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However, cooperative RAM suffers from the same defect generation challenges as conventional systems, necessitating improvements in the detection and prevention of flaws within fabricated components. Quality assurance can be further bolstered through the integration of AM models, which utilize sensor feedback to localize defects, vastly reducing false positives. This research explores defect localization through a novel dynamic defect model created from simulated sensing data. In particular, two cooperative robots are simulated to estimate defect parameters, while observing the workspace and accurately classifying different regions of the part, generating a Gaussian mixture map that identifies and assigns appropriate actions based on defect types and characteristics. The experimental result shows that the implementation of the dynamic defect model and selective reevaluation achieved an effective defect detection accuracy of 99.9%, an improvement of 9.9% without localization. The proposed framework holds potential for application in domains that utilize high degrees-of-freedom machines and collaborative agents, offering scalability, improved fabrication speeds, and enhanced mechanical properties.<\/jats:p>","DOI":"10.1115\/1.4065525","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T10:24:01Z","timestamp":1715768641000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":1,"title":["Stochastic Defect Localization for Cooperative Additive Manufacturing Using Gaussian Mixture Maps"],"prefix":"10.1115","volume":"24","author":[{"given":"Sean","family":"Rescsanski","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02der9h97","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Connecticut School of Mechanical, Aerospace, and Manufacturing Engineering, , Storrs, CT 06269"},{"name":"University of Connecticut School of Mechanical, Aerospace, and Manufacturing Engineering, , Storrs, CT 06269"}]},{"given":"Vihaan","family":"Shah","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02der9h97","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Connecticut School of Computing, , Storrs, CT 06269"},{"name":"University of Connecticut School of Computing, , Storrs, CT 06269"}]},{"given":"Jiong","family":"Tang","sequence":"additional","affiliation":[{"name":"University of Connecticut School of Mechanical, Aerospace, and Manufacturing Engineering, , Storrs, CT 06269"}]},{"given":"Farhad","family":"Imani","sequence":"additional","affiliation":[{"name":"University of Connecticut School of Mechanical, Aerospace, and Manufacturing Engineering, , Storrs, CT 06269"}]}],"member":"33","published-online":{"date-parts":[[2024,7,22]]},"reference":[{"issue":"12","key":"2025072120015035800_CIT0001","doi-asserted-by":"publisher","first-page":"231","DOI":"10.3390\/buildings10120231","article-title":"Additive Manufacturing Applications for Industry 4.0: A Systematic Critical Review","volume":"10","author":"Sepasgozar","year":"2020","journal-title":"Buildings"},{"issue":"2","key":"2025072120015035800_CIT0002","doi-asserted-by":"publisher","first-page":"1687814018822880","DOI":"10.1177\/1687814018822880","article-title":"Additive Manufacturing: Challenges, Trends, and Applications","volume":"11","author":"Abdulhameed","year":"2019","journal-title":"Adv. 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