Health Monitoring of Composite Structures Using

51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
18th 12 - 15 April 2010, Orlando, Florida

AIAA 2010-3105

Health Monitoring of Composite Structures Using Advanced Diagnostic Systems Galib Abumeri1, Florent Rognin2 and Frank Abdi3 Alpha STAR Corporation, Long Beach, CA, 90804 and Ayman Mosallam4, Mohamed Salama5 and Rashid Miraj6 University Of California, Irvine, CA, 92697 Alpha STAR Corporation and the University of California (UCI), under US Army contract, devised a comprehensive program of innovative Diagnostic Prognostic System (DPS) and a structural evaluation of Composite Army Bridge (CAB) system. The approach requires the application of physics based durability and damage tolerance to track damage and fracture numerically while monitoring and streaming strain response. The process starts with a simplistic micro-mechanics based computational simulation to assess composite material behavior. The methodology reverse engineers fiber, matrix and interface constituent properties by iteratively solving micro-mechanics equations using un-notched laminate test data as input. Unnotched longitudinal tension (LT), longitudinal compressive (LC), transverse tension (TT), transverse compression (TC), and in-plane shear (IPS) ASTM tests are simulated with finite element-based Progressive Failure Analysis (PFA). The reverse engineering/calibration approach determines the constituents’ linear and non-linear properties. The calibrated properties can then be used with confidence to evaluate the performance of any structure made from the same composite material system. This technical approach was used to characterize the constituents for biaxial and triaxial carbon-based composite material architectures. The materials were representative of a composite Army bridge beam structure subjected to 4-point bending load. Use of the calibrated constituent properties opened the way for successful a priori prediction of the bridge structure experimental behavior. The progressive failure analysis of the bridge structure, using the calibrated composite constituent properties was in very good agreement with those from experimental test results (less than 8% difference). Results from the DPS for strain measurements compared well with strain data predicted by the simulation.

Nomenclature Ef11 Ef22 Em Gm ν k

= = = = = = =

fiber longitudinal modulus fiber transverse modulus matrix normal modulus matrix shear modulus Poisson’s ratio volume fraction coefficient of thermal expansion

C = compressive T = tension S = Shear f = fiber m = matrix l = ply ν = voids

Subscripts 1 = along fiber direction 2 = transverse to fiber direction 3 = normal to fiber direction

1. Introduction Composite materials have found many structural applications due to their lightweight, relative low-cost, and the evolution of automated composite material/structure fabrication processes. Composite materials are being effectively employed in the manufacture of aircraft, automobiles, transportation systems, infrastructure and power 1

Program Manager, Alpha STAR Corp., Senior Member AIAA. Research Engineer, Alpha STAR Corp. Senior Scientist, Alpha STAR Corp., Senior Member AIAA 4 Professor & Director of Structural Engineering Testing, University of California Irvine 5 Graduate Student, University of California Irvine. 6 Lab Manager Structural Engineering Testing,, University of California Irvine. 2 3

1 American Institute of Aeronautics and Astronautics Copyright © 2010 by Alpha STAR Corp. . Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

plants. This trend is intensifying with the development of new constituent materials and fiber reinforcement configurations. In particular, fiber reinforced composites are increasingly more cost-effective for applications in aircraft fuselage, wing, and tail structures, engine fan blade air-breathing and first stage compressor components and blade containment structures. The safety and reliability of composite systems are dependent on the composite constituents, their configuration and application in a system design. These systems are often subjected to complex service loading conditions, in which two or three dynamic or static principal stresses may exist. The DPS is founded on the integration of three technologies, namely: Diagnostic, prognostic, and Intelligent repair system. Diagnostic system includes: 1) optical fiber sensing, low cost ZigBee Wire less strain gage, and wired strain gage, remote data transmission; 2) Prognostic system utilizes - virtual testing, progressive failure analysis, that determines the failure location, failure cycle/load, and the contributing failure mechanisms. In developing this system, both laboratory and virtual test were used in evaluating different potential damage scenarios. Health monitoring of a composite beam with DPS entailed comparing live strain data to archived strained data in various bridge locations; And 3) a temporary field repairs - protocol procedure is provided subjected to virtual testing data, that determines the severity of damage, the loss in ultimate strength, repair material use, and the bridge operability. As such, composite bridge Beam specimen will be showcased for their strengths, heralded the viability of virtual testing, highlighted the efficacy of field repair, and confirmed the merits of health monitoring. Utilization of advanced composite materials to their full potential requires accurate establishment of effective material mechanical properties including stiffness and strength. It is important to reliably estimate the effect of environment and service on the material response. Other issues that need to be addressed are: (1) variability in processing from one manufacture to the other for the same class of material, (2) effect of process variability on the design, (3) effect of manufacturing defects, and (4) effect of “as-built” versus “as-designed” on use and life of the composite part. Strength design limits are traditionally based on lamina properties which may result in overly conservative design. Physical damage in composites takes place in the fiber, matrix and/or interface region and is load and layup dependent. Modeling of fiber, matrix and interface using finite elements is a cumbersome task [1] requiring large engineering and computational resources. Full-scale Finite Element Analysis (FEA) of constituents is impractical as it is complicated by the inclusion of manufacturing defects such as void shape, size and location. Therefore, the benefits of using physics-based but simplistic multi-scale micro-mechanics approach to assess structural behavior of laminated composites need to be evaluated. In this paper, such an approach is presented and validated by simulating un-notched ASTM tests of biaxial and triaxial carbon-based composite material architectures. These materials are used in the construction of a demonstrator Army bridge subjected to 4-point bending load.

Figure 1. Computational Micro-Mechanics Procedure for Composite

2. Analytical Approach A. Micro-Mechanics Composite Analysis Figure 1, shows the composite micro-mechanics strategy used to compute the physical properties of a composite material system with 1-D, 2-D or 3-D fiber architectures. Physical properties of each type of reinforcement (e.g.

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filler, warp and/or through-thickness fiber) are separated into material directions based on fiber angles and contents. They are then combined with matrix properties and/or void contents to create composite unit cell properties. The modeled composite properties include directional stiffness, Poisson's ratio, strength, coefficients of thermal and hygral expansion, heat conductivity and moisture diffusivity. The composite properties are determined from the composite configuration information, i.e. fiber and matrix properties, the fiber architecture and content, and the manufacturing defect content using the micro-mechanics formulations developed at NASA [2]. Table 1 lists the micro-mechanics relationships used to compute composite properties such as density, stiffness, and thermal expansion coefficients. The overall structural stiffness is formed following classical finite element theory.

Table 1. Micro-Mechanics Based Formulation for Properties of Taped Composite Laminates

B. Progressive Failure Analysis of Composite Structures The micro-mechanics composite analysis is integrated with finite element analysis and damage and fracture tracking to perform Progressive Failure Analysis (PFA). The capability is integrated in the GENOA software system [3, 4, and 5]. Traditionally, failure is assessed at the lamina or laminate scale. This assessment is inconsistent with the physical behavior of the composite material since damage occurs at a lower scale, that is the fiber, matrix or interface level. The methodology presented in this paper augments FEA analysis, with a full-hierarchical modeling that goes down to the micro-scale of sub-divided unit cells composed of fiber bundles and their surrounding matrix.The strategy for progressive failure FEA-based analysis is presented in Figure 2. The stresses and strain at micro level are calculated using a mechanics-of-material-approach from the finite element analysis results of the macro-mechanical analysis at each load increment. Displacements, stress and strains derived from the structural scale FEA solution at a node or element of the finite element model are passed to the laminate and lamina scales using laminate theory. Stresses and strains at the micro-scale are derived from the lamina scale using micro-stress theory. The latter are interrogated for damage using a set of failure criteria (Table 2). This analysis is performed progressively, enabling the analysis of damage initiation and progression including fracture initiation on a micro level. The interface evaluates damage and failure mechanics caused by matrix and fiber failure. The damage mechanisms account for matrix cracking under transverse, compressive, and shear loading. The ply fracture mechanisms include fiber failure under tension, compression (crushing, micro-buckling and de-bonding), and delamination. It allows: (1) use of commercial finite element stress solvers; (2) user selection of 2 or 3-D architectural details (through-thethickness fibers, resin rich interphase layer between weave plies, fiber volume ratio, void shape, size and location, cure condition, etc.); (3) assigning static (thermo-mechanical) or spectrum loading; (4) automatic update of the finite element model prior to executing FEA stress solver for accurate lamina and laminate properties; and (5) degradation of material properties at increase loading (including number of cycles) based on detected damage. The applicability

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of micro-mechanics based PFA to simulate the test behavior of ASTM specimens made from laminated composites is presented in the next section. 3D Fiber

2D Woven Vehicle

Component FEM

Laminate

Traditional FEM stops here GENOA goes down to micro-scale

Micro-Scale FEM results carried down to micro scale Sliced unit cell Reduced properties propagated up to vehicle scale

(a) Interface with FEA Tool for Failure Prediction

Lamina

Unit node Unitcell cellatat each element

(b) Micro-macro scale interaction in GENOA progressive failure analysis

Figure 2. Strategy for FEA-based Progressive Failure Analysis Table 2. Fiber and Matrix Failure Criteria Applied at the Micro–Scale Level of the Composite Mode of Failure 1.

Longitudinal Tensile (S11T)

2.

Longitudinal Compressive (S11C)

3. 4. 5. 6. 7. 8.

Transverse Tensile (S22T) Transverse Compressive (S22C) Normal Tensile (S33T) Normal Compressive (S33C) In Plane Shear (+) (S12S) In Plane Shear (-) (S12S)

9.

Transverse Normal Shear (+) (S23S)

Description

10. Transverse Normal Shear (-) (S23S)

Fiber tensile strength and the fiber volume ratio. 1) Rule of mixtures based on fiber compressive strength and fiber volume ratio 2) Fiber micro-buckling based on matrix shear modulus and fiber volume ratio 3) Compressive shear failure or kink band formation, which is mainly based on ply intra-laminar shear strength and matrix tensile strength. Matrix modulus, matrix tensile strength, and fiber volume ratio. Matrix compressive strength, matrix modulus, and fiber volume ratio. Plies are separating due to normal tension Due to very high surface pressure i.e. crushing of laminate Failure due to Positive in plane shear with reference to laminate coordinates Failure due to negative in plane shear with reference to laminate coordinates Shear Failure due shear stress acting on transverse cross section that is taken on a transverse cross section oriented in a normal direction of the ply Shear Failure due shear stress acting on transverse cross section that is taken on a negative transverse cross section oriented in a normal direction of the ply

11. Longitudinal Normal Shear (+) (S13S)

Shear Failure due shear stress acting on longitudinal cross section that is taken on a positive longitudinal cross section oriented in a normal direction of the ply

12. Longitudinal Normal Shear (-) (S13S)

Shear Failure due shear stress acting on longitudinal cross section that is taken on a negative longitudinal cross section oriented in a normal direction of the ply

13. Relative Rotation Criterion

Considers failure if the adjacent plies rotate excessively with respect to one another

3. Results and Discussion A. Calibration of Constituent (Bulk) Material Properties for Army Bridge Structure The fiber and matrix constituents of biaxial and triaxial composites were calibrated through reverse engineering for use in durability and damage tolerance evaluation of a composite Army bridge structure. The calibrated fiber and matrix properties reproduced five basic in-plane ASTM tests: longitudinal tension (LT), longitudinal compressive (LC), transverse tension (TT), transverse compression (TC), and in-plane shear (IPS). The PFA approach was

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successfully used to simulate the response of a bridge structure made from foam core and biaxial/triaxial composite material systems. The approach effectively reproduced the average test stiffness and strength properties of the bridge structural elements. The maximum difference between test and simulation strength was under 8%. The effective fiber and matrix properties, obtained as a result of the calibration process, were used directly in the composite Army bridge structure evaluation. Figure 3 depicts a cross-sectional view of the bridge structure along with the material usage breakdown per component. CRBBEXP and CARBEPOX are PFA labels to identify the combinations of fibers and matrices used. Each keyword is associated with specific material properties in the material data bank for use as input to progressive failure analysis.

Figure 3. Cross-section of Composite Army Bridge Structure Showing Material Usage Starting with known fiber and matrix properties from literature, material database and/or vendor data, the constituent properties (Table 3) were adjusted as required (within ± 20%) to derive a set of calibrated fiber and matrix properties. This unique set of properties is used in PFA to reproduce the stress-strain curves obtained the five basic un-notched unidirectional ASTM tests (Figure 4). The calibration relies on a reverse optimization process to determine an effective matrix stress-strain curve that includes non-linear effects (in case the five in-plane tests show non-linear behavior).

Table 3. Fiber and Matrix Constituent Properties Considered in Calibration of ASTM Tests Fiber Property Longitudinal Modulus 11 (psi) Transverse Modulus 22 (psi) Poisson’s Ratio 12 Poisson’s Ratio 23 Shear Modulus 12(psi) Shear Modulus 23(psi) Coefficient of Thermal Expansion 11 (in/in/F) Coefficient of Thermal Expansion 22 (in/in/F) Tensile Strength 11 (psi) Compressive Strength 11 (psi)

Matrix Property Normal Modulus (psi) Poisson's Ratio Coefficient of Thermal Expansion (in/in/F) Tensile Strength (psi) Compressive Strength (psi) Shear Strength (psi)

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Figure 4. Five Un-Notched Uniaxial Coupon Tests for Fiber / Matrix Calibration The simulation uses: 1) Material Characterization Analysis (MCA) to determine ply and composite properties. 2) Progressive failure analysis methodology (PFA) to replicate the coupon test data. The code predicts the stiffness, strength, Poisson’s ratio and strength of the lamina and laminates. 3) Material Uncertainty Analysis (MUA) to identify the effect of scatter in composite fiber/matrix material properties, manufacturing variables, environment, and loading on the on lamina and laminate response. The authors recommend five replicas per test to capture the probabilistic nature of composite material response.

Table 4. Comparison of Biaxial Material System Ply Properties - ASTM Tests vs. Analysis Property Description Longitudinal Modulus Transverse Modulus Out-of-Plane Modulus In-Plane Shear Modulus Longitudinal Out-of-Plane Shear Modulus Transverse Out-of-Plane Shear Modulus Longitudinal Tensile Strength Longitudinal Compressive Strength Transverse Tensile Strength Transverse Compressive Strength In-Plane Shear Strength

Symbol and Units*

Test

PFA Simulation

E11 (msi) E22 (msi) E33 (msi)

9.09 9.09 -

9.05 9.05 1.41

G12 (msi)

0.95

0.614

G13 (msi) G23 (msi) S11T (psi) S11C (psi) S22T (psi) S22C (psi) S12 (psi)

97,840 48,000 89,650 43,520 9,570

0.493 0.493 93,147 45,839 93,606 45,999 9,562

% Difference wrt Test -0.4% -0.4% -35.4% (outlier) -4.8% -4.5% 4.4% 5.7% -0.1%

1 psi= 6.894 kPa, 1 msi = 6894757 kPa B. Results for Biaxial Material System The biaxial material system is composed of four layers of carbon fiber and epoxy matrix. Each layer is modeled with 0/90 plies to simulate the biaxial architectures. The biaxial material system has a 53% fiber volume ratio and a 2% void volume. For each of the five ASTM tests, the averages of the replicate test results are presented in Table 4. The maximum difference between test and PFA analytical predictions is under 5.7% for all the properties except the shear modulus. The shear stiffness obtained from test is much higher than what is normally seen in similar materials. Therefore, the shear stiffness was calibrated based on an expected behavior for this type of material and architecture. The reversed engineered biaxial material system fiber and matrix properties are presented in Table 5. These properties were used in the subsequent durability and damage tolerance evaluation of the bridge.

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Table 5. Biaxial Material System Calibrated Fiber/Matrix Properties Reproducing ASTM Tests Fiber Property

Matrix Property

Longitudinal Modulus 11 (msi)

32.0

Normal Modulus (msi)

0.500

Transverse Modulus 22 (msi)

2.50

Poisson's Ratio

0.350

Poisson’s Ratio 12

0.200

Tensile Strength (ksi)

9.84

Poisson’s Ratio 23 Shear Modulus 12(msi) Shear Modulus 23(msi) Tensile Strength 11 (ksi) Compressive Strength 11 (ksi)

0.250 6.00 6.00 340 120

Compressive Strength (ksi) Shear Strength (ksi)

27. 5 15.9

C. Results for Triaxial Material System Fiber and matrix properties for the triaxial material system were calibrated using the same process as the one used in the biaxial material system. In-plane ASTM tests were simulated using the calibrated constituent properties. Figure 5 shows the architecture of the triaxial composite material system. The laminate consisted of 66.7% ± 45° plies and 33.3% 90° plies. The material was made from 52% fiber volume content and 2% void volume content. Table 6 shows the laminate strength and stiffness obtained from the five basic ASTM tests (LT, LC, TT, TC, and IPS).

Figure 5. C-TTX 3600 Triaxial Composite System Architecture used in the Composite Army Bridge The maximum difference between the test and the simulation results is 7.89% while the minimum difference was as low as 0.3%. The calibrated fiber and matrix properties are presented in Table 7. These properties were used in the subsequent durability and damage tolerance evaluation of the bridge. The accurate simulation of the ASTM tests established confidence in the progressive failure analysis capability and the calibrated fiber/matrix properties to evaluate the D&DT of the Army bridge structure. Figure 6a shows the derived non-linear matrix stress-strain curve while Figure 6b shows the simulation of the in-plane shear (IPS) test using the matrix stress-strain curve as input to the PFA analysis. The matrix stress-strain curve is fitted as the IPS test is being reproduced.

Table 6. Comparison of Triaxial Material System Ply Properties - ASTM Tests vs. Analysis Property Description Longitudinal Modulus Transverse Modulus In-plane Shear Modulus Longitudinal Tensile Strength Longitudinal Compressive Strength Transverse Tensile Strength Transverse Compressive Strength

Symbol and Units

Test

E11 (Msi) E22 (Msi) G12 (Msi) S11T (psi) S11C (psi) S22T (psi) S22C (psi)

2.77 8.78 2.458 15,320 31,470 100,030 57,540

PFA Simulation 2.779 8.736 2.65 16,427 31,213 104,880 58,100

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% Difference wrt Test 0.324% -0.50% 7.89% 7.22% -0.8% 4.85% 1.05%

In-plane Shear Strength

S12 (psi)

23,350

23,075

-1.17%

Table 7 - Triaxial Material System Calibrated Fiber/Matrix Properties Reproducing ASTM Tests Fiber Property

Matrix Property

Longitudinal Modulus 11 (msi)

30.5

Normal Modulus (msi)

0.57

Transverse Modulus 22 (msi) Poisson’s Ratio 12 Poisson’s Ratio 23 Shear Modulus 12(msi) Shear Modulus 23(msi) Tensile Strength 11 (ksi) Compressive Strength 11 (ksi)

2.40 0.200 0.250 6.00 6.00 440 375

Poisson's Ratio Tensile Strength (ksi) Compressive Strength (ksi) Shear Strength (ksi)

0.350 6.50 39.0 15.0

a) Reverse engineered matrix stress-strain curve used as input to progressive failure analysis

b) Stress-strain curve generated by progressive failure analysis for in-plane shear coupon based on matrix non-linear properties compared to that from test

Figure 6. Stress-Strain Curves that were calibrated for PFA of the Composite Army Bridge D. Simulation of Composite Army Bridge under 4 Points Bending Load The calibrated constituent properties were used in assessing the behavior of a composite Army bridge under 4-point bending load. The FEM of the 3.048 meter long bridge beam was developed with 9,416 elements and 12,130 nodes. Figure 7 shows the dimensions of the bridge cross section along with FEM and supports and loading. The elements used were 8-node solids. The bridge beam was simply supported at the two outer bulkhead locations. Equal vertical loads were applied at the two inner bulkhead locations to simulate a 4-point bending test. Figure 8 provides a view of the test setup. The test was carried out on the simply supported composite bridge with two concentrated loads (linear across the bridge) applied at the 1/3 points of the span.

a) Measured mean values of cross-section dimensions (length of bridge = 120” and 1”=25.4 mm)

b) Finite element model of composite bridge under 4 points bending load

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Figure 7. Composite Army Bridge Geometry and FEA Model with Test Supports and Applied Loads Shown The loading system employed a 55 kips MTS hydraulic actuator. While testing the load was applied continuously in an increasing manner with a rate of 2 kips/min. Using a National Instruments PXI-1042Q data acquisition system, the applied load and the readings of string potentiometers and strain gages were monitored and recorded continuously during the experiment. The behavior of each test specimen was monitored during the experiment and the observations were recorded. Unloading took place as a result of failure of the bridge specimen. Figure 9 shows the failure of the composite bridge (referred to as specimen D4U).

Figure 8. Composite Army Bridge Test Setup

Figure 9. Failure of the Composite Army Bridge (Specimen Number D4U) Figure 10 shows excellent correlation between the test failure load and the PFA simulation: only a 0.7% error on ultimate load prediction. Ultimate failure occurred due to damage in the lower deck (transverse tension) and sidewalls (shear strain). Note that the predicted beam deflection is linear up to 28 kips while the test data showed a nonlinear response to 10 kips followed by a linear response to 27 kips. There was evident damage in the top skin where the loads were applied. The PFA simulation showed that damage initiated (matrix cracking) in the lower chord of the beam while fracture initiated in the top surface near in the load application area. The PFA analysis indicated that the failure modes were predominately transverse tension, shear, and combined stress. Figure 11 depicts the PFA predicted damage and fracture initiation locations. The results presented in this section illustrate the effectiveness of the micro-mechanics based PFA approach to simulate accurately the behavior of structural components made from complex material architectures.

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Load vs. Displacement - Specimen D4U Test (Blue) vs. GENOA (Red) Raw Data 40

35

30

GENOA Predicted

Load (Kips)

25

Test Results 20

15

10

5

0 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

Displacement (in.)

Figure 10. Load Displacement Curve for Composite Army Bridge under 4-Point Bending Compared to PFA Simulation (1 kip =4448.222 newtons and 1“ = 25.4 mm)

a) Damage initiation location (element marked with red is damaged)

b) Fracture initiation location (marked with red)

Figure 11. Damage Initiation and Propagation, Fracture Initiation and Propagation Locations in Composite Army Bridge under 4-Point Bending Test

E. Health Monitoring of Composite Bridge Using Advanced Diagnostic Systems Considering the test setup, three different behavioral zones were taken into consideration (Figure 8). In zone 3 with no shear stresses and the maximum bending, the specimen is susceptible to undergo local buckling due to excessive compression, while in zone 2, the behavior of the specimen may be influenced by a combination of shear and bending. However, in zone 1 near the end supports, shear behavior is further dominant. Accordingly, the strain gages as well as the string potentiometers were placed and distributed over the length of the specimen in a fashion to acquire data from the behavior of the bridge in different locations, particularly the critical points. These strain data are compared later with numerical tests results. A compact cell phone size radio frequency (ZigBee) wireless strain measurement sensor system to measure the structural strain deformation was developed [6]. The developed system provides an accurate strain measurement data stream to the Internet for further Diagnostic and Prognostic (DPS) correlation. Existing methods of structural measurement by strain sensors (gauges) do not completely satisfy problems posed by continuous structural health monitoring (Figure 12).

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Figure 12. Fiber optics strain measurements The need for efficient health monitoring methods with real-time requirements to bidirectional data flow from sensors and to a commanding device is becoming critical for keeping our daily life safety. The use of full-field strain measurement techniques could reduce costly experimental programs through better understanding of material behavior. Wireless sensor-network technology (Figure 13) is a monitoring method that is estimated to grow rapidly providing potential for cost savings over traditional wired sensors. The many of currently available wireless monitoring methods have: the proactive and constant data rate character of the data streams rather than traditional reactive, event-driven data delivery; mostly static node placement on structures with limited number of nodes. Alpha STAR Electronics’ wireless sensor network system, ASWN, addresses some of these deficiencies, making the system easier to operate.

Figure 13. ASWN and GENOA integration concept Although system-level power consumption and very large scale dataset transmissions (within the Internet) were not tested, the basic data process and wireless operation worked well. In fact, it has been proven that the ASWM is accurate enough for strain measurement using existing technologies, as-is and/or modified. Thus, the system can be expanded to a larger extent economically. Thirteen strain gages were placed on the numerical model (Figure 14) according to experimental tests. Four of them (S5, S7, S8, S9) are situated either on bottom or top of the bridge to record tension and compression strain, the others are placed on the sidewalls for shear measurement.

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Figure 14. Strain gages location on smart bridge Strain data are monitored in the “Sensors” module in GENOA along with experimental data (Figure 7). A userdefined strain threshold can be added in this module. If the strain value exceeds this threshold, a red light is displayed as a warning. Figure 15 shows that simulation data are consistent with experimental data provided by UCI. The accuracy of the PFA prediction as compared to test encourages the application of the technology to other critical structures.

Figure 15. Experimental tests vs. Simulations Strain monitoring

4. Summary The authors presented a simple but effective micro-mechanics based PFA methodology for evaluating the performance of composite structures made from complex material architectures. The methodology is a judicious combination of composite micro and macro mechanics, finite element analysis, and damage tracking and fracture algorithms to perform accurately D&DT of composite parts and structures. The approach is generalized and can be applied to various types of composite structures designed for other applications such automotive and aerospace. The following can be concluded from the present study: 1.

Calibration of the fiber/matrix baseline provided lead to accurate simulation of ASTM standard compression, tension, and shear tests considering the composite fabrication parameters (i.e., ply angle orientation, fiber volume ration, and void volume ratio).

2.

The use of the calibrated properties in PFA structural evaluation of the composite bridge test article showed excellent correlation with laboratory static bending test. Monitoring of behavior of large scale structures requires multiple sensors, gathering of data from those sensors quickly, processing the information stream, and sending the results to remote locations for engineering evaluation. All these processes must be done quickly and accurately. The system’s cost can be prohibitively high. ASWN combined with advanced sensors monitoring capabilities can be one of quick solutions to that problem.

3.

5. References 1. 2.

B. Cox, M. S. Dadkhah, “The Macroscopic Elasticity of 3D woven Composites”, Journal of Composite Materials, Volume 29, No. 6 1995. C. Chamis, “Simplified Composite Micromechanics Equations for Hygral, Thermal and Mechanical Properties”. NASA Technical Memorandum 83320, Thirty Eighth Annual Conference of the Society of the Plastics Industry (SPI) Reinforced Plastics/Composites Institute Houston, Texas, February 7-11, 1983.

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3.

“GENOA Durability and Damage Tolerance, Life and Reliability Prediction Software”, www.ascgenoa.com, Alpha STAR Corp., Long Beach, CA, 2010.

4.

D. Huang, F. Abdi, “Analytical Characterization and Damage Propagation of Three Dimensional Composites”. AIAA2006-1842, Newport, RI, May 1-5, 2006 G. Abumeri, F. Abdi, M. Baker, M. Triplet and, J. Griffin “Reliability Based Design of Composite Over-Wrapped Tanks”. SAE World Congress, 07M-312, Detroit MI, April 2007. H. Ide, F. Abdi, C. Dang, and T. Takahashi, Bruce Sauer, “Development Of A Wireless Strain Sensor System For Structural Health Monitoring”, American Society Of Civil Engineers International Committee, Los Angeles Section, 5th International Engineering and Construction Conference (IECC’5), August 27-29, 2008.

5. 6.

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