Developing neural networks to rapidly map crystallographic orientation using laser ultrasound measurements
Year: 2024
Type: article
Abstract: Rapid measurement of crystal orientation is critical in the materials discovery process as it facilitates real-time decision-making and quality control. Acoustic inspection methods rapidly characterise microstructure without the need for extensive infrastructure or expense – the laser ultrasonic method known as Spatially Resolved Acoustic Spectroscopy (SRAS) has been developed with this intent and accurately characterises crystal orientation by leveraging a combination of forward modelling and an exhaustive brute force process to obtain the best-fit orientation. While effective, this method is computationally demanding and time-intensive. We introduce a novel approach that utilises neural networks to classify measured acoustic signals into orientation planes to significantly expedite the characterisation process and demonstrate classification on real-world Inconel 617 and CMX4 specimens. A reduction in the orientation determination time from around 10 hours (brute-force search) down to 15 seconds (neural network) was achieved while exhibiting an average plane angle difference of between 5.3° and 13.8°.
Source: Scripta Materialia
Authors: Rikesh Patel, Wenqi Li, Richard J. Smith, Matt Clark
Institution: University of Nottingham
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,220
OpenAlex ID: W4403482450