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Shape optimisation of cold-formed steel columns with manufacturing constraints using the Hough transform

Journal Article


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Abstract


  • This paper introduces manufacturing constraints into a recently developed evolutionary algorithm for shape optimisation of CFS profiles. The algorithm is referred to as "self-shape optimisation" and uses Genetic Algorithm (GA) together with the Augmented Lagrangian (AL) method to avoid ill-conditioned problems. Simple manufacturing rules derived from the limitations of current cold-forming processes, i.e. a limited ability to form continuously curved surfaces without discrete bends, are described in the paper and incorporated into the algorithm. The Hough transform is used to detect straight lines and transform arbitrarily drawn cross-sections into manufacturable ones. Firstly, the algorithm is verified against a known optimisation problem and found to accurately converge to a manufacturable optimum solution. Secondly, the algorithm is applied to singly-symmetric CFS columns each of which is subject to an axial compressive load of 75kN and has a uniform wall thickness of 1.2 mm. The strength of the columns is evaluated by the Direct Strength Method (DSM) and all buckling modes are considered. Various column lengths (from 500 mm to 3000 mm) and numbers of roll-forming bends were investigated. The optimised cross-sections are presented and discussed.

Authors


  •   Wang, Bin (external author)
  •   Gilbert, Benoit P. (external author)
  •   Molinier, Adrien M. (external author)
  •   Guan, Hong (external author)
  •   Teh, Lip H.

Publication Date


  • 2016

Citation


  • Wang, B., Gilbert, B. P., Molinier, A. M., Guan, H. & Teh, L. H. (2016). Shape optimisation of cold-formed steel columns with manufacturing constraints using the Hough transform. Thin-Walled Structures, 106 75-92.

Scopus Eid


  • 2-s2.0-84965099445

Ro Full-text Url


  • http://ro.uow.edu.au/context/eispapers/article/6797/type/native/viewcontent

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5769

Has Global Citation Frequency


Number Of Pages


  • 17

Start Page


  • 75

End Page


  • 92

Volume


  • 106

Place Of Publication


  • United Kingdom

Abstract


  • This paper introduces manufacturing constraints into a recently developed evolutionary algorithm for shape optimisation of CFS profiles. The algorithm is referred to as "self-shape optimisation" and uses Genetic Algorithm (GA) together with the Augmented Lagrangian (AL) method to avoid ill-conditioned problems. Simple manufacturing rules derived from the limitations of current cold-forming processes, i.e. a limited ability to form continuously curved surfaces without discrete bends, are described in the paper and incorporated into the algorithm. The Hough transform is used to detect straight lines and transform arbitrarily drawn cross-sections into manufacturable ones. Firstly, the algorithm is verified against a known optimisation problem and found to accurately converge to a manufacturable optimum solution. Secondly, the algorithm is applied to singly-symmetric CFS columns each of which is subject to an axial compressive load of 75kN and has a uniform wall thickness of 1.2 mm. The strength of the columns is evaluated by the Direct Strength Method (DSM) and all buckling modes are considered. Various column lengths (from 500 mm to 3000 mm) and numbers of roll-forming bends were investigated. The optimised cross-sections are presented and discussed.

Authors


  •   Wang, Bin (external author)
  •   Gilbert, Benoit P. (external author)
  •   Molinier, Adrien M. (external author)
  •   Guan, Hong (external author)
  •   Teh, Lip H.

Publication Date


  • 2016

Citation


  • Wang, B., Gilbert, B. P., Molinier, A. M., Guan, H. & Teh, L. H. (2016). Shape optimisation of cold-formed steel columns with manufacturing constraints using the Hough transform. Thin-Walled Structures, 106 75-92.

Scopus Eid


  • 2-s2.0-84965099445

Ro Full-text Url


  • http://ro.uow.edu.au/context/eispapers/article/6797/type/native/viewcontent

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5769

Has Global Citation Frequency


Number Of Pages


  • 17

Start Page


  • 75

End Page


  • 92

Volume


  • 106

Place Of Publication


  • United Kingdom