Abstract
Manufacturers of special-purpose machines are faced with the challenge of having to efficiently engineer systems tailored to individual customer requirements, yet sharing the same base technology. One popular approach is that of “product line engineering” (PLE), the general idea being to maintain product lines, i.e., families of similar systems with variations in features, and to reuse components and assemblies wherever possible. We propose a live product line approach wherein variant information for components and assemblies is dynamically created and alternatives are proposed to the designer in the Computer Aided Design (CAD) system. To this end, we apply unsupervised learning techniques, in particular density-based clustering, to grouping CAD models of machine components. We demonstrate the feasibility using a data set from a special-purpose machine manufacturer and show that the approach can provide benefits in machine design.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. ACM SIGMOD Rec. 28(2), 49–60 (1999)
Bickel, S., Sauer, C., Schleich, B., Wartzack, S.: Comparing CAD part models for geometrical similarity: a concept using machine learning algorithms. Procedia CIRP 96, 133–138 (2021)
Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 160–172. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37456-2_14
Cicconi, P., Raffaeli, R., Germani, M.: An approach to support model based definition by PMI annotations. Comput. Aided Des. Appl. 14(4), 526–534 (2017)
Deshmukh, A.S., et al.: Content-based assembly search: a step towards assembly reuse. Comput. Aided Des. 40(2), 244–261 (2008)
Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: kdd, vol. 96, no. 34, pp. 226–231 (1996)
Feldmann, S., Legat, C., Vogel-Heuser, B.: Engineering support in the machine manufacturing domain through interdisciplinary product lines: an applicability analysis. IFAC-PapersOnLine 48(3), 211–218 (2015)
Fischer, S., Linsbauer, L., Lopez-Herrejon, R.E., Egyed, A.: Enhancing clone-and-own with systematic reuse for developing software variants. In: 2014 IEEE International Conference on Software Maintenance and Evolution, pp. 391–400. IEEE (2014)
Hahsler, M., Piekenbrock, M., Doran, D.: dbscan: Fast density-based clustering with R. J. Stat. Softw. 91, 1–30 (2019)
Hong, T., Lee, K., Kim, S.: Similarity comparison of mechanical parts to reuse existing designs. Comput. Aided Des. 38(9), 973–984 (2006)
Iyer, N., Jayanti, S., Lou, K., Kalyanaraman, Y., Ramani, K.: Three-dimensional shape searching: state-of-the-art review and future trends. Comput. Aided Des. 37(5), 509–530 (2005)
Leitner, A., Preschern, C., Kreiner, C.: Effective development of automation systems through domain-specific modeling in a small enterprise context. Softw. Syst. Model. 13, 35–54 (2014)
Liu, H., et al.: 3D model similarity evaluation for mechanical design reuse based on spatial correlated shape-word clique. Multimedia Tools Appl. 79, 8181–8195 (2020)
Lupinetti, K., Giannini, F., Monti, M., Pernot, J.P.: Content-based multi-criteria similarity assessment of CAD assembly models. Comput. Ind. 112, 103111 (2019)
Machalica, D., Matyjewski, M.: CAD models clustering with machine learning. Arch. Mech. Eng. 66(2) (2019)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Sander, J., et al.: Automatic extraction of clusters from hierarchical clustering representations. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 75–87 (2003)
Schubert, E., et al.: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. 42(3), 1–21 (2017)
Wang, J., Wang, H.: A study of 3D model similarity based on surface bipartite graph matching. Eng. Comput. 34(1), 174–188 (2017)
Zehtaban, L., Elazhary, O., Roller, D.: A framework for similarity recognition of CAD models. J. Comput. Des. Eng. 3(3), 274–285 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Funk, S., Legat, C., Busboom, A. (2024). Live Product Line Engineering Using Density-Based Clustering of CAD Models. In: Fujita, H., Cimler, R., Hernandez-Matamoros, A., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2024. Lecture Notes in Computer Science(), vol 14748. Springer, Singapore. https://doi.org/10.1007/978-981-97-4677-4_18
Download citation
DOI: https://doi.org/10.1007/978-981-97-4677-4_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-4676-7
Online ISBN: 978-981-97-4677-4
eBook Packages: Computer ScienceComputer Science (R0)