Mechanostat-type effective density correction for Carter-Hayes growth applied to topology optimization and its efficient interpolation for a target strain energy and volume fraction

Abstract

The need for optimized structures with good mechanical performance for the minimum weight is common in industry. Solid Isotropic Material with Penalization (SIMP) is a Topology Optimization (TO) method offering a trade-off between minimum compliance (i.e., maximum stiffness) and a fixed material amount for a given set of boundary conditions. Since TO is a non-convex problem, its gradient can be tuned by filtering the topology's contour, creating sharper material profiles without necessarily compromising optimality. However, despite simplifying the layout, some filters fail to address manufacturability concerns such as capillarity (thin tweaks as struts) generated by uncertain loading, vibration or fatigue. A tailored density-based filtering strategy is offered to tackle this issue. Additionally, volume fraction is left unconstrained so material can be strategically replenished through a logarithmic rule acting on the updated compliance. In doing so, an interpolation space with three degrees of freedom (volume, compliance, minimum thickness) is created, yielding diverse topologies for the same boundary conditions and design values along different stages of evolving topological families with distinct features. The optimization process is further accelerated by introducing the volume-compliance iterative scheme as a physical loss function in a Double Distance Neural Network (D$^2$NN), obtaining similar results to 2,000 steps worth of vanilla iteration within 500 training epochs. This proposal offers a novel topology optimization design space based on minimum strut thickness - via filtering - and topological families defined by minimum volume fraction and compliance. The methodology is tested on several examples with diverse loading and boundary conditions, obtaining similarly satisfactory results, and then boosted via Machine Learning, acting as a fast and cheap surrogate.