An Artificial Intelligence–Assisted Design Method for Topology Optimization without Pre-Optimized Training Data

Engineers widely use topology optimization during the initial process of product development to obtain a first possible geometry design. The state-of-the-art method is iterative calculation, which requires both time and computational power. This paper proposes an AI-assisted design for optimization, does not require any optimized data. An artificial neural network—the predictor—provides designs on basis boundary conditions degree filling as input In training phase, so-called evaluators evaluate generated geometries random data with respect given criteria. results those evaluations flow into objective function, minimized by adapting predictor’s parameters. After training, presented procedure generates that are similar conventional optimizers, but only fraction effort. We believe our work could be clue AI-based methods difficult compute or unavailable.