A neural network-based method for spruce tonewood characterization.

Abstract

The acoustical properties of wood are primarily a function of its elastic properties. Numerical and analytical methods for wood material characterization are available, although they are either computationally demanding or not always valid. Therefore, an affordable and practical method with sufficient accuracy is missing. In this article, we present a neural network-based method to estimate the elastic properties of spruce thin plates. The method works by encoding information of both the eigenfrequencies and eigenmodes of the system and using a neural network to find the best possible material parameters that reproduce the frequency response function. Our results show that data-driven techniques can speed up classic finite element model updating by several orders of magnitude and work as a proof of concept for a general neural network-based tool for the workshop.