Structured Machine Learning Tools for Modelling Characteristics of Guided Waves

TL;DR

This paper presents a novel, data-driven Gaussian process-based method for modeling guided wave behavior in composite materials, integrating physical knowledge to improve robustness and interpretability for NDE and SHM applications.

Contribution

It introduces a structured machine learning approach using Gaussian processes that incorporates physical constraints to model guided wave characteristics in complex materials.

Findings

Enhanced modeling accuracy for guided waves in composites

Improved extrapolation and physical interpretability of models

Demonstrated robustness of the structured ML approach

Abstract

The use of ultrasonic guided waves to probe the materials/structures for damage continues to increase in popularity for non-destructive evaluation (NDE) and structural health monitoring (SHM). The use of high-frequency waves such as these offers an advantage over low-frequency methods from their ability to detect damage on a smaller scale. However, in order to assess damage in a structure, and implement any NDE or SHM tool, knowledge of the behaviour of a guided wave throughout the material/structure is important (especially when designing sensor placement for SHM systems). Determining this behaviour is extremely diffcult in complex materials, such as fibre-matrix composites, where unique phenomena such as continuous mode conversion takes place. This paper introduces a novel method for modelling the feature-space of guided waves in a composite material. This technique is based on a data-driven model, where prior physical knowledge can be used to create structured machine learning tools; where constraints are applied to provide said structure. The method shown makes use of Gaussian processes, a full Bayesian analysis tool, and in this paper it is shown how physical knowledge of the guided waves can be utilised in modelling using an ML tool. This paper shows that through careful consideration when applying machine learning techniques, more robust models can be generated which offer advantages such as extrapolation ability and physical interpretation.