{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T14:44:57Z","timestamp":1769352297692,"version":"3.49.0"},"reference-count":37,"publisher":"ASME International","issue":"1","license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/100000084","name":"Directorate for Engineering","doi-asserted-by":"publisher","award":["1563413"],"award-info":[{"award-number":["1563413"]}],"id":[{"id":"10.13039\/100000084","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000151","name":"Division of Industrial Innovation and Partnerships","doi-asserted-by":"publisher","award":["2126882"],"award-info":[{"award-number":["2126882"]}],"id":[{"id":"10.13039\/100000151","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper introduces a new method using deep neural networks for the interactive digital transformation and simulation of n-bar planar linkages, which consist of revolute and prismatic joints, based on hand-drawn sketches. Instead of relying solely on computer vision, our approach combines topological knowledge of linkage mechanisms with the outcomes of a convolutional deep neural network. This creates a framework for recognizing hand-drawn sketches. We generate a dataset of synthetic images that resemble hand-drawn sketches of linkage mechanisms. Next, we fine-tune a state-of-the-art deep neural network to detect discrete objects using building blocks that represent joints and links in various positions, sizes, and orientations within these sketches. We then conduct a topological analysis on the detected objects to construct a kinematic model of the sketched mechanisms. The results demonstrate the effectiveness of our algorithm in handling hand-drawn sketches and converting them into digital representations. This has practical implications for improving communication, analysis, organization, and classification of planar mechanisms.<\/jats:p>","DOI":"10.1115\/1.4064037","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T04:29:43Z","timestamp":1699417783000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":6,"title":["Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation"],"prefix":"10.1115","volume":"24","author":[{"given":"Anar","family":"Nurizada","sequence":"first","affiliation":[{"name":"Stony Brook University Computer-Aided Design and Innovation Lab, Department of Mechanical Engineering, , Stony Brook, NY 11794-2300"}]},{"given":"Anurag","family":"Purwar","sequence":"additional","affiliation":[{"name":"Stony Brook University Computer-Aided Design and Innovation Lab, Department of Mechanical Engineering, , Stony Brook, NY 11794-2300"}]}],"member":"33","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"2024113001021060900_CIT0001","article-title":"Deep-Learning-Based Machine Understanding of Sketches: Recognizing and Generating Sketches With Deep Neural Networks","author":"Huang","year":"2020"},{"key":"2024113001021060900_CIT0002","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-46604-0_56","article-title":"Convolutional Sketch Inversion","author":"G\u00fc\u00e7l\u00fct\u00fcrk","year":"2016"},{"key":"2024113001021060900_CIT0003","author":"Ellis","year":"2017"},{"issue":"4","key":"2024113001021060900_CIT0004","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2897824.2925972","article-title":"Learning to Simplify","volume":"35","author":"Simo-Serra","year":"2016","journal-title":"Trans. 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