Topology Optimisation (TO) is a process that is becoming increasingly reliable and necessary in the pursuit of highly efficient components comprising of low mass with a high structural performance. The ability to maximise a component’s structural characteristics has yielded many variations of computational topological solvers over the years. Over time many different methodologies have been used to generate suitable manufacturable solutions. Despite this, a gap between the generation of TO solutions and the creation of ready-to-manufacture solutions still exists today. The ability to refine and represent a TO solution as a fully manufacturable design is a procedure known as Post-Processing (PP). PP of TO results (e.g. from variable density to manufacturable structures) does however remain a heavily heuristic process where the end-results (and consequently the “efficiency” of the optimised product) can vary significantly as a function of the individual designer/engineer. This “variation” makes the use of TO prohibitive in certain instances. In order to address this issue, this thesis presents a systematic and repeatable approach to parameterised PP of TO results. This method, developed into a software tool, considers the refinement of a TO solution specific to a series of user-defined geometrical features. A stencil method is utilised, which scans the TO solution to extract geometrical features against users’ design requirements. In addition to presenting the methodology, this thesis also investigates different parameter variations; such as geometry update sequence, search radii, stencil shape and type and their influence on the generated post-processed result. Definition of algorithm parameters is provided, together with suggested user-defined settings to enable the derivation of consistent refinements of TO results. The code is applied to more “industry-standard” models to identify its practicality as a usable methodology to refine optimisation results files, with focus taken to improving the designs of 2D TO solutions and further work focussing on refining more complex components.
|Date of Award||Jul 2020|
|Supervisor||Jesper Christensen (Supervisor), Christophe Bastien (Supervisor), Stratis Kanarachos (Supervisor) & Alexis Wilson (Supervisor)|