Towards an Assembly Support System with Dynamic Bayesian Network
Source abstract
Abstract
Due to the new technological advancements and the adoption of Industry 4.0 concepts, the manufacturing industry is now, more than ever, in a continuous transformation. This work analyzes the possibility of using dynamic Bayesian networks to predict the next assembly steps within an assembly assistance training system. The goal is to develop a support system to assist the human workers in their manufacturing activities. The evaluations were performed on a dataset collected from an experiment involving students. The experimental results show that dynamic Bayesian networks are appropriate for such a purpose, since their prediction accuracy was among the highest on new patterns. Our dynamic Bayesian network implementation can accurately recommend the next assembly step in 50% of the cases, but to the detriment of the prediction rate.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Manufacturing Process and Optimization · Digital Transformation in Industry · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania
Department of Industrial Engineering and Management, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania
IFM Prover, 557085 Sibiu, Romania
Funding
Lucian Blaga University of Sibiu
References (36)
Show source-provided references
1. Ruppert, T., Jaskó, S., Holczinger, T., and Abonyi, J. (2018). Enabling Technologies for Operator 4.0: A Survey. Appl. Sci., 8.10.3390/app8091650 ↗
2. Sun, S., Zheng, X., Gong, B., Paredes, J.G., and Ordieres-Meré, J. (2020). Healthy Operator 4.0: A Human Cyber–Physical System Architecture for Smart Workplaces. Sensors, 20.10.3390/s20072011 ↗
3. Assembly system design in the Industry 4.0 era: A general framework10.1016/j.ifacol.2017.08.1121 ↗
4. Miqueo, A., Torralba, M., and Yagüe-Fabra, J.A. (2020). Lean Manual Assembly 4.0: A Systematic Review. Appl. Sci., 10.10.3390/app10238555 ↗
5. Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context10.1016/j.cie.2017.09.016 ↗
6. Augmented reality smart glasses in industrial assembly: Current status and future challenges
7. Santi, G., Ceruti, A., Liverani, A., and Osti, F. (2021). Augmented Reality in Industry 4.0 and Future Innovation Programs. Technologies, 9.10.3390/technologies9020033 ↗
8. Human Factor Analyser for work measurement of manual manufacturing and assembly processes10.1007/s00170-019-03570-z ↗
9. Computer Vision-enabled Human-Cyber-Physical Workstations Collaboration for Reconfigurable Assembly System10.1016/j.promfg.2020.10.079 ↗
10. Tiziana, M., and Bernhard, S. (2021). Engineering a Digital Twin for Manual Assembling. Part IV. Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends, Proceedings of the 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, 20–30 October 2020, Springer.
11. Gellert, A., Precup, S.-A., Pirvu, B.-C., and Zamfirescu, C.-B. (2020, January 8–11). Prediction-Based Assembly Assistance System. Proceedings of the 25th IEEE International Conference on Emerging Technologies and Factory Automation, Vienna, Austria.10.1109/ETFA46521.2020.9212170 ↗
12. Gellert, A., Precup, S.-A., Pirvu, B.-C., Fiore, U., Zamfirescu, C.-B., and Palmieri, F. (2021). An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems. Appl. Sci., 11.10.3390/app11073278 ↗
13. Adaptive Qualification and Assistance Modules for Manual Assembly Workplaces10.1016/j.procir.2014.07.117 ↗
14. How Digital Assistance Systems Improve Work Productivity in Assembly
15. Approach to Increase Worker Acceptance of Cognitive Assistance Systems in Manual Assembly10.1016/j.procir.2019.03.229 ↗
16. Functionalities and Implementation of Future Informational Assistance Systems for Manual Assembly
17. Schüppstuhl, T., Tracht, K., and Franke, J. (2018). Intuitive Assembly Support System Using Augmented Reality. Tagungsband des 3. Kongresses Montage Handhabung Industrieroboter, Springer.10.1007/978-3-662-56714-2 ↗
18. Point cloud and visual feature-based tracking method for an augmented reality-aided mechanical assembly system10.1007/s00170-018-2575-8 ↗
19. Action Recognition in Manufacturing Assembly using Multimodal Sensor Fusion10.1016/j.promfg.2020.01.288 ↗
20. Zhang, Z., Wang, W., Chen, Y., Jia, Y., and Peng, G. (2019). Prediction of Human Actions in Assembly Process by a Spatial-Temporal End-to-End Learning Model. SAE Technical Paper 2019-01-0509, SAE International.10.4271/2019-01-0509 ↗
21. Hawkins, K.P., Vo, N., Bansal, S., and Bobick, A.F. (2013, January 15–17). Probabilistic human action prediction and wait-sensitive planning for responsive human-robot collaboration. Proceedings of the 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Atlanta, GA, USA.10.1109/HUMANOIDS.2013.7030020 ↗
22. Hybrid machine learning for human action recognition and prediction in assembly10.1016/j.rcim.2021.102184 ↗
23. Dojer, N., Gambin, A., Mizera, A., Wilczyński, B., and Tiuryn, J. (2006). Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinform., 7.10.1186/1471-2105-7-249 ↗
24. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data10.1093/bioinformatics/bth463 ↗
25. A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data10.1016/j.trc.2015.03.006 ↗
26. Exchange rate predictability and dynamic Bayesian learning10.1002/jae.2761 ↗
27. Du, Y., Chen, F., Xu, W., and Li, Y. (2006, January 20–24). Recognizing Interaction Activities using Dynamic Bayesian Network. Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, China.
28. Frigault, M., Wang, L., Singhal, A., and Jajodia, S. (2008, January 27). Measuring network security using dynamic bayesian network. Proceedings of the 4th ACM workshop on Quality of Protection, Alexandria, VA, USA.10.1145/1456362.1456368 ↗
29. Chapelle, O., and Zhang, Y. (2009, January 20–24). A dynamic bayesian network click model for web search ranking. Proceedings of the 18th International Conference on World Wide Web, Madrid, Spain.10.1145/1526709.1526711 ↗
30. Dzitac, I., Dzitac, S., Filip, F., Kacprzyk, J., Manolescu, M.J., and Oros, H. (2021). Using Two-Level Context-Based Predictors for Assembly Assistance in Smart Factories. Intelligent Methods in Computing, Communications and Control. ICCCC 2020. Advances in Intelligent Systems and Computing, Springer.10.1007/978-3-030-53651-0 ↗
31. Assembly support systems with Markov predictors10.1080/12460125.2020.1788798 ↗
32. Assembly Process Modeling through Long Short-Term Memory
33. Learning Dynamic Bayesian Networks
34. Ankan, A., and Panda, A. (2015, January 6–12). pgmpy: Probabilistic Graphical Models using Python. Proceedings of the 14th Python in Science Conference, Austin, TX, USA.10.25080/Majora-7b98e3ed-001 ↗
35. p-values, p-values everywhere!10.1080/00480169.2018.1415604 ↗
36. (2021, December 06). OpenVINO. Available online: https://docs.openvino.ai/latest/index.html.