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<\/figure>\n<\/div>\n\n\nBuilding on this work, we next addressed automated screw removal, one of the most common fastening methods used in electronic products such as laptops. Preserving components and materials for reuse or recycling requires screws to be removed nondestructively, making reliable automated extraction an important challenge. We designed a system equipped with a three-axis positioning device, multiple cameras, and a custom sensor-equipped screwdriver that incorporated force and acceleration sensing to accommodate laptops of various sizes. The Soar cognitive architecture was used to coordinate the disassembly sequence through an automated procedure that identified, aligned with, and removed individual screws. In addition, the system differentiated between laptop models and stored screw locations for each model, reducing disassembly time when previously encountered models were processed again.<\/p>\n\n\n
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More recently, our research has shifted toward AI-enabled robotic disassembly, with an emphasis on robust screw detection and autonomous fastener extraction. We developed a deep learning object detection system based on Tiny-YOLO v2 for detecting cross-recessed screws (CRS). We also investigated the trade-off between input resolution and mini-batch size to maximize detection performance across a variety of electronic devices while efficiently utilizing GPU resources. A subsequent study using YOLO v5 further improved automated CRS detection in laptops. This work showed that several screw characteristics influence detection performance, including screw hole depth, the presence of a taper in the screw hole, screw hole location, and the color contrast between the laptop cover and the screw. Among these factors, screw hole depth had the greatest impact on detection accuracy.<\/p>\n\n\n
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Complementing these AI-based detection methods, we developed a robotic fastener extraction system built around a UR5 robotic manipulator equipped with a specialized tool suite for removing CRS fasteners. The system combined a deep convolutional neural network (DCNN)-based screw detection with a custom software package that integrated the robot, tooling, camera system, robot controller, and RoboDK simulation environment into a unified automated disassembly platform.<\/p>\n\n\n
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Separately, we have extended this research to the automated disassembly of electric vehicle (EV) batteries. We conducted a study assessing the automation potential of EV battery disassembly and developed an easy-to-use catalog of ten criteria for evaluating individual disassembly steps. We demonstrated the applicability of the catalog by evaluating both a plug-in hybrid electric vehicle (PHEV) battery and a battery electric vehicle (BEV) battery, providing a structured framework for assessing the automation potential of future EV battery designs.<\/p>\n\n\n
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References:<\/strong><\/p>\n\n\n\n Electronic waste (e-waste) is a growing global challenge as the number of electronic devices continues to increase while their service lifespans decline. Despite the growing need for sustainable recycling and reuse, efficient methods for automated, non-destructive e-waste disassembly remain limited. Our initial work in this area focused on the design and characterization of a prototype […]<\/p>\n","protected":false},"author":4282,"featured_media":1113,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[3,29,8,1],"tags":[17,16],"class_list":["post-1106","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industrial-automation","category-jouaneh","category-research","category-uncategorized","tag-jouaneh","tag-automation"],"acf":[],"_links":{"self":[{"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/posts\/1106","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/users\/4282"}],"replies":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/comments?post=1106"}],"version-history":[{"count":5,"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/posts\/1106\/revisions"}],"predecessor-version":[{"id":1124,"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/posts\/1106\/revisions\/1124"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/media\/1113"}],"wp:attachment":[{"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/media?parent=1106"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/categories?post=1106"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/web.uri.edu\/icrl\/wp-json\/wp\/v2\/tags?post=1106"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n