journal article·2003-05Open access

Computational Analysis and Optimization for Smart Patching Repairs

George J. Tsamasphyros, Nikos K. Furnarakis, George N. Kanderakis, Zaira P. Marioli-Riga

Applied Composite Materials· Volume 10· Issue 3

Abstract

A classical cracked metallic structure, repaired with a ‘smart’ bonded composite patch with embedded optical fibers (to detect the strain field variations of the loaded structure), has been studied here-in. Finite element analysis was used, where-in the composite patch was modeled as a layered structure with three-dimensional elements constituting six different laminae. Each lamina is assumed to have different mechanical properties, according to the studied case, in order to simulate different stacking sequence. A resin rich ‘eye’ pocket has also been modeled in order to simulate the exact form of resin area produced during the manufacturing process. The patch is bonded over a cracked aluminum sheet through a small adhesive layer placed in between. External loads were applied only on the metal structure, as in a real repair case. The primary loading axis of the metal was assumed to be parallel to the direction of the optical fibers. Due to the different nature of the materials that form the composite patch, complex mechanical interactions between the fibers and the surrounding material occur, resulting in a complicated strain field along the optical fiber sensor. This affects the structural integrity of both the patch and the repair. Different optical fiber layer positions were considered, to study their effect on the resulting strain field and the structural integrity of the patch. Analysis concluded that the best available embedding position of an optical fiber in a laminated patch coincides to the one predicted as neutral surface, according to Rose's analytical equations.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Mechanical Behavior of Composites · Metal Forming Simulation Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (7)

Show source-provided references
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