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Carbon/Epoxy Laminate Compression After Impact Load Prediction from Ultrasonic C-Scan Data Eric v. K. Hill, Christopher D. Hess and Yi Zhao OBJECTIVES.

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Presentation on theme: "Carbon/Epoxy Laminate Compression After Impact Load Prediction from Ultrasonic C-Scan Data Eric v. K. Hill, Christopher D. Hess and Yi Zhao OBJECTIVES."— Presentation transcript:

1 Carbon/Epoxy Laminate Compression After Impact Load Prediction from Ultrasonic C-Scan Data Eric v. K. Hill, Christopher D. Hess and Yi Zhao OBJECTIVES Three sets of 3.5 x 6 inch 16-ply AS4/3501-5A carbon/epoxy coupons impacted from 0-20 ft-lb f 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:±15%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 worst case errors -12.12%, 16.62%, and -11.83% for the three setsUT 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 predict accurately without known impact energyBPNN able to predict accurately without known impact energy – valid for real world applications such as impact damaged aircraft wings C/Ep Coupon in Boeing BS-7260 Compression After Impact Test Fixture with Three Acoustic Emission Transducers Attached Instron Dynatup 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 16 Color Format 0-15 Color Format Digital Representation of 0-15 Color Format

3 Data Set SpecimenImpact Energy (ft-lb f ) Compressive Load (lb f ) Predicted Compressive Load (lb f ) % Error Training A20 2865.6 2865.600.00 A32.23 6531.9 6531.900.00 A521.43 3910.1 3910.100.00 A420.2 3042.4 3042.400.00 Testing A620.75 4174.8 4492.737.62 A10 4936.5 4338.07-12.12 BPNN Predictions for Batch A Coupons Optimized BPNN Settings Digital Ultrasonic C-Scan Image Data Predicted CAI Load NeuralWorks Professional II/PLUS ® Software Summary of BPNN Training and Test Results Worst Case Error


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