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Image-based truss recognition for density-based topology optimization approach

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Abstract

Topology optimization is a tool that supports the creativity of structural-designers and is used in various industries, from automotive to aeronautics, to reduce design iterations towards obtaining the optimal structure layout. However, this tool requires both time and experience to interpret the results into manufacturable and reliable structure layout. To improve this aspect, an interpretation support tool is being developed by our research team based on industrial knowledge and axiomatic design principles. This design tool will be very useful for aircraft structure development, for instance, as it aims to help the structural designer in the conception of stiffened panels. The tool has been divided into three modules: feature recognition, feature analysis and design support. This paper presents the first of the three modules that identifies the trusses of the optimized topology in terms of truss recognition algorithms. The purpose of the truss recognition algorithm is to translate the densities of the element of the optimized topology (low-level abstraction) to a skeletal structure (high-level abstraction) that contains nodes and branches that describe the same topology as the optimized topology. It should also ensure that the structural skeleton retains connectivity with loads and boundary conditions. The information may then be used by the design support tool for analysis, comparison, decision-making, design and optimization purposes. To do so, a novel image-based method using a binary skeleton is proposed. For this work, we identified multiple limitations existing in similar solutions and we mitigated them. Therefore, a new skeletonization method is proposed, which is specifically designed for truss recognition in the optimized topology. The capabilities of the skeletonization method are demonstrated by comparing it with existing methods, and the truss recognition algorithm is used with a test case exhibiting the algorithm’s capabilities on an airplane wing box rib.

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Acknowledgements

The authors wish to thank Stelia Amérique du Nord and Bombardier as industrial collaborators in this project. We would also like to thank the CARIC and MITACS for their funding of our project: MDO-1601: MUltidisciplinary Framework for Optimization of wingboX – 1a [IT07469].

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Authors and Affiliations

  1. Department of Mechanical Engineering, Machine Design Section, Polytechnique Montreal, Montreal, QC, Canada

    Jean-François Gamache, Aurelian Vadean, Dominique Beaini & Sofiane Achiche

  2. Research and Technology, Stelia Amérique du Nord, Mirabel, QC, Canada

    Émeric Noirot-Nérin

Authors
  1. Jean-François Gamache
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  2. Aurelian Vadean
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  3. Émeric Noirot-Nérin
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  4. Dominique Beaini
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  5. Sofiane Achiche
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Correspondence to Jean-François Gamache.

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Gamache, JF., Vadean, A., Noirot-Nérin, É. et al. Image-based truss recognition for density-based topology optimization approach. Struct Multidisc Optim 58, 2697–2709 (2018). https://doi.org/10.1007/s00158-018-2028-x

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  • DOI: https://doi.org/10.1007/s00158-018-2028-x

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