A significant challenge in nondestructive evaluation has been understanding the difficulty in measuring rough defects. The traditional approaches, which include running numerous finite element models, were time-consuming and constraining.

Surrogate models use machine learning to closely approximate original finite element models of time-of-flight diffraction measurements in less than one millisecond. These models are trained on pairs of input (rough defect) and output (A-scan) data from finite element models, learning to generate accurate results almost instantly.

The surrogate model converts defect characteristics into A-scan data using a sequence-to-sequence approach that is reminiscent of natural language processing. Trained with 2,160 finite element results across different parameters, this model gives out highly precise A-scans within 0.425 milliseconds instead of the 8 minutes that it used to be in traditional methods.”

The ability to process rapid and scalable non-destructive examination of this machine learning invention enables instant generation of millions of results; this in turn ensures complete analysis as well as effective identification of faults within such sectors as manufacturing or infrastructure inspection.

The NDE industry can obtain higher efficiency and accuracy in defect identification which bolsters quality assurance and product dependability thus marking a significant step forward in non-destructive evaluation.