturing the approach for higher manufacturing sustainability in the era of industry 4.0: a position paper. International Journal of Production Research, 60(1):73–91, 2022.
  3. Multivariate control charts for monitoring covariance matrix: a review. Quality Technology & Quantitative Management, 3(4):415–436, 2006.
  4. A review of multivariate control charts. IIE Transactions, 27(6):800–810, 1995.
  5. A survey of inspection strategy and sensor distribution studies in discrete-part manufacturing processes. IIE Transactions, 38(4):309–328, 2006.
  6. Twofold variation propagation modeling and analysis for roll-to-roll manufacturing systems. IEEE Transactions on Automation Science and Engineering, 16(2):599–612, 2018.
  7. Le Yao and Zhiqiang Ge. Cooperative deep dynamic feature extraction and variable time-delay estimation for industrial quality prediction. IEEE Transactions on Industrial Informatics, 17(6):3782–3792, 2020.
  8. Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability. Journal of Manufacturing Systems, 63:381–391, 2022.
  9. A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction. Journal of Intelligent Manufacturing, 30(5):2245–2256, 2019.
  10. Virtual metrology as an approach for product quality estimation in industry 4.0: a systematic review and integrative conceptual framework. International Journal of Production Research, 60(2):742–765, 2022.
  11. Nonlinear dynamic soft sensor modeling with supervised long short-term memory network. IEEE Transactions on Industrial Informatics, 16(5):3168–3176, 2019.
  12. A wide-deep-sequence model-based quality prediction method in industrial process analysis. IEEE Transactions on Neural Networks and Learning Systems, 31(9):3721–3731, 2020.
  13. Real-time defect identification of narrow overlap welds and application based on convolutional neural networks. Journal of Manufacturing Systems, 62:800–810, 2022.
  14. Quality analysis in metal additive manufacturing with deep learning. Journal of Intelligent Manufacturing, 31(8):2003–2017, 2020.
  15. Virtual metrology of semiconductor pvd process based on combination of tree-based ensemble model. Isa Transactions, 103:192–202, 2020.
  16. A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes. Journal of Intelligent Manufacturing, 32:1173–1187, 2021.
  17. Jian Liu. Variation reduction for multistage manufacturing processes: a comparison survey of statistical-process-control vs stream-of-variation methodologies. Quality and Reliability Engineering International, 26(7):645–661, 2010.
  18. Data-driven analysis of product state propagation in manufacturing systems using visual analytics and machine learning. Procedia CIRP, 93:449–454, 2020.
  19. Cascade quality prediction method using multiple pca+ id3 for multi-stage manufacturing system. Ieri Procedia, 4:201–207, 2013.
  20. An adversarial bidirectional serial–parallel lstm-based qtd framework for product quality prediction. Journal of Intelligent Manufacturing, 31(6):1511–1529, 2020.
  21. Path enhanced bidirectional graph attention network for quality prediction in multistage manufacturing process. IEEE Transactions on Industrial Informatics, 18(2):1018–1027, 2021.
  22. Production quality prediction of multistage manufacturing systems using multi-task joint deep learning. Journal of Manufacturing Systems, 70:48–68, 2023.
  23. A bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing. computers in industry, 105:35–47, 2019.
  24. Monitoring and diagnosis of multistage manufacturing processes using hierarchical bayesian networks. Procedia Manufacturing, 53:32–43, 2021.
  25. A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing. Advances in Manufacturing, pages 1–15, 2023.
  26. Quality control and improvement for multistage systems: A survey. IIE Transactions, 41(9):744–753, 2009.
  27. Online stochastic control of dimensional quality in multistation manufacturing systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(5):865–880, 2007.
  28. Real-time quality monitoring and predicting model based on error propagation networks for multistage machining processes. Journal of Intelligent Manufacturing, 25:521–538, 2014.
  29. Deep multistage multi-task learning for quality prediction of multistage manufacturing systems. Journal of Quality Technology, 53(5):526–544, 2021.
  30. Design of multi-station manufacturing processes by integrating the stream-of-variation model and shop-floor data. Journal of Manufacturing Systems, 30(2):70–82, 2011.
  31. Process-oriented tolerancing for multi-station assembly systems. IIE Transactions, 37(6):493–508, 2005.
  32. Stream-of-quality methodology for industrial internet-based manufacturing system. Manufacturing Letters, 34:58–61, 2022.
  33. Bernard O Koopman. Hamiltonian systems and transformation in hilbert space. Proceedings of the National Academy of Sciences, 17(5):315–318, 1931.
  34. Peter J Schmid. Dynamic mode decomposition of numerical and experimental data. Journal of Fluid Mechanics, 656:5–28, 2010.
  35. A data–driven approximation of the koopman operator: Extending dynamic mode decomposition. Journal of Nonlinear Science, 25(6):1307–1346, 2015.
  36. Deep learning for universal linear embeddings of nonlinear dynamics. Nature Communications, 9(1):1–10, 2018.
  37. Stochastic adversarial koopman model for dynamical systems. arXiv preprint arXiv:2109.05095, 2021.
  38. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
  39. Multi-stage continuous-flow manufacturing process, 2020.
  40. Omogbai Oleghe. A predictive noise correction methodology for manufacturing process datasets. Journal of Big Data, 7(1):1–27, 2020.
  41. Multistage manufacturing process control robust to inaccurate knowledge about process noise. CIRP Annals, 66(1):437–440, 2017.
  42. Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index. Computer Physics Communications, 181(2):259–270, 2010.
  43. Sensitivity analysis for minimization of input data dimension for feedforward neural network. In Proceedings of IEEE International Symposium on Circuits and Systems-ISCAS’94, volume 6, pages 447–450. IEEE, 1994.
  44. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019.
  45. Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing. Journal of Manufacturing Systems, 64:197–210, 2022.
  46. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378:686–707, 2019.
  47. Zhiyi Chen. Control of High Precision Roll-to-Roll Printing Systems. PhD thesis, University of Michigan, 2023.
  48. A sustainable development framework for a cleaner multi-item multi-stage textile production system with a process improvement initiative. Journal of Cleaner Production, 246:119055, 2020.
  49. Andreas Se Ho Kugele and Biswajit Sarkar. Reducing carbon emissions of a multi-stage smart production for biofuel towards sustainable development. Alexandria Engineering Journal, 70:93–113, 2023.
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