Optimization of geometric analysis strategy in CAD-systems
- Authors: Bozhko A.N.1, Livantsov V.E.1
-
Affiliations:
- Bauman Moscow State Technical University
- Issue: Vol 16, No 4 (2024)
- Pages: 825-840
- Section: MATHEMATICAL MODELING AND NUMERICAL SIMULATION
- URL: https://bakhtiniada.ru/2076-7633/article/view/306589
- DOI: https://doi.org/10.20537/2076-7633-2024-16-4-825-840
- ID: 306589
Cite item
Full Text
Abstract
Computer-aided assembly planning for complex products is an important engineering and scientific problem. The assembly sequence and content of assembly operations largely depend on the mechanical structure and geometric properties of a product. An overview of geometric modeling methods that are used in modern computer-aided design systems is provided. Modeling geometric obstacles in assembly using collision detection, motion planning, and virtual reality is very computationally intensive. Combinatorial methods provide only weak necessary conditions for geometric reasoning. The important problem of minimizing the number of geometric tests during the synthesis of assembly operations and processes is considered. A formalization of this problem is based on a hypergraph model of the mechanical structure of the product. This model provides a correct mathematical description of coherent and sequential assembly operations. The key concept of the geometric situation is introduced. This is a configuration of product parts that requires analysis for freedom from obstacles and this analysis gives interpretable results. A mathematical description of geometric heredity during the assembly of complex products is proposed. Two axioms of heredity allow us to extend the results of testing one geometric situation to many other situations. The problem of minimizing the number of geometric tests is posed as a non-antagonistic game between decision maker and nature, in which it is required to color the vertices of an ordered set in two colors. The vertices represent geometric situations, and the color is a metaphor for the result of a collision-free test. The decision maker’s move is to select an uncolored vertex; nature’s answer is its color. The game requires you to color an ordered set in a minimum number of moves by decision maker. The project situation in which the decision maker makes a decision under risk conditions is discussed. A method for calculating the probabilities of coloring the vertices of an ordered set is proposed. The basic pure strategies of rational behavior in this game are described. An original synthetic criterion for making rational decisions under risk conditions has been developed. Two heuristics are proposed that can be used to color ordered sets of high cardinality and complex structure.
About the authors
Arkadiy N.. Bozhko
Bauman Moscow State Technical University
Author for correspondence.
Email: bozh12@yandex.ru
Viktor E.. Livantsov
Bauman Moscow State Technical University
Email: viktor.livantsov@yandex.ru
References
- А. Н. Божко, “Анализ геометрической разрешимости при сборке сложных изделий как задача принятия решений”, Математика и математическое моделирование, 2018, № 5, 17–34 [A. N. Bozhko, “Analysis of geometric reasoning in the assembly of complex products as a decision-making task”, Matematika i matematicheskoe modelirovanie, 2018, no. 5, 17–34 (in Russian)].
- А. Н. Божко, “Игровое моделирование геометрического доступа”, Наука и образование. МГТУ им. Н. Э. Баумана. Электрон. журн., 2009, № 12 https://cyberleninka.ru/article/n/igrovoe-modelirovanie-geometricheskogo-dostupa/viewer [A. N. Bozhko, “Game-theoretic modeling of geometric reasoning”, Nauka i obrazovanie. MGTU im. N. E. Baumana. Elektron. Journal, 2009, no. 12 https://cyberleninka.ru/article/n/igrovoe-modelirovanie-geometricheskogo-dostupa/viewer
- (in Russian)].
- А. Н. Божко, С. В. Родионов, “Комбинаторные методы геометрической разрешимости в автоматизированных системах проектирования. Обзор”, Информационные технологии, 28:3 (2022), 115–125 [A. Bozhko, S. Rodionov, “Combinatorial methods of geometric reasoning in computer-aided design systems. Review”, Informacionnye tekhnologii, 28:3 (2022), 115–125 (in Russian)].
- А. С. Рыков, Системный анализ: модели и методы принятия решений и поисковой оптимизации, Издательский дом МИСиС, М., 2009, 608 с. [A. S. Rykov, System analysis: models and methods of decision-making and search engine optimization, Izdatel’skij dom MISiS, Moscow, 2009, 608 pp. (in Russian)].
- A. N. Bozhko, “Hypergraph model for assembly sequence problem”, IOP Conference Series: Materials Science and Engineering, 560:1 (2019), 012010, IOP Publishing.
- A. N. Bozhko, “Mathematical modelling of mechanical structures and assembly processes of complex technical systems”, International Russian Automation Conference, 2021, 80–91.
- T.-H. Eng, Z.-K. Ling, W. Olson, Ch. Mclean, “Feature-based assembly modeling and sequence generation”, Computers & Industrial Engineering, 36:1 (1999), 17–33.
- S. Ghandi, E. Masehian, “Review and taxonomies of assembly and disassembly path planning problems and approaches”, Computer-Aided Design, 67–68 (2015), 58–86.
- D. González, et al., “A review of motion planning techniques for automated vehicles”, IEEE Transactions on Intelligent Transportation Systems, 17:4 (2015), 1135–1145.
- D. Halperin, J.-C. Latombe, R. H. A. Wilson, “General framework for assembly planning: the motion space approach”, Algorithmica, 26:3–4 (2000), 577–601.
- R. Hoffman, “A common sense approach to assembly sequence planning”, Computer-Aided Mechanical Assembly Planning, 148 (1991), 289–313.
- P. Jiménez, F. Thomas, C. Torras, “3D collision detection: a survey”, Computers & Graphics, 25:2 (2001), 269–285.
- S. Krishnan, A. Sanderson, “Reasoning about geometric constraints for assembly sequence planning”, Robotics and Automation, 1991. Proceedings, v. 1, 1991, 776–782.
- J. Miller, R. Hofman, “Automatic assembly planning with fasteners”, Robotics and Automation. Proceedings. 1989 IEEE International Conference, v. 1, 1989, 69–74.
- Ch. Pan, Sh. Smith, G. Smith, “Determining interference between parts in CAD STEP files for automatic assembly planning”, Journal of Computing and Information Science in Engineering, 5:1 (2005), 56–62.
- S. Roman, Lattices and ordered sets, Springer, 2008, 307 pp.
- B. Romney, C. Godard, M. Goldwasser, G. Ramkumar, “An efficient system for geometric assembly sequence generation and evaluation”, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 1995, 699–712.
- M. Schenk, S. Straßburger, H. Kißner, “Combining virtual reality and assembly simulation for production planning and worker qualification”, Proc. of International Conference on Changeable, Agile, Reconfigurable and Virtual Production, 2005.
- A. Seth, J. M. Vance, J. H. Oliver, “Virtual reality for assembly methods prototyping: a review”, Virtual Reality, 15:1 (2011), 5–20.
- H. Srinivasan, R. Gadh, “A non-interfering selective disassembly sequence for components with geometric constraints”, IIE Transactions, 34:4 (2002), 349–361.
- Q. Su, “A hierarchical approach on assembly sequence planning and optimal sequences analyzing”, Robotics and Computer-Integrated Manufacturing, 25:1 (2009), 224–234.
- W. Weiwei, H. Kensuke, N. Kazuyuki, “Assembly sequence planning for motion planning”, Assembly Automation, 38:2 (2018), 195–206.
- R. Wilson, On geometric assembly planning, PhD thesis, Stanford Univ., Dept. Comput. Sci, Stanford, 1992, 156 pp.
- R. Wilson, J.-C. Latombe, “Geometric reasoning about mechanical assembly”, Artificial Intelligence, 71:2 (1994), 371–396.
- T. Woo, D. Dutta, “Automatic disassembly and total ordering in three dimension”, Journal of Engineering for Industry, 113:2 (1991), 207–213.
- Z. P. Yin, H. Ding, Y. L. Xiong, “A virtual prototyping approach to generation and evaluation of mechanical assembly sequences”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 218:1 (2004), 87–102.
Supplementary files
