IJAT Vol.15 No.2 pp. 168-181
doi: 10.20965/ijat.2021.p0168


An Ontology-Based Method for Semi-Automatic Disassembly of LCD Monitors and Unexpected Product Types

Gwendolyn Foo*,†, Sami Kara*, and Maurice Pagnucco**

*School of Mechanical and Manufacturing Engineering, The University of New South Wales (UNSW)
UNSW Sydney, New South Wales 2052, Australia

Corresponding author

**School of Computer Science and Engineering, The University of New South Wales (UNSW), Sydney, Australia

August 13, 2020
December 1, 2020
March 5, 2021
disassembly, LCD monitors, ontology, cognitive robotics, automation

Disassembly is a vital step in any treatment stream of waste electrical and electronic equipment (WEEE), preventing hazardous and toxic chemicals and materials from damaging the ecosystem. However, the large variations and uncertainties in WEEE is a major limitation to the implementation of automation and robotics in this field. Therefore, the advancement of robotic and automation intelligence to be flexible in handling a variety of situations in WEEE disassembly is sought after. This paper presents an ontology-based cognitive method for generating actions for the disassembly of WEEE, with a focus on LCD monitors, handling uncertainties throughout the disassembly process. The system utilizes reasoning about relationships between a typical LCD monitor product, component features, common fastener types, and actions that the system is capable of, to determine 4 key stages of robotic disassembly: component identification, fastener identification, disassembly action generation, and identification of disassembly extent. Further uncertainties in the form of possible failure of action execution is reasoned about to provide new actions, and any unusual scenarios that result in incorrect reasoning outputs are rectified with user-demonstration as a last resort. The proposed method is trialed for the disassembly of LCD monitors and a product unknown to the system, in the form of a DVD-ROM drive.

Cite this article as:
G. Foo, S. Kara, and M. Pagnucco, “An Ontology-Based Method for Semi-Automatic Disassembly of LCD Monitors and Unexpected Product Types,” Int. J. Automation Technol., Vol.15 No.2, pp. 168-181, 2021.
Data files:
  1. [1] M. Alfaro-Algaba and F. J. Ramŕez, “Techno-economic and environmental disassembly planning of lithium-ion electric vehicle battery packs for remanufacturing,” Resources, Conservation and Recycling, Vol.154, 104461, 2020.
  2. [2] J. Huang, D. T. Pham, Y. Wang, C. Ji, W. Xu, Q. Liu, and Z. Zhou, “A strategy for human-robot collaboration in taking products apart for remanufacture,” FME Trans., Vol.47, No.4, pp. 731-738, 2019.
  3. [3] J. Huang, D. T. Pham, Y. Wang, M. Qu, C. Ji, S. Su, W. Xu, Q. Liu, and Z. Zhou, “A case study in human-robot collaboration in the disassembly of press-fitted components,” Proc. of the Institution of Mechanical. Engineers, Part B, J. of Engineering Manufacture, Vol.234, No.3, pp. 654-664, 2020.
  4. [4] F. J. Ramírez, J. A. Aledo, J. A. Gamez, and D. T. Pham, “Economic modelling of robotic disassembly in end-of-life product recovery for remanufacturing,” Computers & Industrial Engineering, Vol.142, 106339, 2020.
  5. [5] G. Seliger, H.-J. Kim, and T. Keil, “Generation of control sequences for a pilot-disassembly system,” Environmentally Conscious Manufacturing II, Vol.4569, pp. 81-92, 2002.
  6. [6] Y. Laili, F. Tao, D. T. Pham, Y. Wang, and L. Zhang, “Robotic disassembly re-planning using a two-pointer detection strategy and a super-fast bees algorithm,” Robotics and Computer-Integrated Manufacturing, Vol.59, pp. 130-142, 2019.
  7. [7] J. Liu, Z. Zhou, D. T. Pham, W. Xu, C. Ji, and Q. Liu, “Collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem using improved discrete Bees algorithm in remanufacturing,” Robotics and Computer-Integrated Manufacturing, Vol.61, 101829, 2020.
  8. [8] S. Vongbunyong, “Applications of cognitive robotics in disassembly of products,” Ph.D. thesis, The University of New South Wales, 2013.
  9. [9] S. Vongbunyong and W. H. Chen, “Disassembly Automation: Automated Systems with Cognitive Abilities,” Springer Int. Publishing, 2015.
  10. [10] S. Vongbunyong, S. Kara, and M. Pagnucco, “A framework for using cognitive robotics in disassembly automation,” D. Dornfeld and B. Linke (Eds.), “Leveraging technology for a sustainable world,” Springer, pp. 173-178, 2012.
  11. [11] S. Vongbunyong, S. Kara, and M. Pagnucco, “Application of cognitive robotics in disassembly of products,” CIRP Annals, Vol.62, No.1, pp. 31-34, doi: 10.1016/j.cirp.2013.03.037, 2013.
  12. [12] S. Vongbunyong, S. Kara, and M. Pagnucco, “Basic behaviour control of the vision-based cognitive robotic disassembly automation,” Assembly Automation, Vol.33, No.1, pp. 38-56, doi: 10.1108/01445151311294694, 2013.
  13. [13] S. Vongbunyong, S. Kara, and M. Pagnucco, “General plans for removing main components in cognitive robotic disassembly automation,” Proc. of the 6th Int. Conf. on Automation, Robotics and Applications (ICARA), pp. 501-506, doi: 10.1109/ICARA.2015.7081199, 2015.
  14. [14] S. Vongbunyong, S. Kara, and M. Pagnucco, “Learning and revision in cognitive robotics disassembly automation,” Robotics and Computer-Integrated Manufacturing, Vol.34, pp. 79-94, doi: 10.1016/j.rcim.2014.11.003, 2015.
  15. [15] S. Vongbunyong, M. Pagnucco, and S. Kara, “Vision-Based Execution Monitoring of State Transition in Disassembly Automation,” Int. J. Automation Technol., Vol.10, No.5, pp. 708-716, doi: 10.20965/ijat.2016.p0708, 2016.
  16. [16] W. H. Chen, “Towards a generic and robust system for the robotic disassembly of end-of-life electronics,” Ph.D. thesis, The University of New South Wales, 2017.
  17. [17] L. Qiao, Y. Qie, Z. Zhu, Y. Zhu, U. K. uz Zaman, and N. Anwer, “An ontology-based modelling and reasoning framework for assembly sequence planning,” The Int. J. of Advanced Manufacturing Technology, Vol.94, Nos.9-12, pp. 4187-4197, 2018.
  18. [18] Y. Zhong, C. Jiang, Y. Qin, G. Yang, M. Huang, and X. Luo, “Automatically generating assembly sequences with an ontology-based approach,” Assembly Automation, Vol.40, No.2, pp. 319-334, 2019.
  19. [19] D. Šormaz and A. Sarkar, “SIMPM – Upper-level ontology for manufacturing process plan network generation,” Robotics and Computer-Integrated Manufacturing, Vol.55, pp. 183-198, 2019.
  20. [20] M. M. Mabkhot, S. K. Amri, S. Darmoul, A. M. Al-Samhan, and S. Elkosantini, “An ontology-based multi-criteria decision support system to reconfigure manufacturing systems,” IISE Trans., Vol.52, No.1, pp. 18-42, 2020.
  21. [21] L. Mesmer and A. Olewnik, “Enabling supplier discovery through a part-focused manufacturing process ontology,” Int. J. of Computer Integrated Manufacturing, Vol.31, No.1, pp. 87-100, 2018.
  22. [22] L. Xue, P. Wang, H. Cheng, X. Tong, P. Zeng, and H. Yu, “An ontology modeling and application for an assembly line of manufacturing system,” Proc. of the 43rd Annual Conf. of the IEEE Industrial Electronics Society (IECON 2017), pp. 5414-5419, 2017.
  23. [23] P. Chhim, R. B. Chinnam, and N. Sadawi, “Product design and manufacturing process based ontology for manufacturing knowledge reuse,” J. of Intelligent Manufacturing, Vol.30, No.2, pp. 905-916, 2019.
  24. [24] S. An, P. Martinez, R. Ahmad, and M. Al-Hussein, “Ontology-based knowledge modeling for frame assemblies manufacturing,” Proc. of the 36th Int. Symp. on Automation and Robotics in Construction (ISARC), pp. 709-715, 2019.
  25. [25] Y. Zhang, X. Luo, Y. Zhao, and H. Zhang, “An ontology-based knowledge framework for engineering material selection,” Advanced Engineering Informatics, Vol.29, No.4, pp. 985-1000, 2015.
  26. [26] S. Saha, Z. Usman, W. Li, S. Jones, and N. Shah, “Core domain ontology for joining processes to consolidate welding standards,” Robotics and Computer-Integrated Manufacturing, Vol.59, pp. 417-430, 2019.
  27. [27] Y. He, C. Hao, Y. Wang, Y. Li, Y. Wang, L. Huang, and X. Tian, “An ontology-based method of knowledge modelling for remanufacturing process planning,” J. of Cleaner Production, Vol.258, 120952, 2020.
  28. [28] P. Borst and H. Akkermans, “An ontology approach to product disassembly,” E. Plaza and R. Benjamins (Eds.), “Knowledge Acquisition, Modeling and Management,” Springer, pp. 33-48, 1997.
  29. [29] H. Jiang, J. Yi, X. Zhu, and Z. Li, “Generating disassembly tasks for selective disassembly using ontology-based disassembly knowledge representation,” Assembly Automation, Vol.38, No.2, pp. 113-124, doi: 10.1108/aa-04-2016-034, 2018.
  30. [30] H. J. Kim, S. Kernbaum, and G. Seliger, “Emulation-based control of a disassembly system for LCD monitors,” The Int. J. of Advanced Manufacturing Technology, Vol.40, pp. 383-392, doi: 10.1007/s00170-007-1334-z, 2009.
  31. [31] E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, and Y. Katz, “Pellet: A practical OWL-DL reasoner,” J. of Web Semantics, Vol.5, No.2, pp. 51-53, doi: 10.1016/j.websem.2007.03.004, 2007. [Accessed September 8, 2020]
  32. [32] J.-B. Lamy, “Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies,” Artificial intelligence in medicine, Vol.80, pp. 11-28, 2017.
  33. [33] “Protégé.” [Accessed August 10, 2020]
  34. [34] W. H. Chen, G. Foo, S. Kara, and M. Pagnucco, “Application of a multi-head tool for robotic disassembly,” Procedia CIRP, Vol.90, pp. 630-635, doi: 10.1016/j.procir.2020.02.047, 2020.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024