Prediction of Impact Contact Forces of Composite Plates Using Fiber Optic Sensors and Neural Networks


Real-time determination of contact forces due to impact on composite plates is necessary for on-line impact damage detection and identification. We demonstrate the use of fiber optic strain sensor data as inputs to a neural network to obtain contact force history. An experimental and theoretical study is conducted to determine the in-plane strains of a clamped graphite/epoxy composite plate upon low-velocity impacts using surface-mounted extrinsic Fabry-Perot interferometric (EFPI) strain sensors. The plate is impacted with a semispherical impactor with various impact energies using the drop-weight technique. The significant features of the strain and contact force response are contact duration, peak strain, and strain rise time. We have designed and built an instrumented drop-weight impact tower to facilitate the measurement of contact force during an impact event. The impact head assembly incorporates a load cell to measure the contact forces experimentally. An in-house finite-element program is used to establish the validity of the EFPI fiber optic sensor contact force response. The finite-element model is based on a higher-order shear deformation theory and accounts for geometric nonlinearity. Experimental load cell data and finite-element impact-induced contact force responses are in close agreement. The load cell data is used to train a three-layer feed-forward neural network which utilizes the Delta Bar Delta back-propagation algorithm. The output of the neural network simulation is the contact force history and the inputs are fiber optic sensor data in two different locations and time in 10-ms intervals. The efficiency and accuracy of the neural network method is discussed. The neural network scheme recovers the impact contact forces without using any complex signal processing techniques.


Electrical and Computer Engineering

Second Department

Mechanical and Aerospace Engineering

Document Type

Article - Journal

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