Improving the manufacturability of highly materially restricted topology-optimized designs with Mixed Integer Linear Programming

ENGINEERING STRUCTURES(2023)

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摘要
This paper presents a new free-form topology optimization framework for highly materially restricted design situations. Highly materially restricted design situations are herein defined as being limited significantly by the combined demands on material use and manufacturability. The new framework uses the ground structure approach with frame elements and casts the design problem as a Mixed Integer Linear Program (MILP), ensuring a solution with a manufacturable discrete material distribution. This paper also presents an extension to hybrid mesh ground structures containing both frame and thin solid elements. In both cases, the user can easily tailor the resultant design to discrete fabrication requirements as e.g. relevant when using an extrusion -based Additive Manufacturing (AM) process that requires the design features to adhere to a discrete number of bead depositions. The new framework is numerically demonstrated on topology optimization benchmark examples. In addition, numerical and experimental comparisons are made to the conventional density-based topology optimization approach (with continuous density variables). When the design is highly restricted, the new MILP framework is in most tested cases found to generate solutions with improved numerical predictions on the compliance. For the experimentally validated case study, the numerical prediction is seen to significantly underestimate the experimentally observed performance gain. The experimental investigation additionally cements the high performance of density-based topology optimization in design situations with low levels of design restrictions. In these cases (6 out of 7 tested herein), the solutions generated with density-based topology optimization have comparable or preferable performance.
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关键词
Topology optimization,Manufacturability,Additive Manufacturing,Frame structures,Hybrid mesh,Mixed Integer Linear Programming
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