Applied algorithm in the liner inspection of solid rocket motors
•An algorithm to detect defective areas on the liner surface is proposed.•Image surface information is recovered with photometric stereo.•K-nearest neighbor method classifies image pixels into two classes: non-defect and defect.•The algorithm can detect discontinuities and foreign objects on the lin...
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Published in: | Optics and lasers in engineering Vol. 102; pp. 143 - 153 |
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Main Authors: | , , |
Format: | Journal Article |
Language: | English |
Published: |
Elsevier Ltd
01-03-2018
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Subjects: | |
Online Access: | Get full text |
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Summary: | •An algorithm to detect defective areas on the liner surface is proposed.•Image surface information is recovered with photometric stereo.•K-nearest neighbor method classifies image pixels into two classes: non-defect and defect.•The algorithm can detect discontinuities and foreign objects on the liner surface.•The algorithm can prevent late characterization of bonding defects.
In rocket motors, the bonding between the solid propellant and thermal insulation is accomplished by a thin adhesive layer, known as liner. The liner application method involves a complex sequence of tasks, which includes in its final stage, the surface integrity inspection. Nowadays in Brazil, an expert carries out a thorough visual inspection to detect defects on the liner surface that may compromise the propellant interface bonding. Therefore, this paper proposes an algorithm that uses the photometric stereo technique and the K-nearest neighbor (KNN) classifier to assist the expert in the surface inspection. Photometric stereo allows the surface information recovery of the test images, while the KNN method enables image pixels classification into two classes: non-defect and defect. Tests performed on a computer vision based prototype validate the algorithm. The positive results suggest that the algorithm is feasible and when implemented in a real scenario, will be able to help the expert in detecting defective areas on the liner surface. |
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ISSN: | 0143-8166 1873-0302 |
DOI: | 10.1016/j.optlaseng.2017.11.006 |