Detection of liner surface defects in solid rocket motors using multilayer perceptron neural networks

• Published in: Polymer testing Vol. 88; p. 106559
• Main Authors: Simões Hoffmann, Luiz Felipe, Parquet Bizarria, Francisco Carlos, Parquet Bizarria, José Walter
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-08-2020
• Subjects: Image analysis; Liner; Local binary pattern; Multilayer perceptron neural network; Solid rocket motor; Surface defect; Solid rocket motor; Multilayer perceptron neural network; Local binary pattern; Image analysis; Surface defect; Liner
• ISSN: 0142-9418; 1873-2348
• DOI: 10.1016/j.polymertesting.2020.106559

Debonding problems along the propellant/liner/insulation interface are a critical point to the integrity and one of the major causes of structural failures of solid rocket motors. Current solutions are typically restricted to methods for assessing the integrity of the rocket motors structure and visually inspecting their components. In this context, this paper presents an improved algorithm to detect liner surface defects that may compromise the bonding between the solid propellant and the insulation. The use of Local Binary Patterns (LBP) provides a structural and statistical approach to texture analysis of liner sample images. Along with color information extraction, these two methods allow the representation of image pixels by feature vectors that are further processed by a Multilayer Perceptron (MLP) neural network classifier. The MLP neural network analyzes liner sample images and classifies each pixel into one of three classes: non-defect, foreign object, and defect. Several tests were executed varying different parameters to find the optimal MLP configuration, and as a result, the best classification accuracy of 99.08%, 90.66%, and 99.48% was achieved for the corresponding classes. Moreover, the defect size estimate showed that the MLP classifier correctly identified defects less than 1 mm long, with a relatively small number of training examples. Positive results indicate that the algorithm can identify liner surface defects with a performance similar to human inspectors and has the potential to assist or even automate the liner inspection process of solid rocket motors.
•An algorithm to detect defective areas on the liner surface of solid rocket motors is proposed.•A neural network model classifies image pixels based on color and texture features.•The algorithm can detect defects on the liner surface with a precision comparable to human inspectors.•The algorithm can prevent the late characterization of bonding defects.