An Artificial Intelligence–Assisted Design Method for Topology Optimization without Pre-Optimized Training Data
Source abstract
Abstract
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 method for topology optimization, which does not require any optimized data. An artificial neural network—the predictor—provides the designs on the basis of boundary conditions and degree of filling as input data. In the training phase, the so-called evaluators evaluate the generated geometries on the basis of random input data with respect to given criteria. The results of those evaluations flow into an objective function, which is minimized by adapting the predictor’s parameters. After training, the presented AI-assisted design procedure generates geometries that are similar to those of conventional topology optimizers, but require only a fraction of the computational effort. We believe that our work could be a clue for AI-based methods that require data that are difficult to compute or unavailable.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topology Optimization in Engineering · Advanced Numerical Analysis Techniques · Robotic Mechanisms and Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, Chemnitz University of Technology, 09126 Chemnitz, Germany
References (34)
Show source-provided references
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