Abstract
A new approach to characterizing and predicting impact damage level in (Reaction Injection Molding) RIM structures is presented. The technique encodes thermal images maps and extracts features from presented thermal images. Complex Neural Networks structure is employed to reconstruct thermal imaging maps and predict the extent of damage an impact can cause. Neural network weigh elimination algorithm is used and proved effective in predicting areas of damage.
Highlights
Structural Integrity Monitoring (SIM) is the ability to detect and classify structural changes that have an adverse effect on the performance of the component
Traditional non-destructive testing (NDT) techniques are Impractical for efficient design and in-service testing
Each chosen composite was a laminate made by Resin Injection Molding (RIM) to produce 5mm thick samples
Summary
Structural Integrity Monitoring (SIM) is the ability to detect and classify structural changes that have an adverse effect on the performance of the component. Damage can lead to catastrophic consequences, which is critical in many commercial and military applications that employ composite materials to take advantage of their excellent specific strength and stiffness properties, and fatigue performance. Composite materials are hard to design, manufacture, and repair compared to other materials since they tend to fail through diverse interacting damage modes. Damage detection in composites is more difficult in other structures due to the structural characteristics of the composed Matrix-Fiber. Traditional non-destructive testing (NDT) techniques are Impractical for efficient design and in-service testing. It is essential to develop new testing techniques and advanced interpretation algorithms
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