{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:35:41Z","timestamp":1781534141715,"version":"3.54.5"},"reference-count":36,"publisher":"ASME International","issue":"11","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2433484"],"award-info":[{"award-number":["2433484"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Tool condition monitoring (TCM) is a critical maintenance task in industrial-scale ultrasonic metal welding (UMW). UMW tools, consisting of a horn and an anvil, experience geometric changes over time, which negatively affect the joint quality and introduce significant process variations. Conventional indirect TCM methods have achieved high accuracy in classification tasks; however, they cannot provide detailed insights into the geometric changes of tools. In contrast, using high-resolution 3D metrology, direct TCM methods can characterize and monitor tool surface degradation more comprehensively, but such measurements are usually expensive and time-consuming to acquire, which prevent their widespread use on the factory floor. To address these challenges, this article presents a novel, cost-effective optical imaging system that uses optical images of resin cast replicas for fine-scale characterization of tool surface degradation in UMW. Furthermore, computer vision (CV) algorithms are developed to process and analyze 2D optical images to support a series of maintenance decision-making tasks. Based on a U-Net architecture, a segmentation network identifies the tip regions most prone to wear, while a two-stage model further reconstructs 3D height maps, all from 2D optical images. Additionally, a convolutional neural network provides end-to-end predictions of aggregated knurl-level geometric features. Experimental results demonstrate that the CV models accurately capture geometric tool wear features at both knurl level and tool level from optical images, offering an efficient, effective, and interpretable alternative to high-resolution 3D measurements for fine-scale TCM in UMW.<\/jats:p>","DOI":"10.1115\/1.4069219","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T10:07:01Z","timestamp":1753178821000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":4,"title":["Fine-Scale Characterization and Monitoring of Tool Surface Degradation in Ultrasonic Metal Welding Using Optical Measurements and Computer Vision"],"prefix":"10.1115","volume":"25","author":[{"given":"Zhiqiao","family":"Dong","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/047426m28","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Illinois at Urbana-Champaign Department of Mechanical Science and Engineering, , , \u00a0","place":["Urbana, IL, 61801"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenhui","family":"Shao","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00jmfr291","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Michigan Department of Mechanical Engineering, , , \u00a0 ;","place":["Ann Arbor, MI, 48109"]},{"name":"University of Illinois at Urbana-Champaign Department of Mechanical Science and Engineering, , , \u00a0","place":["Urbana, IL, 61801"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"33","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"issue":"1","key":"2025081808500362600_CIT0001","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.cirp.2011.03.016","article-title":"Process Robustness of Single Lap Ultrasonic Welding of Thin, Dissimilar Materials","volume":"60","author":"Kim","year":"2011","journal-title":"CIRP Ann."},{"key":"2025081808500362600_CIT0002","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.jmsy.2018.04.014","article-title":"Improving Process Robustness in Ultrasonic Metal Welding of Lithium-Ion Batteries","volume":"48","author":"Nong","year":"2018","journal-title":"J. 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