Damage tolerance modeling and validation of a wireless sensory composite panel for a structural health monitoring system

Abstract

The paper proposes the diagnostic and prognostic modeling and test validation of a Wireless Integrated Strain Monitoring and Simulation System (WISMOS). The effort verifies a hardware and web based software tool that is able to evaluate and optimize sensorized aerospace composite structures for the purpose of Structural Health Monitoring (SHM). The tool is an extension of an existing suite of an SHM system, based on a diagnostic-prognostic system (DPS) methodology. The goal of the extended SHM-DPS is to apply multi-scale nonlinear physics-based Progressive Failure analyses to the “as-is” structural configuration to determine residual strength, remaining service life, and future inspection intervals and maintenance procedures. The DPS solution meets the JTI Green Regional Aircraft (GRA) goals towards low weight, durable and reliable commercial aircraft. It will take advantage of the currently developed methodologies within the European Clean sky JTI project WISMOS, with the capability to transmit, store and process strain data from a network of wireless sensors (e.g. strain gages, FBGA) and utilize a DPS-based methodology, based on multi scale progressive failure analysis (MS-PFA), to determine structural health and to advice with respect to condition based inspection and maintenance. As part of the validation of the Diagnostic and prognostic system, Carbon/Epoxy ASTM coupons were fabricated and tested to extract the mechanical properties. Subsequently two composite stiffened panels were manufactured, instrumented and tested under compressive loading: 1) an undamaged stiffened buckling panel; and 2) a damaged stiffened buckling panel including an initial diamond cut. Next numerical Finite element models of the two panels were developed and analyzed under test conditions using Multi-Scale Progressive Failure Analysis (an extension of FEM) to evaluate the damage/fracture evolution process, as well as the identification of contributing failure modes. The comparisons between predictions and test results were within 10% accuracy.