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Correction: Kang et al. Fluid Flow to Electricity: Capturing Flow-Induced Vibrations with Micro-Electromechanical-System-Based Piezoelectric Energy Harvester. <i>Micromachines</i> 2024, <i>15</i>, 581.

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A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model.

Quan Liu1, Wupeng Deng1,2, Duc Truong Pham2

  • 1School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.

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|May 27, 2023
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Summary
This summary is machine-generated.

This study introduces an automated two-stage screw detection system for remanufacturing. It accurately identifies damaged screws even in uneven lighting, improving automated disassembly efficiency.

Keywords:
data learningillumination conditionreflection featurerobotic disassemblyscrew detection

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Area of Science:

  • Engineering
  • Robotics
  • Computer Vision

Background:

  • Remanufacturing requires efficient automated disassembly processes.
  • Automatic screw removal is a critical step in end-of-life product disassembly.
  • Existing methods struggle with damaged screws and variable lighting conditions.

Purpose of the Study:

  • To develop a robust automated visual detection framework for structurally damaged screws.
  • To enable accurate screw detection under uneven illumination for remanufacturing.
  • To enhance the economic viability of remanufacturing through improved automation.

Main Methods:

  • A two-stage detection framework utilizing reflection and texture features.
  • A linear regression model for reflection features to handle uneven illumination.
  • A self-optimization strategy and weighted fusion for robust detection.

Main Results:

  • The framework successfully detected structurally damaged screws in complex scenarios.
  • The method demonstrated effectiveness under uneven illumination conditions.
  • Implementation on a robotic platform for electric vehicle battery disassembly was successful.

Conclusions:

  • The developed framework enables automatic screw removal in complex disassembly tasks.
  • The use of reflection features and data learning offers novel approaches for automated disassembly.
  • This research contributes to more economically attractive remanufacturing processes.