Metamodel Based Forward and Inverse Design for Passive Vibration Suppression

TL;DR

This paper introduces a metamodel-based framework for the forward and inverse design of aperiodic 1D metamaterials, specifically a drill string, to optimize vibration suppression with reduced computational effort.

Contribution

It develops an analytical and neural network-based approach for efficient optimization and inverse design of aperiodic metamaterials, enabling rapid, low-cost design solutions.

Findings

Neural network models accurately predict frequency response.

Optimization reduces mass while controlling resonance peaks.

Inverse neural network effectively designs metamaterials for target frequencies.

Abstract

Aperiodic metamaterials represent a class of structural systems that are composed of different building blocks (cells), instead of a self-repeating chain of the same unit cells. Optimizing aperiodic cellular structural systems thus presents high-dimensional problems that are challenging to solve using purely high-fidelity structural optimization approaches. Specialized analytical modeling along with metamodel based optimization can provide a more tractable alternative solution approach. To this end, this paper presents a design automation framework applied to a 1D metamaterial system, namely a drill string, where vibration suppression is of utmost importance. The drill string comprises a set of nonuniform rings attached to the outer surface of a longitudinal rod. As such, the resultant system can now be perceived as an aperiodic 1D metamaterial with each ring/gap representing a cell. Despite being a 1D system, the simultaneous consideration of multiple DoF (i.e., torsional, axial, and lateral motions) poses significant computational challenges. Therefore, a transfer matrix method (TMM) is employed to analytically determine the frequency response of the drill string. A suite of neural networks (ANN) is trained on TMM samples (which present minute-scale computing costs per evaluation), to model the frequency response. ANN-based optimization is then performed to minimize mass subject to constraints on the gap between consecutive resonance peaks in one case, and minimizing this gap in the second case, leading to crucial improvements over baselines. Further novel contribution occurs through the development of an inverse modeling approach that can instantaneously produce the 1D metamaterial design with minimum mass for a given desired non-resonant frequency range. This is accomplished by using invertible neural networks, and results show promising alignment with forward solutions.