Quantitative assessment of impact damage in stitched foam‐filled Aluminium honeycomb Sandwich panels by experimental and machine learning methods

Abstract

AbstractNovel Stitched Foam‐filled Honeycomb Sandwich (SFHS) panels have been fabricated using vacuum‐assisted resin transfer molding to address the weak interfaces between the face sheets and the core in the Foam‐filled Honeycomb Sandwich (FHS) panel. The SFHS panels have shown better load‐bearing capacity and performance characteristics compared to FHS panel after Low‐Velocity Impact (LVI) tests. After the LVI test, MATLAB image processing was used to analyze the impact damage areas and failure mechanisms. In addition, Machine Learning regression algorithms were employed to predict the optimal amount of energy absorbed during low‐velocity impact testing of fabricated panels with a maximum impactor drop height of 700 mm. The results indicated that nylon yarn stitching significantly improved energy absorption and interfacial behavior compared to unstitched honeycomb panels. This research also revealed that SFHS1 panels with adjacent honeycomb cell stitching are more impact resistant, provide increased load carrying capacity, and are cost‐effective. These panels can be utilized by modern engineers to increase economy, durability, and functionality in industrial, automotive, and construction applications.Highlights Stitched Foam Filled Honeycomb Sandwich (SFHS) panels, manufactured via resin transfer molding, and eliminates weak interfaces between the face sheets and core. SFHS panels outperformed unstitched panels in load‐bearing and energy absorption during low‐velocity impact, as confirmed by MATLAB image analysis showing reduced damage and failure. Machine learning algorithms particularly polynomial regression model predicted maximum absorption energy precisely with 99.9% accuracy, close to experimental results. SFHS panels can be used in automotive and industrial applications due to their through‐thickness stitching.