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Simulated annealing-based fitting of CAD models to point clouds of mechanical parts’ assemblies

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Abstract

This paper introduces a new fitting approach to allow an efficient part-by-part reconstruction or update of editable CAD models fitting the point cloud of a digitized mechanical parts\('\) assembly. The idea is to make use of parameterized CAD models whose dimensional parameters are to be optimized to match the acquired point cloud. Parameters may also be related to assembly constraints, e.g. the distance between two parts. The optimization kernel relies on a simulated annealing algorithm to find out the best values of the parameters so as to minimize the deviations between the point cloud and the CAD models to be fitted. Both global and local fitting are possible. During the optimization process, the orientation and positioning of the CAD parts are driven by an ICP algorithm. The modifications are ensured by the batch calls to a CAD modeler which updates the models as the fitting process goes on. The modeler also handles the assembly constraints. Both single and multiple parts can be fitted, either sequentially or simultaneously. The evaluation of the proposed approach is performed using both real scanned point clouds and as-scanned virtually generated point clouds which incorporate several artifacts that could appear with a real scanner. Results cover several Industry 4.0 related application scenarios, ranging from the global fitting of a single part to the update of a complete Digital Mock-Up embedding assembly constraints. The proposed approach demonstrates good capacities to help maintaining the coherence between a product/system and its digital twin.

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References

  1. Lu Y (2017) Industry 4.0: a survey on technologies, applications and open research issues. J Ind Inf Integr 6:1–10

    Google Scholar 

  2. Bagci E (2009) Reverse engineering applications for recovery of broken or worn parts and re-manufacturing. Adv Eng Softw 40(6):407–418

    Article  Google Scholar 

  3. Falcidieno B, Giannini F, Léon J-C, Pernot J-P (2014) Processing free form objects within a product development process framework. In: Michopoulos JG, Paredis CJJ, Rosen DW, and Vance JM (eds) Advances in Computers and Information in Engineering Research, Vol 1. in ASME-Press, pp 317–344

    Google Scholar 

  4. Berger M, Tagliasacchi A, Seversky LM, Alliez P, Guennebaud G, Levine JA, Sharf A, Silva CT (2016) A survey of surface reconstruction from point clouds. Comput Gr Forum 36(1):301–329

    Article  Google Scholar 

  5. Fischler MA, Bolles RC (1981) Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  6. Schnabel R, Wahl R, Klein R (2007) Efficient ransac for point-cloud shape detection. Comput Gr Forum 26(2):214–226

    Article  Google Scholar 

  7. Schnabel R, Degener P, Klein R (2009) Completion and reconstruction with primitive shapes. Comput Gr Forum 28(2):503–512

    Article  Google Scholar 

  8. Bey A, Chaine R, Marc R, Thibault G (2012) Effective shapes generation for bayesian cad model reconstruction. In: Proceedings of the 5th eurographics conference on 3D object retrieval, pp 63–66

  9. Lari Z, Habib A (2014) An adaptive approach for the segmentation and extraction of planar and linear/cylindrical features from laser scanning data. ISPRS J Photogramm Remote Sens 93:192–212

    Article  Google Scholar 

  10. Attene M, Falcidieno B, Spagnuolo M (2006) Hierarchical mesh segmentation based on fitting primitives. Vis Comput 22(3):181–193

    Article  Google Scholar 

  11. Mitra N, Wand M, Zhang HR, Cohen-Or D, Kim V, Huang Q-X (2013) Structure-aware shape processing, In: SIGGRAPH Asia 2013 courses, pp 1:1–1:20

  12. Li Y, Wu X, Chrysathou Y, Sharf A, Cohen-Or D, Mitra NJ (2011) Globfit: consistently fitting primitives by discovering global relations. ACM Trans Gr 30(4):52:1–52:12

    Google Scholar 

  13. Monszpart A, Mellado N, Brostow GJ, Mitra NJ (2015) Rapter: rebuilding man-made scenes with regular arrangements of planes. ACM Trans Gr 34(4):103:1–103:12

    Article  Google Scholar 

  14. Nan L, Xie K, Sharf A (2012) A search-classify approach for cluttered indoor scene understanding. ACM Trans Gr 31(6):137:1–137:10

    Article  Google Scholar 

  15. Ip CY, Gupta SK (2007) Retrieving matching cad models by using partial 3d point clouds. Comput Aid Des Appl 4(5):629–638

    Article  Google Scholar 

  16. Gelfand N, Mitra NJ, Guibas LJ, Pottmann H (2005) Robust global registration. Symp Geom Process 2(3):197–206

    Google Scholar 

  17. Rabbani T, Van Den Heuvel F (2004) Methods for fitting csg models to point clouds and their comparison. In: Proceedings of the 7th IASTED international conference on computer graphics and imaging, Kauai, HI, USA 1719, pp 279–284

  18. Buonamici F, Carfagni M, Furferi R, Governi L, Lapini A, Volpe Y (2018) Reverse engineering of mechanical parts: a template-based approach. J Comput Des Eng 5(2):145–159

    Google Scholar 

  19. Wang J, Gu D, Yu Z, Tan C, Zhou L (2012) A framework for 3d model reconstruction in reverse engineering. Comput Ind Eng 63(4):1189–1200

    Article  Google Scholar 

  20. Stark R, Grosser H, Müller P (2013) Product analysis automation for digital mro based on intelligent 3d data acquisition. CIRP Ann Manuf Technol 62(1):123–126

    Article  Google Scholar 

  21. Bénière R, Subsol G, Gesquière G, Le Breton F, Puech W (2013) A comprehensive process of reverse engineering from 3d meshes to cad models. Comput Aid Des 45(11):1382–1393

    Article  Google Scholar 

  22. Xu M, Li M, Xu W, Deng Z, Yang Y, Zhou K (2016) Interactive mechanism modeling from multi-view images. ACM Trans Gr 35(6):236:1–236:13

    Google Scholar 

  23. Montlahuc J, Shah GA, Polette A, Pernot J-P (2019) As-scanned point clouds generation for virtual reverse engineering of cad assembly models. Comput Aid Des Appl 16(6):1171–1182

    Article  Google Scholar 

  24. Lupinetti K, Pernot J-P, Monti M, Giannini F (2019) Content-based cad assembly model retrieval: survey and future challenges. Comput Aid Des 113:62–81

    Article  Google Scholar 

  25. Gouaty G, Fang L, Michelucci D, Daniel M, Pernot J-P, Raffin R, Lanquetin S, Neveu M (2016) Variational geometric modeling with black box constraints and dags. Comput Aid Des 75:1–12

    Article  Google Scholar 

  26. Pernot J-P, Michelucci D, Daniel M, Foufou S (2019) Towards a better integration of modelers and black box constraint solvers within the product design process. Ann Math Artif Intell 85(2):147–173

    Article  MathSciNet  Google Scholar 

  27. Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256

    Article  Google Scholar 

  28. Katz S, Tal A, Basri R (2007) Direct visibility of point sets. ACM Trans Gr 26(3):24:1–24:11

    Article  Google Scholar 

  29. Ben-Ameur W (2004) Computing the initial temperature of simulated annealing. Comput Optim Appl 29:369–385

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Arts et Métiers Institute of Technology, LISPEN, HESAM Université, F-13617 Aix-en-Provence, France

    Ghazanfar Ali Shah, Arnaud Polette & Jean-Philippe Pernot

  2. Istituto di Matematica Applicata e Tecnologie Informatiche “Enrico Magenes”, CNR Via De Marini 6, 16149, Genoa, Italy

    Ghazanfar Ali Shah, Franca Giannini & Marina Monti

  3. DIME-Dipartimento di Ingegneria meccanica, energetica, gestionale e dei trasporti, Università degli Studi di Genova, Genoa, Italy

    Ghazanfar Ali Shah

Authors
  1. Ghazanfar Ali Shah
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  2. Arnaud Polette
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  3. Jean-Philippe Pernot
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  4. Franca Giannini
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  5. Marina Monti
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Correspondence to Ghazanfar Ali Shah.

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Shah, G.A., Polette, A., Pernot, JP. et al. Simulated annealing-based fitting of CAD models to point clouds of mechanical parts’ assemblies. Engineering with Computers 37, 2891–2909 (2021). https://doi.org/10.1007/s00366-020-00970-8

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  • DOI: https://doi.org/10.1007/s00366-020-00970-8

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