Data-Driven Sizing and Shaping of Topology Optimization Concepts Using Implicit Surfaces, Free Form Deformations and Multifidelity-Based Surrogate Models

Abstract In this work, a framework for data-driven sizing and shaping of topology optimization (TO) concepts is developed, implemented and demonstrated. The density field from a solid isotropic material with penalization (SIMP)-based TO solution is converted to an implicit surface-based geometry (ISG) by using regularized radial basis function networks (RBFN) with Wendland’s compactly supported radial basis functions. Sizing of the ISG is done locally by morphing operations and shaping is performed by applying free form deformations (FFD) on the stl-mesh which is generated from the ISG by a marching cube algorithm. The smooth FFD-based shaping is represented as a RBFN with cubic splines using a set of control points with corresponding prescribed deformations. Data-driven sizing and shaping of TO concepts are then performed by using multifidelity non-linear computer experiments and surrogate model-based design optimization. The developed and implemented framework is demonstrated for the well-known Messerschmitt-Bölkow-Blohm (MBB)-beam as well as an application of a flywheel to a compactor machine.