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A robotic manipulator for handling TLD badges.
S Levinson1, M Weinstein, A Abraham
1Radiation Safety Department, Nuclear Research Center Negev, Beer-Sheva, Israel. sea-and-spa@013.net.il
Health Physics
|October 18, 2008
Summary
A robotic system automates the handling of thermoluminescent dosimetry (TLD) badges, improving efficiency and quality assurance. This automated process ensures accurate TLD card and badge correlation for radiation monitoring.
Area of Science:
- Medical Physics
- Radiation Dosimetry
- Automation Engineering
Background:
- Manual handling of thermoluminescent dosimetry (TLD) badges can be time-consuming and prone to errors.
- Accurate tracking of TLD cards within badges is crucial for reliable radiation exposure monitoring.
- Existing systems may lack the automation necessary for high-throughput processing and quality control.
Purpose of the Study:
- To design and build a prototype automated system for handling TLD badges.
- To enhance the quality assurance process in TLD badge management.
- To ensure accurate correlation between TLD cards and their corresponding badges.
Main Methods:
- Development of a robotic arm system for automated TLD badge handling.
- Implementation of barcode technology for worker identification and data management.
- Integration of loading/unloading and magazine stations for efficient workflow.
- On-line identification for real-time verification of badge-TLD card correlation.
Main Results:
- Successful design and construction of a functional prototype automated TLD badge handling system.
- Demonstrated capability of the robotic arm to load, unload, and transport TLD cards and badges.
- Effective implementation of barcode stickers for worker details and quality assurance.
- Verified accuracy of automatic on-line identification for badge-TLD card correlation.
Conclusions:
- The prototype system demonstrates the feasibility of automating TLD badge handling.
- Automation significantly improves the efficiency and reliability of radiation dosimetry processes.
- The integrated quality assurance measures enhance the integrity of worker exposure data.

