Long Term Durability of Structural Materials PJ.M. Monteiro, K.P. Chong, J. Larsen-Basse, K. Komvopoulos (Eds) 35 © 2001 Elsevier Science Ltd. All rights reserved

DEVELOPMENT OF AN INTELLIGENT STRUCTURAL DAMAGE ASSESSMENT SYSTEM: PRELIMINARY RESULTS

R.M.V. Pidaparti and MJ. Palakai

^Dg)artment of Mechamcal Engineering Department of Computer Science

Indiana University Purdue University Indianapolis 723 W. Michigan Street

Indianapolis, Indiana 46202-5132

ABSTRACT

The overall goal of this project is to develop a structural damage assessment system to quantify the damage due to different sources in aging structures, estimate the severity of the quantified damage, and integrate the developments into an intelligent system so that it can be used to empirically predict fatigue failure and fatigue life of aging materials and structures. The proposed system will provide a fatigue "safety index" to assess the long-term durability and size effects on aging structures. A multi-disciplinary approach consisting of materials, damage/fracture mechanics, artificial intelligence, computer vision, pattern recognition techniques, and engineering optimization is being pursued to quantification and prediction of damage in aging structures. The intelligent system and the associated developments are validated through a series of carefully selected problems from aging aircraft structures. This paper discusses some of the developments up to date and the progress of the proposed intelligent structural damage assessment system.

KEYWORDS

Structural damage assessment, Corrosion, Artificial neural networks. Image processing. Signal analysis, Wavelet analysis.

INTRODUCTION

Structural damage quantification and estimating its severity is needed in many aging structures in aerospace engineering (aircraft wings, fuselages, rotating and manufacturing machinery) and civil engineering structures (bridges, building, pressure vessels). The damage may be due to fatigue, corrosion and/or wear of materials resulting from operating conditions and the environment. Some of tiie major problems of aging militaiy and commercial aircraft include, for example, in-service

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cracking of the aircraft wing upper surface, widespread fatigue damage of the various structural components, uncertainty in variable amplitude loading and overload effects of aircrafls, discrete source damage induced by foreign objects, and repairs of metallic components with composite counterparts to extend the service life.

Given the modem day requirements for extaiding fatigue life, maintenance personnel are required to inspect and ensure the safety of the structures. Periodic inspections of critical areas using appropriate non-destructive evaluation (NDE) techniques are carried out for ensuring safety. The inspection intervals are calculated based on damage tolerance predictions of crack-growth for aircraft and rotorcraft structural components (Bates, 1995). Structural integrity prediction tools are needed to estimate the severity of the damage in many aging structures in aerospace engineering (aircraft wings and fiiselage) as well as in civil engineering (bridges, buildings and pressure vessels). The current study deals with the fatigue damage predictions in aging aircraft structures. Recently, Pidaparti et. al. (2000) developed a structural integrity simulation program for aging aircraft panels in Matlab environment.

The long-term durability assessment of structures should involve NDI/NDE techniques integrated with prediction methods for in-situ tests and validation. However, current approaches do not attempt to integrate both these methodologies. Our focus therefore, is to develop such an integrated system which will provide capabilities for reliable damage assessment and prediction using existing NDE techniques. Such a system will result in reduced maintenance and lower cost.

OVERVIEW OF THE INTELLIGENT STRUCTURAL DAMAGE ASSESSMENT SYSTEM

The approach proposed in this research attempts to quantify the damage due to different sources in aging structures, estimate the severity of the quantified damage, and integrate the developments into an intelligent system so that it can be used to empirically predict fatigue failure and fatigue life of aging matCTials and structures. The objective is to develop a fatigue "safety index" using the intelligent system to assess long-term durability and size effects on aging structures. The intelligent system and the associated developments will be validated through a series of carefiilly selected problems for which other alternate or experimental solutions are available in the literature.

Figure 1 shows the organization of the Intelligent Structural Damage Assessment System (ISDAS). The development of the system involves interfacing an NDI system with a database, quantification and classification of damage, estimation of the severity of the quantified damage and prediction of the safety index in terms of fatigue life and residual strength. As shown in Fig. 1, the ISDAS system consists of five major components: (i) a database that manages information obtained fi-om various NDI systems; (ii) modules for damage quantification and classification using computer vision and pattem recognition techniques; (iii) an intelligent learning system based on artificial neural networks and fuzzy logic for severity estunation; (iv) an integrated decision maker using expert system methodologies to report the safety index; and (v) a graphical user interface which allows the users to interact with the system. The outcome fi:om this intelligent system will be a safety index which reflects the long-term deterioration of the structure. The intelligent system and the associated developments are being tested and validated through a series of carefully selected sample problems in aging aircraft structures. Details of the two specific modules in ISDAS, damage classification/quantification and severity estimation, are described below.

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f GRAPHICAL

USER INTERFACE

,

" ^

-J

SAFETY INDEX INTEGRATED 1

DECISION MAKER 1

NDE SYSTEM DAMAGE QUANTIFICATION

& CLASSIFICATION SYSTEM

SEVERITY ESTIMATION LEARNING SYSTEM

Figure 1: Overview of the Intelligent Structural Damage Assessment System (ISDAS)

Damage Quantification and Classification Module

Imaging has become an increasingly important tool to enhance detection and characterization of damage from the existing NDI techniques (acoustic imaging, infrared imaging, eddy current imaging, impedance imaging and X-ray radiography). Images obtained using NDI techniques can be effectively used to assess the damage more accurately than conventional methods. Image analysis-based techniques are developed for the identification and quantification of corrosion damages.

The overall process of identification and quantification of corroded regions from NDI images is shown in Figure 2. The process essentially involves two stages: first, classification of various regions in the image as corroded or uncorroded, and second, prediction of the material loss of the corroded regions.

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Input Image

A

Wavelet Transforms Energy Operator

K-Means Learning

Features from each segment

<

Region Damage Identification Cluster Formation and

Segmentation

Predicted Material

Loss

Feature Extractor Artificial Neural

Network

Figure 2: The Damage Analysis and Quantification Process

The classification process involves segmenting the image into various regions. Multi-resolution wavelet analysis is performed on the NDI images to obtain a set of wavelet coefficients as feature vectors. These features were used for the identification of the damaged regions on the NDI images using clustering techniques. Each of the segments on the segmented image would correspond to a damaged region or an undamaged region as shown in Figure 3. Some of the recent results on segmentation algorithms are reported in Rebbapragda et. al. (1999).

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Figure 3: Segmented and classified regions of a damaged panel

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Once the damaged segments are identified, first-order and second-order features are extracted firom each identified segment. First order statistical features are computed using the histogram of the NDI images. These include meariy standard deviation, skew, energy, and entropy. The second order features such as angular second moment, inverse second moment, entropy, and contrast are calculated using a co-occurance matrix. The co-occurance matrix is an estimate of the second order joint probability density. A back-propagation neural network is then used to quantify the damage. Neural networks are capable of realizing a variety of non-linear relationships of considerable complexity and are effectively used in this research. Figure 4 shows results of using different number features for predicting the material loss for the same specimen and Figure 5 shows the material loss predicted by the neural network compared with experimental data. It can be seen from Figure 4 that 15 features were sufficient for the neural network to generalize and predict material loss fairly accurate. The quantification of damage is based on the extent of material loss. For further results on material loss prediction, see Palakal et. al. (2000).

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92S 92T 92U 92V

Specimen ID

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A 15 features

X 27 features

Figure 4: Comparison of different numbers features used for predicting material loss

. 15 (0

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92P 92Q 92R 92S 92T 92U 92V 92W 92X 92Y

Specimen ID

Figure 5: Neural network prediction of material loss compared with experimental data

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Severity Analysis and Estimation Module

Severity of the damage assessment is based on various factors such as the quantitative value of the damage, the area where it occurred and other peripheral information. The severity of the damage will be estimated through a learning and prediction model that is based on artificial neural networks and fuzzy logic. During the learning phase, the models learn to predict various properties such as fatigue life, material property, residual strength, and crack growth. As an example, the residual strength and corrosion rate predictions of aging aircraft panels is presented.

/ P a n e l Width f Hole Diameter

Hole Spacing

/ X \ .

Center cradc length / \ Avg. MSD crack laigtiij Yidd strcngtii Number of holes wifli \ panel ' MSD \Thickness / Percentage material V n / MSD Percentage material loss

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Ky

w

sz laputs I

\ \ Material type

Environment type Temperature Duration of exposure

, ^ ^ ^ 1 1 ^ — ^ * ' ^ j

Residual strengtii

Corrosion rate ^ ASTMG34

rating

Figure 6: Neural network model to predict corrosion and residual strength behavior

A neural network model is developed for predicting the residual strength Mid corrosion parameters of MSD panels of aging aircraft. A multi-layer, feed-forward neural network with back-propagation learning algorithm was used in this study. Figure 6 shows the parameters affecting the corrosion behavior and residual strength of MSD panels. A total of 13 parameters were used to model both the phenomena. All the parameters except material type designator and corrosion environment are continuous variables. Material type designator can take integer values from 1 to 4 depending on whether the material belongs to the 2xxx, 3xxx, 6xxx or 7xxx series of Aircraft Aluminum, respectively. Similarly, the corrosion environment can take integer vedues from 1 to 5, depending on the type of environment.

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Table 1 presents the predictions from the different analytical models and the neural network models along with the experimental data (Sivam & Ochoa, 1999; Sheuring & Grant, 1995; Moukawsher, et. al., 1996; Smith et. al, 2000; Luzar, 1998) for the testing set. Although, the analytical methods predict better than the neural network model for a few panels, overall the predictions from neural network are consistently close to the experimental data. The neural network is able to predict the corrosion rate and the ASTM rating for the panels, fairly well. Figure 7 compare the corrosion rate and the ASTM rating predicted for the panels in the training set, with the experimental data. As observed from these figures, the network model captures the corrosion phenomena fairly accurately.

TABLE 1 PREDICTION OF RESIDUAL STRENGTH FOR TESTING SET FROM VARIOUS METHODS

Reference Specimen

ID

Experimental strength

Neural Netwark

model (kN)

Net Section yield method

(kN)

Swift's Linkup load (kN)

Luzar Luzar

Moidcawsher Moukawsher

SmiUi Smith Smitiii Sivam Sivam

2024-T3 7075-T6 RS-Ola RS-04C WSU12 WSU19 WSU26

10 17

112.23 92.73 156.72 160.94 112.18 94.94 83.70 27.59 20.06

109.11 86.22 173.29 159.58 107.65 92.54 76.37 30.00 16.77

123.52 325.74 115.99 93.76 158.62 141.80 124.99 28.83 21.80

69.78 143.96 116.05 93.12 114.04 87.15 85.42

--

ASTM G34 Rating Corrosion rate

0 0.2 0.4 0.6 0.8 1 1.2

Experimental data

0 .0021

0 0.0005 0.001 0.0015 0.002

E)q?erimental data (cm/cm -days)

Figure 7: Comparison of neural network results of corrosion rate and rating against experimental data for the training set

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The Damage Assessment Model

The damage assessment model is based on an optimization process in which different networks, analytical model and experimental data will interact in a dynamic process to obtain the key parameters for developing the safety index. The optimization model tries to minimize the total energy in the system with physical constraints based on mechanical behavior of the material and physics, similar to the approach by Pidaparti & Palakal (1998). The outcome from the damage assessment model will be corrosion rate, fatigue crack growth behavior and residual strength. These two parameters along with other uncertainties will be combined to obtain the safety index. Currently, this aspect of the research is being carried out and the results will be reported in the future.

SUMMARY

An intelligent structural damage assessment system (ISDAS) is being developed for the purpose of estimating the structural integrity of aging aircraft panels with damage. The ISDAS program uses analytical/neural network solutions to predict the residual strength, fatigue crack-initiation, fatigue crack-growth, and fatigue life based on several user defined failure criteria. The framework of the ISDAS program is designed such that it is user friendly and has limited graphics capabilities. The developed system is iosiQd against the experimental and analytical data and preliminary results were found to be in good agreement. Currently, this system is being extended to include an optimization method to determine the safety index of an aged structure. The overall software system is written in JAVA environment and can be easily portable.

Acknowledgements

The authors thank the National Science Foundation for supporting this work through a grant CMS-9812723 with Dr. Ken Chong as the Program Manager. The authors thank Dr. Jones of FAA/NDI Validation Center, Dr. Peeler of AFRL, Dayton, Ohio, and Dr. Sivam of Raytheon Systems, Texas. Thanks also due to Mr. Rebbapragada, Mr. Jayanti, and Dr. Q. Wang for their contributions.

References

Bates P.R. (1995). Technical Considerations for Managing Aging Rotorcraft. ASME Structural Integrity in Aging Aircraft 47:1, 21-34.

Koch G.H. (1995). On the mechanisms of interaction between corrosion and fatigue cracking in Aircraft Aluminum alloys. Structural Integrity of Aging Aircraft y Chang C.I. & Sun C.T. (eds), American Society of Mechanical Engineers 47, 159-169.

Luzar J. (1998). Pre-corroded fastener hole multiple site damage testing. Boeing Technical Report EA 96-135, 1-46.

Moukawsher E.J., Heinimann M.B., and Grandt Jr. A.F. (1996). Residual Strength of Panels with Multiple Site Damage. Journal of Aircraft 33: 5,1014-1021.

Palakal M.J., Pidaparti R.M. and Rebbapragada S. (2000). Intelligent Computational Methods for Corrosion Damage Assessment. AIAA Journal, (under review).

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Pidaparti R.M., Palakal M.J. and Rahman Z.A. (2000). Simulation of Structural Integrity Predictions for Panels with Multiple Site Damage. Advances in Engineering Software 31,127-135.

Pidaparti R.M. and Palakal M.J. (1998). Fatigue Crack-growth Predictions in Aging Aircraft Panels using Optimization Neural Network. AIAA Journal 36:7,1300-1304.

Rebbapragada S., Palakal M.J., Pidaparti R.M. and Jones C.R. (1999). Corrosion detection and quantification using image processing for aging aircraft panels. Third Joint FAA/DoD/NASA Conference on Aging Aircraft, Albuquerque, New Mexico.

Sheuring J.N. and Grandt A.F. Jn (1995). An evaluation of Aging Material Properties. Structural Integrity of Aging Aircraft, Chang C.I. & Sun C.T. (eds), American Society of Mechanical Engineers 47,99-110.

Sivam T.P. and Ochoa CM. (1999). Aircraft Corrosion inspection and evaluation technique using ultrasonic scanning methods. Second Joint FAA/DoD/NASA Conference on Aging Aircraft, Williamsburg, Virginia.

Smith B.L., SaviUe P.A., Mouak A., and Myose R.Y. (2000). Strength of 2024-T3 Aluminum Panels with Multiple Site Damage. Journal of Aircraft 37:2, 325-331.