SLIDE: A machine-learning based method for forced dynamic response estimation of multibody systems

TL;DR

SLIDE is a deep learning method that efficiently estimates the dynamic response of multibody systems, significantly speeding up simulations without needing full system states.

Contribution

The paper introduces SLIDE, a neural network-based approach that estimates system responses using truncated outputs and error prediction, improving simulation speed and applicability.

Findings

Achieved up to several million times speedup in simulations.

Effectively estimated responses of various systems including industrial manipulators.

Outperformed traditional methods in speed while maintaining accuracy.

Abstract

In computational engineering, enhancing the simulation speed and efficiency is a perpetual goal. To fully take advantage of neural network techniques and hardware, we present the SLiding-window Initially-truncated Dynamic-response Estimator (SLIDE), a deep learning-based method designed to estimate output sequences of mechanical or multibody systems with primarily, but not exclusively, forced excitation. A key advantage of SLIDE is its ability to estimate the dynamic response of damped systems without requiring the full system state, making it particularly effective for flexible multibody systems. The method truncates the output window based on the decay of initial effects, such as damping, which is approximated by the complex eigenvalues of the systems linearized equations. In addition, a second neural network is trained to provide an error estimation, further enhancing the methods applicability. The method is applied to a diverse selection of systems, including the Duffing oscillator, a flexible slider-crank system, and an industrial 6R manipulator, mounted on a flexible socket. Our results demonstrate significant speedups from the simulation up to several millions, exceeding real-time performance substantially.