• Medientyp: E-Artikel
  • Titel: Generative design of space frames for additive manufacturing technology
  • Beteiligte: Watson, Marcus; Leary, Martin; Downing, David; Brandt, Milan
  • Erschienen: Springer Science and Business Media LLC, 2023
  • Erschienen in: The International Journal of Advanced Manufacturing Technology
  • Sprache: Englisch
  • DOI: 10.1007/s00170-023-11691-9
  • ISSN: 0268-3768; 1433-3015
  • Schlagwörter: Industrial and Manufacturing Engineering ; Computer Science Applications ; Mechanical Engineering ; Software ; Control and Systems Engineering
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  • Beschreibung: <jats:title>Abstract</jats:title><jats:p>A generative design methodology is presented that solves for minimum volume and compliance space-frame systems, with consideration of stress and buckling constraints. The solution space is explored using formal topology optimisation routines. A parameterisation method converts voxelised topology optimisation solutions into skeletonised connectivity representations. An inequality constrained gradient descent optimisation method optimises and defines cross-sectional geometry. This enables fast and automatic solution generation, providing designers with sets of high-performing problem solutions. Skeleton representations provide an inexpensive modelling tool for parallel analysis of physical problems difficult to model using topology optimisation. Geometry is represented using traditional engineering cross-sections with well understood behaviour. This improves certainty in the performance of solutions, simplifying certification processes. The generative design of a structural aerospace bracket for additive manufacture has been used as a case study within this research. A design of experiments produced 360 topology optimisation results, altering input variables and discretisation resolution to identify their effect on solution outcomes and the performance of parameterisation. The proposed method was found to robustly generate high-performing solutions utilising low-resolution topology optimisation. Additionally, 6 high-performing topologies were identified, providing designers with a set of solutions to select from. Limitations on the parameterisation process were identified, with topology optimisation solutions with volume fractions above 0.2 not parameterising successfully, and simulations with a resolution of 5 mm element size and below generating low performing skeletonised topologies.</jats:p>