Advanced Deep Learning Techniques for Industry 4.0: Application to Mechanical Design and Structural Health Monitoring

Abstract

Nowadays, Deep Learning (DL) techniques are increasingly employed in industrial applications. This paper investigate the development of data-driven models for two use cases: Additive Manufacturing-driven Topology Optimization and Structural Health Monitoring (SHM). We first propose an original data-driven generative method that integrates the mechanical and geometrical constraints concurrently at the same conceptual level and generates a 2D design accordingly. In this way, it adapts the geometry of the design to the manufacturing criteria, allowing the designer better interpretation and avoiding being stuck in a timeconsuming loop of drawing the CAD and testing its performance. On the other hand, SHM technique is dedicated to the continuous and non-invasive monitoring of structures integrity, ensuring safety and optimal performances through on-site real-time measurements. We propose in this work new ways of structuring data that increase the accuracy of data driven SHM algorithms and that are based on the physical knowledge related with the structure to be inspected. We focus our study on the damage classification step within the aeronautic context, where the primary objective is to distinguish between different damage types in composite.