A digital twin-driven part spatio-temporal quality prediction framework integrated with equipment degradation state analysis

References

• Ait-El-Cadi, A., A. Gharbi, K. Dhouib, and A. Artiba. 2021. “Integrated Production, Maintenance and Quality Control Policy for Unreliable Manufacturing Systems Under Dynamic Inspection.” International Journal of Production Economics 236 (June): 108140. https://doi.org/10.1016/j.ijpe.2021.108140.
   Web of Science ®Google Scholar
• Allegri, G. 2020. “A Unified Formulation for Fatigue Crack Onset and Growth via Cohesive Zone Modelling.” Journal of the Mechanics and Physics of Solids 138 (May): 103900. https://doi.org/10.1016/j.jmps.2020.103900.
   Google Scholar
• Bai, Y., Z. Sun, B. Zeng, J. Long, L. Li, J. V. de Oliveira, and C. Li. 2019. “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. https://doi.org/10.1007/s10845-017-1388-1.
   Web of Science ®Google Scholar
• Bai, Y., J. Xie, D. Wang, W. Zhang, and C. Li. 2021. “A Manufacturing Quality Prediction Model Based on AdaBoost-LSTM with Rough Knowledge.” Computers & Industrial Engineering 155 (May): 155. https://doi.org/10.1016/j.cie.2021.107227.
   Web of Science ®Google Scholar
• Benkedjouh, T., K. Medjaher, N. Zerhouni, and S. Rechak. 2015. “Health Assessment and Life Prediction of Cutting Tools Based on Support Vector Regression.” Journal of Intelligent Manufacturing 26 (2): 213–223. https://doi.org/10.1007/s10845-013-0774-6.
   Web of Science ®Google Scholar
• Cai, H. X., J. M. Zhu, and W. Zhang. 2021. “Quality Deviation Control for Aircraft Using Digital Twin.” Journal of Computing and Information Science in Engineering 21 (3): 3. https://doi.org/10.1115/1.4050376.
   Web of Science ®Google Scholar
• Cheng, H., X. Kong, G. Chen, Q. Wang, and R. Wang. 2021. “Transferable Convolutional Neural Network Based Remaining Useful Life Prediction of Bearing Under Multiple Failure Behaviors.” Measurement 168 (January): 108286. https://doi.org/10.1016/j.measurement.2020.108286.
   Google Scholar
• Cheng, G., and L. Li. 2020. “Joint Optimization of Production, Quality Control and Maintenance for Serial-Parallel Multistage Production Systems.” Reliability Engineering & System Safety 204 (December): 204. https://doi.org/10.1016/j.ress.2020.107146.
   Web of Science ®Google Scholar
• Chen, G. G., P. Wang, B. Feng, Y. H. Li, and D. K. Liu. 2020. “The Framework Design of Smart Factory in Discrete Manufacturing Industry Based on Cyber-Physical System.” International Journal of Computer Integrated Manufacturing 33 (1): 79–101. https://doi.org/10.1080/0951192X.2019.1699254.
   Web of Science ®Google Scholar
• Cui, P.-H., J.-Q. Wang, and Y. Li. 2021. “Data-Driven Modelling, Analysis and Improvement of Multistage Production Systems with Predictive Maintenance and Product Quality.” September in International Journal of Production Research, 1–18. Taylor & Francis. https://doi.org/10.1080/00207543.2021.1962558.
   Google Scholar
• Diaz-Rozo, J., C. Bielza, and P. Larranaga. 2020. “Machine-Tool Condition Monitoring with Gaussian Mixture Models-Based Dynamic Probabilistic Clustering.” Engineering Applications of Artificial Intelligence 89 (March): 103434. https://doi.org/10.1016/j.engappai.2019.103434.
   Web of Science ®Google Scholar
• Duan, Y., L. Hou, and S. Leng. 2021. “A Novel Cutting Tool Selection Approach Based on a Metal Cutting Process Knowledge Graph.” The International Journal of Advanced Manufacturing Technology 112 (11–12): 3201–3214. https://doi.org/10.1007/s00170-021-06606-5.
   Web of Science ®Google Scholar
• Fabregat, A., F. Korninger, G. Viteri, K. Sidiropoulos, P. Marin-Garcia, P. Ping, G. Wu, L. Stein, P. D’Eustachio, H. Hermjakob, and T. Poisot. 2018. “Reactome Graph Database: Efficient Access to Complex Pathway Data.” Plos Computational Biology 14 (1): e1005968. https://doi.org/10.1371/journal.pcbi.1005968.
   PubMed Web of Science ®Google Scholar
• Kingma, D., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.” Computer Science, arXiv. http://arxiv.org/abs/1412.6980v8.
   Google Scholar
• Kong, Z., Y. Cui, Z. Xia, and H. Lv. 2019. “Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics.” Applied Sciences 9 (19): 4156. https://doi.org/10.3390/app9194156.
   Google Scholar
• Kong, X., G. Mao, Q. Wang, H. Ma, and W. Yang. 2020. “A Multi-Ensemble Method Based on Deep Auto-Encoders for Fault Diagnosis of Rolling Bearings.” Measurement 151 (February): 107132. https://doi.org/10.1016/j.measurement.2019.107132.
   Web of Science ®Google Scholar
• Lei, Y., N. Li, L. Guo, N. Li, T. Yan, and J. Lin. 2018. “Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction.” Mechanical Systems and Signal Processing 104 (May): 799–834. https://doi.org/10.1016/j.ymssp.2017.11.016.
   Google Scholar
• Liu, J., X. Cao, H. Zhou, L. Li, X. Liu, P. Zhao, and J. Dong. 2021. “A Digital Twin-Driven Approach Towards Traceability and Dynamic Control for Processing Quality.” Advanced Engineering Informatics 50 (October): 101395. https://doi.org/10.1016/j.aei.2021.101395.
   Web of Science ®Google Scholar
• Liu, Y., H. Cheng, X. Kong, Q. Wang, and H. Cui. 2019. “Intelligent Wind Turbine Blade Icing Detection Using Supervisory Control and Data Acquisition Data and Ensemble Deep Learning.” Energy Science & Engineering 7 (6): 2633–2645. https://doi.org/10.1002/ese3.449.
   Web of Science ®Google Scholar
• Liu, Y., X. Hu, and W. Zhang. 2019. “Remaining Useful Life Prediction Based on Health Index Similarity.” Reliability Engineering & System Safety 185 (May): 502–510. https://doi.org/10.1016/j.ress.2019.02.002.
   Google Scholar
• Liu, K., L. Song, W. Han, Y. M. Cui, and Y. Q. Wang. 2022. “Time-Varying Error Prediction and Compensation for Movement Axis of CNC Machine Tool Based on Digital Twin.” IEEE Transactions on Industrial Informatics 18 (1): 109–118. https://doi.org/10.1109/TII.2021.3073649.
   Web of Science ®Google Scholar
• Liu, C., L. Zhang, J. Niu, R. Yao, and C. Wu. 2020. “Intelligent Prognostics of Machining Tools Based on Adaptive Variational Mode Decomposition and Deep Learning Method with Attention Mechanism.” Neurocomputing 417 (December): 239–254. https://doi.org/10.1016/j.neucom.2020.06.116.
   Google Scholar
• Liu, T., and K. Zhu. 2021. “A Switching Hidden Semi-Markov Model for Degradation Process and Its Application to Time-Varying Tool Wear Monitoring.” IEEE Transactions on Industrial Informatics 17 (4): 2621–2631. https://doi.org/10.1109/TII.2020.3004445.
   Web of Science ®Google Scholar
• Li, J., B. Yang, H. Li, Y. Wang, C. Qi, and Y. Liu. 2021. “DTDR–ALSTM: Extracting Dynamic Time-Delays to Reconstruct Multivariate Data for Improving Attention-Based LSTM Industrial Time Series Prediction Models.” Knowledge-Based Systems 211 (January): 106508. https://doi.org/10.1016/j.knosys.2020.106508.
   Web of Science ®Google Scholar
• Lu, B., and X. Zhou. 2017. “Opportunistic Preventive Maintenance Scheduling for Serial-Parallel Multistage Manufacturing Systems with Multiple Streams of Deterioration.” Reliability Engineering & System Safety 168 (December): 116–127. https://doi.org/10.1016/j.ress.2017.05.017.
   Google Scholar
• Lu, B., and X. Zhou. 2019. “Quality and Reliability Oriented Maintenance for Multistage Manufacturing Systems Subject to Condition Monitoring.” Journal of Manufacturing Systems 52 (July): 76–85. https://doi.org/10.1016/j.jmsy.2019.04.003.
   Google Scholar
• Ma, L., J. Dong, and K. Peng. 2020. “A Novel Robust Semisupervised Classification Framework for Quality-Related Coupling Faults in Manufacturing Industries.” IEEE Transactions on Industrial Informatics 16 (5): 2946–2955. https://doi.org/10.1109/TII.2019.2901931.
   Web of Science ®Google Scholar
• Papananias, M., T. E. McLeay, M. Mahfouf, and V. Kadirkamanathan. 2022. “A Bayesian Information Fusion Approach for End Product Quality Estimation Using Machine Learning and On-Machine Probing.” Journal of Manufacturing Processes 76 (April): 475–485. https://doi.org/10.1016/j.jmapro.2022.01.020.
   Google Scholar
• Paraschos, P. D., G. K. Koulinas, and D. E. Koulouriotis. 2020. “Reinforcement Learning for Combined Production-Maintenance and Quality Control of a Manufacturing System with Deterioration Failures.” Journal of Manufacturing Systems 56 (July): 470–483. https://doi.org/10.1016/j.jmsy.2020.07.004.
   Google Scholar
• Peng, K., R. Jiao, J. Dong, and Y. Pi. 2019. “A Deep Belief Network Based Health Indicator Construction and Remaining Useful Life Prediction Using Improved Particle Filter.” Neurocomputing 361 (October): 19–28. https://doi.org/10.1016/j.neucom.2019.07.075.
   Google Scholar
• Petrillo, A., A. Picariello, S. Santini, B. Scarciello, and G. Sperlí. 2020. “Model-Based Vehicular Prognostics Framework Using Big Data Architecture.” Computers in Industry 115 (February): 103177. https://doi.org/10.1016/j.compind.2019.103177.
   Google Scholar
• Ren, L., J. Cui, Y. Sun, and X. Cheng. 2017. “Multi-Bearing Remaining Useful Life Collaborative Prediction: A Deep Learning Approach.” High Performance Computing and Data Analytics for Cyber Manufacturing 43 (April): 248–256. https://doi.org/10.1016/j.jmsy.2017.02.013.
   Google Scholar
• Saha, R., A. Azeem, K. Wahadul Hasan, S. Mithun Ali, and S. Paul. 2021. “Integrated Economic Design of Quality Control and Maintenance Management: Implications for Managing Manufacturing Process.” International Journal of System Assurance Engineering & Management 12 (2): 263–280. https://doi.org/10.1007/s13198-021-01053-7.
   Web of Science ®Google Scholar
• Schnell, J., C. Nentwich, F. Endres, A. Kollenda, F. Distel, T. Knoche, and G. Reinhart. 2019. “Data Mining in Lithium-Ion Battery Cell Production.” Journal of Power Sources 413 (February): 360–366. https://doi.org/10.1016/j.jpowsour.2018.12.062.
   Google Scholar
• Tasias, K. A. 2022. “Integrated Quality, Maintenance and Production Model for Multivariate Processes: A Bayesian Approach.” Journal of Manufacturing Systems 63 (April): 35–51. https://doi.org/10.1016/j.jmsy.2022.02.008.
   Google Scholar
• Wang, J., R. X. Gao, Z. Yuan, Z. Fan, and L. Zhang. 2019. “A Joint Particle Filter and Expectation Maximization Approach to Machine Condition Prognosis.” Journal of Intelligent Manufacturing 30 (2): 605–621. https://doi.org/10.1007/s10845-016-1268-0.
   Web of Science ®Google Scholar
• Wang, P., and M. Luo. 2021. “A Digital Twin-Based Big Data Virtual and Real Fusion Learning Reference Framework Supported by Industrial Internet Towards Smart Manufacturing.” Journal of Manufacturing Systems 58 (January): 16–32. https://doi.org/10.1016/j.jmsy.2020.11.012.
   Google Scholar
• Wang, P., Y. Zhang, B. Feng, D. K. Liu, and S. T. Xie. 2021. “Investigation of Multivariate Profile Monitoring on Complex Thin-Walled Components Batch Machined Using a Sliding Time Window Cluster Method.” International Journal of Computer Integrated Manufacturing 34 (2): 134–152. https://doi.org/10.1080/0951192X.2020.1858500.
   Web of Science ®Google Scholar
• Wentao, M., J. He, and M. J. Zuo. 2020. “Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning.” IEEE Transactions on Instrumentation and Measurement 69 (4): 1594–1608. https://doi.org/10.1109/TIM.2019.2917735.
   Web of Science ®Google Scholar
• Wuest, T., C. Irgens, and K.-D. Thoben. 2014. “An Approach to Monitoring Quality in Manufacturing Using Supervised Machine Learning on Product State Data.” Journal of Intelligent Manufacturing 25 (5): 1167–1180. https://doi.org/10.1007/s10845-013-0761-y.
   Web of Science ®Google Scholar
• Yang, S., X. Kong, Q. Wang, Z. Li, H. Cheng, and L. Yu. 2021. “A Multi-Source Ensemble Domain Adaptation Method for Rotary Machine Fault Diagnosis.” Measurement 186 (December): 110213. https://doi.org/10.1016/j.measurement.2021.110213.
   Web of Science ®Google Scholar
• Yu, W., I. Y. Yong Kim, and M. Chris. 2019. “Remaining Useful Life Estimation Using a Bidirectional Recurrent Neural Network Based Autoencoder Scheme.” Mechanical Systems and Signal Processing 129 (August): 764–780. https://doi.org/10.1016/j.ymssp.2019.05.005.
   Google Scholar
• Zhang, C., K. Peng, and J. Dong. 2021. “An Extensible Quality-Related Fault Isolation Framework Based on Dual Broad Partial Least Squares with Application to the Hot Rolling Process.” Expert Systems With Applications 167 (April): 167. https://doi.org/10.1016/j.eswa.2020.114166.
   Web of Science ®Google Scholar
• Zhou, B., J. Bao, J. Li, Y. Lu, T. Liu, and Q. Zhang. 2021. “A Novel Knowledge Graph-Based Optimization Approach for Resource Allocation in Discrete Manufacturing Workshops.” Robotics and Computer-Integrated Manufacturing 71 (October): 102160. https://doi.org/10.1016/j.rcim.2021.102160.
   Web of Science ®Google Scholar
• Zhu, C., Q. Chang, and J. Arinez. 2020. “Data-Enabled Modeling and Analysis of Multistage Manufacturing Systems with Quality Rework Loops.” Journal of Manufacturing Systems 56 (July): 573–584. https://doi.org/10.1016/j.jmsy.2020.07.019.
   Google Scholar