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Carbon/Epoxy Laminate Compression After Impact Load Prediction from Ultrasonic C-Scan Data Eric v. K. Hill, Christophe

Carbon/Epoxy Laminate Compression After Impact Load Prediction from Ultrasonic C-Scan Data Eric v. K. Hill, Christopher D. Hess and Yi Zhao. C/ Ep Coupon in Boeing BS-7260 Compression After Impact Test Fixture with Three Acoustic Emission Transducers Attached. OBJECTIVES

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Carbon/Epoxy Laminate Compression After Impact Load Prediction from Ultrasonic C-Scan Data Eric v. K. Hill, Christophe

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  1. Carbon/Epoxy Laminate Compression After Impact Load Prediction from Ultrasonic C-Scan Data Eric v. K. Hill, Christopher D. Hess and Yi Zhao C/Ep Coupon in Boeing BS-7260 Compression After Impact Test Fixture with Three Acoustic Emission Transducers Attached • OBJECTIVES • Three sets of 3.5 x 6 inch 16-ply AS4/3501-5A carbon/epoxy coupons impacted from 0-20 ft-lbf with 5/8 inch diameter hemispherical tup to create barely visible impact damage (BVID) • Back-propagation neural network (BPNN) prediction of compression after impact (CAI) load from transformed ultrasonic (UT) C-scan image • Goal: Worst case prediction error within ±15% • APPROACH/TECHNICAL CHALLENGES • AE data too noisy: Train BPNN using 50 data points representing column summation data from UT C-scan image and known CAI loads as input • Test BPNN using column summation UT C-scan image to predict CAI loads on remaining coupons • ACCOMPLISHMENTS/RESULTS • UT image data alone used to predict ultimate compressive strengths with worst case errors of -12.12%, 16.62%, and -11.83% for the three sets • BPNN able to predict accurately without known impact energy– valid for real world applications such as impact damaged aircraft wings InstronDynatup 9250 Calibrated Impactor Delaminations in Coupon Due to Impact Damage

  2. MATLAB Data Transformation • Pixel color and location is represented by a matrix array of numbers (0-16) • Numerical values represent hue color • Image data summed and normalized in the column direction • 50-100 data points surrounding the maximum used as inputs to BPNN • UltraPAC II C-Scan Imaging System: • Water Couplant Immersion • 5 MHz Unfocused Transducer 0-15 Color Format Digital Representation of 0-15 Color Format 16 Color Format

  3. BPNN Predictions for “Batch A” Coupons Summary of BPNN Training and Test Results Worst Case Error Optimized BPNN Settings Predicted CAI Load Digital Ultrasonic C-Scan Image Data NeuralWorks Professional II/PLUS® Software

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