Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm

TL;DR

This paper presents a high-performance computing implementation of the U-BRAIN soft computing algorithm for diagnosing defects in aerospace structures, enabling efficient processing of complex multi-parameter data.

Contribution

It introduces a novel HPC parallel version of the U-BRAIN algorithm for aerospace defect diagnosis, improving data processing capabilities.

Findings

Effective defect classification in aerospace structures

HPC implementation enhances processing speed and scalability

System successfully tested on Linux-based multi-core clusters

Abstract

This study concerns with the diagnosis of aerospace structure defects by applying a HPC parallel implementation of a novel learning algorithm, named U-BRAIN. The Soft Computing approach allows advanced multi-parameter data processing in composite materials testing. The HPC parallel implementation overcomes the limits due to the great amount of data and the complexity of data processing. Our experimental results illustrate the effectiveness of the U-BRAIN parallel implementation as defect classifier in aerospace structures. The resulting system is implemented on a Linux-based cluster with multi-core architecture.