Numerically Trained Ultrasound AI for Monitoring Tool Degradation
| dc.contributor.author | Jin, Yuqi | en |
| dc.contributor.author | Wang, Xinyue | en |
| dc.contributor.author | Fox, Edward A. | en |
| dc.contributor.author | Xie, Zhiwu | en |
| dc.contributor.author | Neogi, Arup | en |
| dc.contributor.author | Mishra, Rajiv S. | en |
| dc.contributor.author | Wang, Tianhao | en |
| dc.date.accessioned | 2024-01-22T13:01:07Z | en |
| dc.date.available | 2024-01-22T13:01:07Z | en |
| dc.date.issued | 2022-01-13 | en |
| dc.description.abstract | Monitoring tool degradation during manufacturing can ensure product accuracy and reliability. However, due to variations in degradation conditions and complexity in signal analysis, effective and broadly applicable monitoring is still challenging to achieve. Herein, a novel monitoring method using ultrasound signals augmented with a numerically trained machine learning technique is reported to monitor the wear condition of friction stir welding and processing tools. Ultrasonic signals travel axially inside the tools, and even minor tool wear will change the time and amplitude of the reflected signal. An artificial intelligence (AI) algorithm is selected as a suitable referee to identify the small variations in the tool conditions based on the reflected ultrasound signals. To properly train the AI referee, a human‐error‐free data bank using numerical simulation is generated. The simulation models the experimental conditions with high fidelity and can provide comparable ultrasound signals. As a result, the trained AI model can recognize the tool wear from real experiments with subwavelength accuracy prediction of the worn amount on the tool pins. | en |
| dc.description.version | Submitted version | en |
| dc.format.extent | 8 page(s) | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier | ARTN 2100215 (Article number) | en |
| dc.identifier.doi | https://doi.org/10.1002/aisy.202100215 | en |
| dc.identifier.eissn | 2640-4567 | en |
| dc.identifier.issn | 2640-4567 | en |
| dc.identifier.issue | 5 | en |
| dc.identifier.orcid | Fox, Edward [0000-0003-1447-6870] | en |
| dc.identifier.orcid | Xie, Zhiwu [0000-0002-2702-3806] | en |
| dc.identifier.uri | https://hdl.handle.net/10919/117426 | en |
| dc.identifier.volume | 4 | en |
| dc.language.iso | en | en |
| dc.publisher | Wiley | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | machine learning | en |
| dc.subject | subwavelength accuracy | en |
| dc.subject | tool wear | en |
| dc.subject | ultrasonic detection | en |
| dc.title | Numerically Trained Ultrasound AI for Monitoring Tool Degradation | en |
| dc.title.serial | Advanced Intelligent Systems | en |
| dc.type | Article | en |
| dc.type.dcmitype | Text | en |
| dc.type.other | Article | en |
| dc.type.other | Journal | en |
| pubs.organisational-group | /Virginia Tech | en |
| pubs.organisational-group | /Virginia Tech/Engineering | en |
| pubs.organisational-group | /Virginia Tech/Engineering/Computer Science | en |
| pubs.organisational-group | /Virginia Tech/Library | en |
| pubs.organisational-group | /Virginia Tech/All T&R Faculty | en |
| pubs.organisational-group | /Virginia Tech/Engineering/COE T&R Faculty | en |
| pubs.organisational-group | /Virginia Tech/Library/Dean's office | en |