A multi-case-based assembly management method for the shipbuilding industry

• Autorzy: Iwańkowicz Remigiusz

Identyfikatory

DOI

10.2478/pomr-2021-0018

• Języki publikacji: EN

Abstrakty

EN

This article describes a method for planning the assembly of ship hulls that focuses on a welding sequence, takes into account subassembly processes and makes use of a previously built database of structures. Different degrees of similarity between structures are taken into account. The described research led to the development of an intelligent hybrid sequencing method for structure assembly that uses fuzzy clustering, case-based reasoning and evolutionary optimization. The method is called ‘Multi-case-Based Assembly Planning (MBAP)’. The method is developed to provide satisfactory solutions with low user effort. The analyses carried out show that the calculations are highly timeefficient. The developed evolutionary algorithm converges on sub-optimal solutions. The MBAP method can be directly implemented by any shipbuilder that assembles hulls. Apart from this, fuzzy clustering integrated with case-based reasoning can be applied in practice. The integration of fuzzy clustering and case-based reasoning has been taken to a level higher than previously described in the literature.

Słowa kluczowe

EN

assembly management; shipbuilding; case-based reasoning; fuzzy classification

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2021
• Tom: nr 2
• Strony: 27--35
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Iwańkowicz Remigiusz

• Maritime University of Szczecin, ul. Wały Chrobrego 1-2, 70-500 Szczecin, Poland

Bibliografia

• 1. M. Suszyński, M. Rogalewicz, ‘Selection of an Industrial Robot for Assembly Jobs Using Multi-Criteria Decision Making Methods’, Management and Production Engineering Review. 2020, doi: 10.24425/mper.2020.
• 2. R. Iwańkowicz and R. Rutkowski, ‘Assembly Management in the Shipyard Using a Welding Database’, in: Application of Computational Methods in Engineering Science. Wydawnictwo Politechniki Lubelskiej, Lublin 2019.
• 3. M. Deja, M. S. Siemiatkowski, and D. Zielinski, ‘Multicriteria comparative analysis of the use of subtractive and additive technologies in the manufacturing of offshore machinery components’, Polish Maritime Research. 2020, doi: 10.2478/pomr-2020-0048.
• 4. X. Li, Z. Zhu, Y. Li, and Z. Hu, ‘Design and Mechanical Analysis of a Composite T-Type Connection Structure for Marine Structures’, Polish Maritime Research. 2020, doi: 10.2478/pomr-2020-0036.
• 5. R. Iwańkowicz, ‘An efficient evolutionary method of assembly sequence planning for shipbuilding industry’, Assembly Automation. 2016, doi: 10.1108/AA-02-2015-013.
• 6. M. Kang, J. Seo, and H. Chung, ‘Ship block assembly sequence planning considering productivity and welding deformation’, International Journal of Naval Architecture and Ocean Engineering. 2017, doi: 10.1016/j. ijnaoe.2017.09.005.
• 7. D. Kolich, R. L. Storch, and N. Fafandjel, ‘Lean built-up panel assembly in a newbuilding shipyard’, Journal of Ship Production and Design. 2018, doi: 10.5957/JSPD.170007.
• 8. A. Oliveira, and J. M. Gordo, ‘Lean tools applied to a shipbuilding panel line assembling process’, Brodogradnja: Teorija i praksa brodogradnje i pomorske tehnike. 2018, doi: 10.21278/brod69404.
• 9. F. Bonneville, C. Perrard, and J. M. Henrioud, ‘A genetic algorithm to generate and evaluate assembly plans’, IEEE Symposium on Emerging Technology and Factory Automation. 1995, doi: 10.1109/ETFA.1995.496663.
• 10. S.F. Chen and Y.J. Liu, ‘An adaptive genetic assembly-sequence planner’, International Journal of Computer Integrated Manufacturing. 2001, doi: 10.1080/09511920110034987.
• 11. R. Schank, Dynamic memory: A theory of reminding and learning in computers and people. Cambridge University Press, 1982.
• 12. J. Kolodner, ‘Reconstructive memory, a computer model’. Cognitive Science. 1983, doi: 10.1016/ S0364-0213(83)80002-0.
• 13. A. Aamodt and E. Plaza, ‘Case-based reasoning: Foundational issues, methodological variations, and aystem approaches’, AI Communications. IOS Press. 1994, doi: 10.3233/AIC-1994-7104.
• 14. Q. Shipeng, J. Zuhua, and T. Ningrong, ‘An integrated method for block assembly sequence planning in shipbuilding’, The International Journal of Advanced Manufacturing Technology. 2013, doi: 10.1007/s00170-013-5087-6.
• 15. K.K. Cho, S.H. Lee, and D.S. Chung, ‘An automatic processplanning system for block assembly in shipbuilding’, Annals of the CIRP. 1996, doi: 10.1016/S0007-8506(07)63013-3.
• 16. K.K. Cho, J.G. Sun, and J.S. Oh, ‘An automated welding operation planning system for block assembly in shipbuilding’, International Journal of Production Economics. 1999, doi: 10.1016/S0925-5273(98)00151-0.
• 17. S. Qu, Z. Jiang, and N. Tao, ‘An integrated method for block assembly sequence planning in shipbuilding’, The International Journal of Advanced Manufacturing Technology. 2013, doi: 10.1007/s00170-013-5087-6.
• 18. Y. Seo, D. Sheen, and T. Kim, ‘Block assembly planning in shipbuilding using case-based reasoning’, Expert Systems with Applications. 2007, doi: 10.1016/j.eswa.2005.11.013
• 19. J.C. Bezdek, ‘Pattern recognition with fuzzy objective function algorithms’, Plenum Press, New York. 1981, doi:10.1007/978-1-4757-0450-1.
• 20. M.J. Khan and C. Khan, ‘Performance evaluation of fuzzy clustered case-based reasoning’, Journal of Experimental & Theoretical Artificial Intelligence. 2020, doi: 10.1080/0952813X.2020.1744194.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).