Evaluation of Bonding Quality with Advanced Nondestructive Testing (NDT) and Data Fusion

This work aims to compare quantitatively different nondestructive testing (NDT) techniques and data fusion features for the evaluation of adhesive bonding quality. Adhesively bonded composite-epoxy single-lap joints have been investigated with advanced ultrasonic nondestructive testing and induction thermography. Bonded structures with artificial debonding defects in three different case studies have been investigated: debonding with release film inclusion, debonding with brass film-large, debonding with brass film-small. After completing preprocessing of the data for data fusion, the feature matrices, depending on the interface reflection peak-to-peak amplitude and the principal component analysis, have been extracted from ultrasonic and thermography inspection results, respectively. The obtained feature matrices have been used as the source in basic (average, difference, weighted average, Hadamard product) and statistical (Dempster–Shafer rule of combination) data fusion algorithms. The defect detection performances of advanced nondestructive testing techniques, in addition to data fusion algorithms have been evaluated quantitatively by receiver operating characteristics. In conclusion, it is shown that data fusion can increase the detectability of artificial debonding in single-lap joints.

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Failure of Adhesive-Bonded Composite Single Lap Joints with Embedded Cracks

AIAA Journal ◽

10.2514/2.385 ◽

1998 ◽

Vol 36 (3) ◽

pp. 448-456 ◽

Cited By ~ 35

Author(s):

L. Tong

Keyword(s):

Lap Joints ◽

Single Lap Joints ◽

Bonded Composite

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Forecast Customization System (FOCUS): A Multimodel Ensemble-Based Seasonal Climate Forecasting Tool for the Homogeneous Climate Zones of Myanmar

Advances in Meteorology ◽

10.1155/2019/4957127 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15

Author(s):

Itesh Dash ◽

Masahiko Nagai ◽

Indrajit Pal

Keyword(s):

Climate Models ◽

Global Climate ◽

Principal Component Regression ◽

Weighted Average ◽

Principal Component ◽

Global Climate Models ◽

Seasonal Rainfall ◽

Operating Characteristics ◽

Rainfall Forecast ◽

Probabilistic Forecasts

A Multi-Model Ensemble (MME) based seasonal rainfall forecast customization tool called FOCUS was developed for Myanmar in order to provide improved seasonal rainfall forecast to the country. The tool was developed using hindcast data from 7 Global Climate Models (GCMs) and observed rainfall data from 49 meteorological surface observatories for the period of 1982 to 2011 from the Department of Meteorology and Hydrology. Based on the homogeneity in terms of the rainfall received annually, the country was divided into six climatological zones. Three different operational MME techniques, namely, (a) arithmetic mean (AM-MME), (b) weighted average (WA-MME), and (c) supervised principal component regression (PCR-MME), were used and built-in to the tool developed. For this study, all 7 GCMs were initialized with forecast data of May month to predict the rainfall during June to September (JJAS) period, which is the predominant rainfall season for Myanmar. The predictability of raw GCMs, bias-corrected GCMs, and the MMEs was evaluated using RMSE, correlation coefficients, and standard deviations. The probabilistic forecasts for the terciles were also evaluated using the relative operating characteristics (ROC) scores, to quantify the uncertainty in the GCMs. The results suggested that MME forecasts have shown improved performance (RMSE = 1.29), compared to the raw individual models (ECMWF, which is comparatively better among the selected models) with RMSE = 4.4 and bias-corrected RMSE = 4.3, over Myanmar. Specifically, WA-MME (CC = 0.64) and PCR-MME (CC = 0.68) methods have shown significant improvement in the high rainfall (delta) zone compared with WA-MME (CC = 0.57) and PCR-MME (CC = 0.56) techniques for the southern zone. The PCR method suggests higher predictability skill for the upper tercile (ROC = 0.78) and lower tercile categories (ROC = 0.85) for the delta region and is less skillful over lower rainfall zones like dry zones with ROC = 0.6 and 0.63 for upper and lower terciles, respectively. The model is thus suggested to perform relatively well over the higher rainfall (Wet) zones compared to the lower (Dry) zone during the JJAS period.

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