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 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 strain sensors. The plate is impacted with a semi-spherical impactor with various impact energies using the drop-weight technique. The impacts did not produce apparent damage in the composite plates. The significant features of the strain and contact force response are contact duration, peak strain, strain rise-time and full-width at half maximum. 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. The load cell data is used to train a three-layer feedforward neural network which utilizes the back-propagation algorithm. The output of the neural network simulation is the impact contact force history and the inputs are fiber optic sensor data in two different locations and time in 10 microsecond 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.
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