Comparing CAD part models for geometrical similarity: A concept using machine learning algorithms

Bickel S, Sauer C, Schleich B, Wartzack S (2020)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Pages Range: 133-138

URI: https://www.sciencedirect.com/science/article/pii/S2212827121000895

DOI: 10.1016/j.procir.2021.01.065

Abstract

The efficient execution of process planning activities requires the knowledge from
several distinct domains. However, in this context, a common issue is the existence of
collective knowledge in the one domain with a lack of networked expertise with other
domains. Motivated by this, the paper proposes an approach to support process
planning by comparing the generated part design with older, validated products. The
utilization of this earlier CAD part models has the potential to reduce development
costs, shorten the production start-up time and improve the product quality. The new
concept provides the manufacturing personnel with a method for comparing the newly
designed part with a pool of validated models to identify the most similar one. Each
previous CAD part model is linked with the necessary manufacturing information, so
that the initial values for the manufacturing process are available without a time
consuming testing phase. The method is divided in three main steps: the global
similarity comparison, the segmentation of the part and the local similarity comparison.
In the first step, the geometry is projected onto a sphere and then transformed to a
matrix. Afterwards these matrices are compared and clustered into corresponding
groups. In the following step, a Machine Learning algorithm segments the objects into
specific, manufacturing relevant groups. In every cluster, the segmented geometries
are again compared for similarity. The combination of the first and the second ranking
results in a global similarity hierarchy for the newly designed part. In this paper, the
entire procedure is shown with the example of sheet-bulk metal formed parts. This new
manufacturing process particularly benefits from this method, as the amount of data is
still limited and therefore little expert knowledge exists.

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How to cite

APA:

Bickel, S., Sauer, C., Schleich, B., & Wartzack, S. (2020). Comparing CAD part models for geometrical similarity: A concept using machine learning algorithms. In Procedia CIRP (Eds.), Proceedings of the CIRPe 2020 - 8th CIRP Global Web Conference - (pp. 133-138).

MLA:

Bickel, Sebastian, et al. "Comparing CAD part models for geometrical similarity: A concept using machine learning algorithms." Proceedings of the CIRPe 2020 - 8th CIRP Global Web Conference - Ed. Procedia CIRP, 2020. 133-138.

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