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An Automated Classification Technique for Detecting Defects in Battery Cells
- Publication Year :
- 2006
- Publisher :
- United States: NASA Center for Aerospace Information (CASI), 2006.
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Abstract
- Battery cell defect classification is primarily done manually by a human conducting a visual inspection to determine if the battery cell is acceptable for a particular use or device. Human visual inspection is a time consuming task when compared to an inspection process conducted by a machine vision system. Human inspection is also subject to human error and fatigue over time. We present a machine vision technique that can be used to automatically identify defective sections of battery cells via a morphological feature-based classifier using an adaptive two-dimensional fast Fourier transformation technique. The initial area of interest is automatically classified as either an anode or cathode cell view as well as classified as an acceptable or a defective battery cell. Each battery cell is labeled and cataloged for comparison and analysis. The result is the implementation of an automated machine vision technique that provides a highly repeatable and reproducible method of identifying and quantifying defects in battery cells.
- Subjects :
- Metals And Metallic Materials
Subjects
Details
- Language :
- English
- Database :
- NASA Technical Reports
- Notes :
- WBS 22-101-53-00
- Publication Type :
- Report
- Accession number :
- edsnas.20060046624
- Document Type :
- Report