Structured Machine Learning Tools for Modelling Characteristics of Guided Waves
This research is significant for engineers and researchers in non-destructive evaluation and structural health monitoring, as it offers a new approach to accurately model guided wave behavior in complex composite materials, which is a known bottleneck for designing effective sensor placement and damage assessment systems.
This paper addresses the challenge of modeling guided wave behavior in complex materials like fiber-matrix composites, which is crucial for non-destructive evaluation and structural health monitoring. It introduces a novel data-driven method using structured machine learning tools, specifically Gaussian processes, to incorporate prior physical knowledge and generate more robust models with extrapolation ability and physical interpretation.
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.