A System for Robotic Extraction of Fasteners
Automating the extraction of mechanical fasteners from end-of-life (EOL) electronic waste is challenging due to unpredictable conditions and unknown fastener locations relative to robotic coordinates. This study develops a system for extracting cross-recessed screws using a Deep Convolutional Neural...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/618 |
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| author | Austin Clark Musa K. Jouaneh |
| author_facet | Austin Clark Musa K. Jouaneh |
| author_sort | Austin Clark |
| collection | DOAJ |
| description | Automating the extraction of mechanical fasteners from end-of-life (EOL) electronic waste is challenging due to unpredictable conditions and unknown fastener locations relative to robotic coordinates. This study develops a system for extracting cross-recessed screws using a Deep Convolutional Neural Network (DCNN) for screw detection, integrated with industrial robot simulation software. The simulation models the tooling, camera, environment, and robot kinematics, enabling real-time control and feedback between the robot and the simulation environment. The system, tested on a robotic platform with custom tooling, including force and torque sensors, aimed to optimize fastener removal. Key performance indicators included the speed and success rate of screw extraction, with success rates ranging from 78 to 89% on the first pass and 100% on the second. The system uses a state-based program design for fastener extraction, with real-time control via a web-socket interface. Despite its potential, the system faces limitations, such as longer cycle times, with single fastener extraction taking over 30 s. These challenges can be mitigated by refining the tooling, DCNN model, and control logic for improved efficiency. |
| format | Article |
| id | doaj-art-cba7f59619c746f6ad03e341346a6bcd |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-cba7f59619c746f6ad03e341346a6bcd2025-01-24T13:20:05ZengMDPI AGApplied Sciences2076-34172025-01-0115261810.3390/app15020618A System for Robotic Extraction of FastenersAustin Clark0Musa K. Jouaneh1Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USADepartment of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USAAutomating the extraction of mechanical fasteners from end-of-life (EOL) electronic waste is challenging due to unpredictable conditions and unknown fastener locations relative to robotic coordinates. This study develops a system for extracting cross-recessed screws using a Deep Convolutional Neural Network (DCNN) for screw detection, integrated with industrial robot simulation software. The simulation models the tooling, camera, environment, and robot kinematics, enabling real-time control and feedback between the robot and the simulation environment. The system, tested on a robotic platform with custom tooling, including force and torque sensors, aimed to optimize fastener removal. Key performance indicators included the speed and success rate of screw extraction, with success rates ranging from 78 to 89% on the first pass and 100% on the second. The system uses a state-based program design for fastener extraction, with real-time control via a web-socket interface. Despite its potential, the system faces limitations, such as longer cycle times, with single fastener extraction taking over 30 s. These challenges can be mitigated by refining the tooling, DCNN model, and control logic for improved efficiency.https://www.mdpi.com/2076-3417/15/2/618roboticscomputer visionDCNNautomationdisassembly |
| spellingShingle | Austin Clark Musa K. Jouaneh A System for Robotic Extraction of Fasteners Applied Sciences robotics computer vision DCNN automation disassembly |
| title | A System for Robotic Extraction of Fasteners |
| title_full | A System for Robotic Extraction of Fasteners |
| title_fullStr | A System for Robotic Extraction of Fasteners |
| title_full_unstemmed | A System for Robotic Extraction of Fasteners |
| title_short | A System for Robotic Extraction of Fasteners |
| title_sort | system for robotic extraction of fasteners |
| topic | robotics computer vision DCNN automation disassembly |
| url | https://www.mdpi.com/2076-3417/15/2/618 |
| work_keys_str_mv | AT austinclark asystemforroboticextractionoffasteners AT musakjouaneh asystemforroboticextractionoffasteners AT austinclark systemforroboticextractionoffasteners AT musakjouaneh systemforroboticextractionoffasteners |