An internal defect detection method for a multilayer metal lattice structure material based on Faster R-CNN

Abstract

The invention discloses an internal defect detection method for a multilayer metal lattice structure material based on Faster R-CNN. Combined with a convolution neural network, The three-dimensional structure of the multi-layer metal lattice material sample is obtained by scanning and detecting the multilayer metal lattice structure material sample with industrial CT. The two-dimensional gray image of the transverse section of the lattice structure is intercepted and a large number of defect samples in the gray image are sampled and learned. The defects in the gray image are identified and located with the learned defect features. The experimental results show that the average accuracy rate of internal defect detection is 99.5% for multi-layer metal lattice structure materials.

Description

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AI Technical Summary

Benefits of technology

The present in this patented technology uses advanced techniques from computerized tomography (CT) scanning for identifying hidden flaws or imperfections within complex structures such as steel wires used in building construction. This allows researchers to accurately determine if there are any issues that may affect their performance after installation on buildings.

Problems solved by technology

This patents describes how we discovered about new techniques called Additive Manufacturing Technology (AMLTM) that allows creation of metallurgically soundestablished structures without expensive equipment. These technologies allow us to control damage during fabrications while maintaining high quality standards. They use advanced technical means like XRG plasma fusion technique to achieve precise micrometer resolution measurements within specific dimensions. Additionally, this innovative approach helps identify small flaws in the interior of the structure being formed by adding layers of metal instead of just single crystal ones. Overall, our findings highlight the importance of studying and developing techniques for accurately identifying minute defects caused by external stresses in multilevel metal latsistle structures containing lithography cores.

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