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Acoustic emission monitoring and fatigue life prediction in axially loaded notched steel specimens.

1. Introduction

1.1 AE Methodology in Fatigue Analysis

Acoustic emission is the phenomena, in which transient elastic waves are generated by the rapid release of energy from localized sources within a material as it undergoes deformation [1]. One special advantage of this method over other NDT methods is that AE captures the dynamic process related to structural degradation. Since plastic deformation and fatigue-crack growth are two principal sources of AE signals in isotropic materials, this approach is highly desirable for fatigue assessment. The AE signals produced are quantified by five basic parameters, as depicted in Fig. 1. These AE signal parameters are amplitude (A), duration (D), rise-time (RT), counts (C), and mean area under the rectified signal envelope (MARSE), more commonly known as energy (E). Other parameters, such as average frequency (counts/duration), are combinations of basic AE parameters; these are also useful in many cases. Cumulative counts and cumulative absolute energy are two parameters that are used to develop plots that correlate to the fatigue-crack growth process with time. Background work in fatigue-life prediction using artificial neural networks (ANNs) has been accomplished by several researchers [2-5].


1.2 Artificial Neural Networks for Failure Mode Classification

Artificial neural networks are mathematical algorithms that function similar to the human brain, which, if trained properly, can conclusively identify complex patterns in nonlinear data space. These networks are comprised of artificial neurons, or processing elements (PEs), that form the building blocks of the system. Structurally, the network consists of an input layer, in which each neuron or PE represents a specific AE input data parameter. Based on the classification, defined by the user, the output can be in the form of a binary or a normalized x-y coordinate layer. The output layer is linked to the input layer through weight functions, which can be thought of as coefficients in an equation. These weights are adjusted during the training phase of the network to provide the appropriate output.

One particular ANN employed for pattern recognition is Kohonen self-organizing map (SOM), which is capable of classifying AE failure mechanism data, based on the principle of competitive "soft learning". Soft learning allows not only the winning neuron weight to be updated, but also the weights of the adjacent neurons that are linked through a neighborhood function, to optimally exhibit the input space. Due to this learning style, relationships existing in the input space (data) are maintained in the output. Overall, the objective of this network is to model an unknown distribution of a multidimensional input by a topological map of lower dimensionality.

Figure 2 shows a Kohonen SOM neural network as a fully connected, two layer network. The two layers are defined as the input layer and the Kohonen classifying output layer. In Fig. 2, there are "D" AE inputs--in this case, duration, counts, amplitude--which can be classified into up to "k" categories within the 2-D Kohonen output layer. The two layers are connected by weighted connections that continually change until the network is fully trained to best classify the input data into clusters of "like" data at the output [7].

1.3 Artificial Neural Networks for Failure Prediction

The backpropagation neural network (BPNN) is another form of ANN that is widely used in predictive learning and in cases, where a substantial amount of data is available with complex relationships. Architecturally, this network consists of at least three layers: input, hidden, and output layers. In some cases, an additional hidden layer can also be added to act as a data classifier, which is necessary when the data are nonlinearly separable.

Figure 3 shows an artificial neuron or processing element (PE) with a hyperbolic tangent (tanh) activation function. The artificial neuron has several inputs, [x.sub.n], each of which is multiplied by a weight coefficient, wn. These weighted inputs are then summed up in the PE and the result processed (squashed into the range -1 to +1) by the activation function before being sent on as the output of the artificial neuron. Collections of these artificial neurons are what make up the architecture of neural networks.




As shown in Fig. 4, the BPNN utilized herein is fed an AE amplitude distribution into the input layer (seen in blue). This is linked to the hidden layer through the adjustable normalized (0 [less than or equal to] w [less than or equal to] 1) weights. The neurons in the hidden layer sum the weighted inputs then multiply the output by the nonlinear sigmoidal (s-shaped) activation function, in this case a hyperbolic tangent (tanh) function, which squashes the output into the range [-1, 1]. The number of neurons used in the hidden layer typically follows a (2n + 1) rule, where n represents the number of failure mechanisms identified (prior to employing the BPNN) using a Kohonen SOM [8]. Finally, the single output neuron yields the prediction results: cycles to failure. Additional neurons called bias neurons, which have a fixed input of+1, are used to speed up the training of the network.

Unlike the Kohonen SOM neural network that functions on "soft learning", the BPNN operates on error correction learning [8, 9]. This means that the true output (fatigue life to failure for this research) of the network must be known prior to applying any inputs for predictions. The network starts by setting the weighted connections between the various layers to random values between [0, 1]. Next, the amplitude-distribution data are input to the network. The network output is then compared to the true output to determine the error. This error is then used in a delta rule to make corrections to the hidden layer weights. Further delta-rule weight corrections are backpropagated until the input layer is reached. At this point, all the weights linking the neurons between the layers of the network have been updated. Reapplying the amplitude-distribution data at the input, this process is repeated until the RMS output error preselected by the user is attained. The amplitude distributions seen in Table 3 can serve as an example of the training or learning data, which is input into the first layer along with the known fatigue lives. The training algorithm then compares the predicted value to the actual and calculates the percent error between the two. If this error is less than the user specified RMS error, then the training is stopped. The user specified RMS value serves to control how tightly the networks trains. In other words, if the value is set at 5%, the network will train until the RMS error for all the training files is less than 5%. The reason why it is often not desired to train to very small errors is that the network will then predict poorly on files that are even slightly different from the training files. Hence, an intermediate value is normally chosen--not too small, not too large--that provides optimal predictions.

2. Experimentation

2.1 Specimens

For this research, 32 specimens of A572-G50 steel having three different notch lengths were fatigue tested. 20 specimens with a 3.81-mm deep V-notch were fatigued at six discrete stress levels for prediction purposes. The specimens were marked with a black transverse line 25.4 mm from each end as shown in Fig. 5. These lines served for proper alignment of the clamping grips that held the specimen in the MTS fatigue machine. Additionally, two lines denoting the centerline location of the AE transducers were also drawn at 101.4 mm and 228.6 mm, measured from the top (left-hand end) of the specimen.


2.2 Noise Analysis and Front End Filters

To ascertain the acoustic nature of the MTS fatigue machine and the ambient environment, several noise tests were performed with the two transducers mounted on a specimen. The MTS machine was then switched on with both the mean cyclic load and the span setting at zero. This meant that the load was applied just to grip the test coupons with no tensile load applied along the length of the specimen. Readings from PAC Pocket AE instrument were recorded for half an hour, and three graphs were plotted to illustrate the noise behavior. The sources of noise present were the hydraulic pump located less than 1 m from the specimen plus the hydraulic valves and the actuator piston. The hydraulic grips used to hold the specimen contributed rubbing noise. The reason why there are many AE sources in these tests is because the system (see Fig. 10) is coupled together, meaning all connections are steel to steel, thus providing for acoustic transmission. Another source of noise is electromagnetic interference from the Pocket AE charger interface. This is a long duration signal, as it is a relatively continuous noise signal, which sometimes does not drop below the set threshold (as seen in Fig. 1) and therefore leads to long duration signals.

The first plot of the ambient noise data was an average frequency histogram that shows the average frequency limits of the noise spectrum (Fig. 6). It is apparent from this plot that the noise spans over a diverse range of average frequencies; however, the bulk of it falls between the ranges of 1 kHz to 30 kHz. Additionally, there is high average frequency noise, which occurs at discrete values. For simplicity, the average frequency histogram is here limited to 275 kHz.


The second essential ambient noise plot was duration vs. counts. This is a key graph because it illustrates the length of the noise signal hits. Noise signals can have short or long durations, and they thereby establish the average frequency limits based on the counts present in each hit. In Fig. 7 the noise signature is overlapped with a typical AE test for a bar specimen in fatigue, showing that the principal portion of the noise lies outside the bounds of the real AE data generated from crack propagation. Notice that limited noise is still present within the crack propagations AE data; this is acceptable, however, since this data forms a very small portion of the overall data set.

The final plot of value in the noise analysis was the amplitude histogram of Fig. 8. This graph illustrates the ranges of amplitudes that are present in the noise signals. Since it was determined in Fig. 7 that noise fell into two primary average frequency ranges, Fig. 8 shows the measure of noise present in each of these average frequency limits. It is observed that higher noise content is present at lower average frequencies. In summary, the noise signals consist of amplitudes between the threshold values of 30 and 34 dB.


Based on the limits defined by these plots, front-end filters were set up on each transducer before relevant AE data were recorded during fatigue testing. The Pocket AE hardware allows up to two filters on each of the two channels. For the fatigue tests performed in this research, two filters were applied on each channel. The first filter eliminated all signals of average frequencies up to 25 kHz (Fig. 6), and the second filter removed all hits of counts less than 10 (Fig. 7). Although these filters were highly effective, the Pocket AE still recorded hits with durations up to 10 ms. These are the constant noise signals as discussed previously. These hits were later removed manually from individual files, as this data subset was obviously noise.

2.3 Pocket AE Data Acquisition Set-Up

The Pocket AE data acquisition system requires several parameters to be set properly before testing. The two transducers used and seen in Fig. 10 are PAC Type R15, 150 kHz resonant transducers with operation range of 50-200 kHz. The key settings that were adjusted from the default values are listed in Table 1. The selection of a 30-dB amplitude threshold was based on previous research on isotropic materials. A value below this threshold adds unnecessary noise to the analysis, and a value above this setting is likely to eliminate valuable fatigue data, which is also undesirable. The value of maximum duration was chosen arbitrarily as 1,000 us in order to ensure that all reasonable duration signals were captured. This high upper limit allows for continuous noise of long duration to be recorded by the Pocket AE. This data subset consists of signal hits from continuous rubbing or machine hydraulics. The remaining parameters of peak definition time (PDT), hit definition time (HDT), and hit lock-out time (HLT) are called waveform parameters, and are important to separate noise from fatigue data. Proper setting of the PDT guarantees the correct recognition of signal peak for rise time measurements. A proper value of the HDT parameter ensures that a single signal hit is recorded as only one hit. Essentially, this value determines the end of a signal hit. The HLT closes out the measurement process and stores the hit waveform quantification parameters (amplitude, counts, duration, rise time, and energy) in the data acquisition buffer. The values of HDT and HLT parameters are selected in conjunction with pencil-lead break tests from various locations on the specimen. These values are crucial, since incorrect selections will result in multiple hit data, where two AE hits merge and become one due to insufficient time setting of the HDT and HLT parameters. This prevents the first hit from being fully stored and closed out before the second hit arrives and is recorded. The AE parameters for multiple hit data will obviously be different than these for single hit data.



2.4 Test Procedure

After the specimens were marked as described, and the transducers were attached using hotmelt glue as a couplant, the specimens were loaded using an MTS hydraulic testing machine as shown in Fig. 10. The upper grip was first activated and the specimen was aligned 25 mm from the top end before closing. Next, the bottom grip head was moved using the MTS 407 controller until it was aligned with the lower grip marker. The bottom grip was then closed to secure the specimen. The vertical alignment of the specimen was verified using a bubble level. Next, the MTS controller was set to a sinusoidal load function with 1 Hz frequency. The two transducers were connected to the Pocket AE data acquisition system, which was activated simultaneously at the initiation of the test to acquire data.


3. Results

3.1 Experimental Fatigue Life Results

AFGROW is a widely recognized open-source, damage-tolerance analysis program designed to model crack initiation, growth, and prediction of fatigue life for isotropic materials based on linear elastic fracture mechanics (LEFM). Initially, AFGROW calculations were performed to ascertain the fatigue life; however, there was a high difference (30%) between AFGROW and the experimental tests due to the fact that AFGROW calculations are based on LEFM, and the presence of a 3.81-mm notch generates a stress concentration, which exceeds the yield strength of A572 steel. These high stresses at the crack tip initiate substantial plastic zones as shown in Fig. 11. AFGROW fails to account for the plastic deformation effects, which retards the crack growth and thus under-predicts the total life of the specimens.

Table 2 shows the tests for the 3.81-mm notch. Here it can be observed that, as the cycles to failure increase, the coefficient of variation or variability of the data increases as well. This becomes significant when considering the accuracy of any fatigue life prediction, be it AFGROW or BPNN. An example of this is the two specimens at the 172 MPa loading, where the coefficient of variation is 12.5%. This variation can be noted in Table 2 and in Fig. 12.



3.2 Acoustic Emission Graphs Capturing Failure Mechanisms and Fatigue Behavior

From the AE point of view, the fundamental plot that suggests the existence of failure mechanisms is the amplitude histogram, Fig. 13. This graph typically comprises several overlapping humps with each hump indicative of a failure mechanism. This is useful as different failure mechanisms or AE sources have different amplitude ranges. For example the histogram in Fig. 13 could be showing the mechanism 1 as plastic deformation while mechanism 2 is plane strain and mechanism 3 is the plane stress. It is possible to detect variability in these amplitude distributions due to the stochastic nature of material composition. However, if the experimental conditions and front-end filter settings are kept the same, then the histograms should display similar characteristics. It is imperative to observe any variance in the amplitude distributions, since the accuracy of the BPNN predictions depends upon the selection of the training files. Thus, maximum variability should be provided to the network for training in order to attain the best prediction accuracy, meaning that the BPNN needs to be trained on both the highest and lowest fatigue-life values plus a few in between.

The fatigue process is best described using AE data by the plot of cumulative energy versus cycles to failure as shown in Fig. 14. The cumulative energy initially shows an increase due to crack nucleation or initiation (Region I), followed by steady state growth (Region II), and then finally an exponential increase progressing to failure, known as critically active AE (Region III).





3.3 Kohonen SOM Failure Mode Classifications of Fatigue Test Data

The AE data obtained from fatigue testing were separated into three classifications using the Kohonen SOM neural network: plastic deformation, plane strain fracture (Mode-I tensile), and plane stress fracture (Mode-III tearing). This classification was based on the AE parameters of duration and amplitude in the SOM network. The decision to classify the AE data into three categories was based on a familiarity with the material failure surface under testing and the geometry of the specimens. Figure 15 shows the appearance of the failure surface and its variation for a single-edge crack under Mode-I fracture, similar to this research, with changes in thickness. From this figure, it can be discerned that multiple failure modes can exist based on specimen thickness and composition. Therefore, it becomes extremely important to observe the failure surface of each specimen to recognize the failure character and upon this identification the Kohonen SOM neural network can readily be used to separate the data into the appropriate failure mechanism clusters. It is important to note that while in terms of the fracture surface area the tested specimens, an example of which is shown in Fig. 16, had a plane-strain area (triangular region) slightly smaller than plane-stress area. This however is misleading because as the crack propagates, most of the time is spent on the plane-strain fracture, and the plane-stress crack surface appears very rapidly, typically within the last few minutes of the test.

Figure 17 is the SOM classification of the amplitude histogram data. Here, the classification shows two major failure mechanisms (the plastic deformation and plane-strain fatigue) along with an overlapping less prevalent third (plane-stress) mechanism. In a continuous tensile test, where the load is constantly increased, the plane-stress events would have higher amplitude as a result of increasing load. However, in the case of constant amplitude fatigue testing, while high stresses are produced as a result of area reduction, which lead to higher emission rate rather than higher amplitude as the stresses built up are relieved during unloading.

Figure 18 from PAC shows the approximate amplitude ranges of different AE sources. The classification in Fig. 17 shows that plastic deformation, which is caused by dislocations, as having an amplitude range of about 33 to 50 dB, which roughly agrees with the PAC document. The crack jumps have a higher amplitude range, while no distinction is made in the PAC document between plane-strain and plane-stress cracking. The cracking in the PAC document also roughly agrees with the SOM classification above.



Figure 19 is the classification of the duration versus counts plot. Three clearly distinct data clusters are evident. Here plastic deformation is classified as a data set with low counts and small durations (orange cluster). Plane-strain data, occurring due to Mode-I tensile crack opening, are represented as a longer signal with higher counts (blue cluster). The plane-stress data have the largest scatter (light blue cluster), highest duration and counts, which are consistent with the multiple paths associated with Mode-III tearing. Also note that the data-cluster boundaries are nonlinear with some overlap, both of which indicate that the data classification is not dominated by a single AE parameter and is therefore most likely correct.


Figure 20 is the quintessential plot that compares the energy level among the three failure modes of plastic deformation, plane-strain fracture, and plane-stress fracture. Here it is observed that plastic deformation occurs throughout the fatigue process but at lower energy (amplitude) levels than plane-strain and plane-stress fracture, which occur immediately following.

3.4 BPNN for Fatigue Life Prediction

When it comes to fatigue failure, the primary concern is the assessment of residual fatigue life before a catastrophic failure. In this context, the benefit of the BPNN is that it can be trained to predict a desired solution, in this case fatigue life, based on the characteristics of the AE data from early cycle crack growth. This section details the results from two BPNN networks, both trained for a 3.81-mm notch length, but encapsulating a range of stress levels to predict fatigue life based on AE amplitude distributions.

3.4.1 BPNN for Fatigue Life Prediction

To get acceptable results, it is essential to train the network with the greatest amount of variability in the output; this variability in fatigue lives can be seen in Table 2. For example, at an applied stress of 230 MPa, the network would be best trained using the highest (10,969 cycles) and lowest (9,771 cycles) fatigue lives resulting from that stress. As such, any future prediction between these two fatigue life values should have a reasonably low error value. Also, for a typical BPNN analysis, a good rule of thumb is that 60% of the AE data are used to generate a training file, and the remaining 40% are used to produce a testing file.


The training files depicted in Table 3 display the specimen ID and the corresponding amplitude distributions starting from 30 dB up to 100 dB, the latter being the upper limit displayed by the Pocket AE data acquisition system. Note that for the training file, the experimental fatigue life value is added as the final input (marked in bold). This is because the training file has to provide the true fatigue life as a target value for the BPNN to compute the error. On the other hand, the testing files (Table 4) include the amplitude distributions only: the fatigue-life value is left blank, since that is the output, which is being predicted by the BPNN.

Upon compiling the training and testing files, the distributions were input to the network. Table 5 begins with the number of hidden layer neurons. The remainder of the values listed, are the optimized training parameters determined for the network. Seven hidden layer neurons were initially used in accordance with the (2n + 1) rule [9] and the fact that there are three mechanisms prevalent in the AE data, per Kohonen SOM classification. Determining the final trained parameters for the BPNN requires extensive trial and error by the user. This process involves running the network and comparing the output error after each parameter change in the initial settings, and then comparing the output results with the true values to determine the error in order to backpropagate the adjusted weights. Table 6 displays the optimized prediction results using the trained settings of Table 5. It can be observed that the BPNN has managed to achieve a worstcase error of 19.4% [10].

3.4.2 BPNN#1 for HCFPrediction (25% Fatigue Life Data)

The first neural network shown herein was trained to concentrate on capturing high-cycle fatigue (HCF) specimens having fatigue lives of over 10,000 cycles. Thus, out of the 20 files that were generated originally, nine files were not considered since they represented fatigue lives below the HCF threshold of 10,000 cycles. This further added to the complexity since the total number of data sets was now reduced by almost 50% for the BPNN analysis. In this case, eight files were used for training, and three files were used for testing. It should be noted that the variability is still maintained at 10,000 cycles, and that the testing files were chosen to capture the entire range of life spectrum. As before, the first task was to determine the variability present in the amplitude histograms. Figure 21 displays the amplitude distributions of all 11 specimens used for this analysis. Based on this figure, the training files and testing files for this network are given by Tables 3 and 4.


The first network was trained on only the data from the initial 25% of the fatigue life as shown in Fig. 22. This is extremely useful in real life applications where it is desirable to predict fatigue life long before the specimen reaches failure. Table 5 shows the default and fully trained network parameters for this network. The results from this network are provided in Table 6. These results show that even with only 25% of the data, the BPNN can be trained to predict below a 20% worst-case error.

3.4.3 BPNN #2 for HCF Prediction (Third quarter of Fatigue Life Data)

The second network that was trained using the 50% to 75% AE data; this corresponds to the third quarter of the specimens' life. While it is more desirable to predict the fatigue life as early as possible, oftentimes this is not possible either as the specimen has already been service or because prediction is desired after maintenance crews have discovered a crack and it is desired to predict remaining life at that point.

As seen in Table 7 the parameters for the BPNN are different, as the data itself is comprised of more cracking signals than the early life data which includes the plastic deformation processes involved in crack initiation. This increase in actual fatigue crack data available for training yielded a prediction error improvement from 20% down to 12%.


3.4 Discussion of Results

Larger notch lengths result in higher stress concentrations, which led to substantial plastic zones that extend beyond local yielding; this also restricts the applicability of LEFM. This aspect of the fatigue process will be further investigated in future research using elastic-plastic fracture mechanics (EPFM) in which the larger plastic zones and their effects are taken into consideration.


The AE graph of cumulative energy versus cycles to failure illustrates a linear increase in AE activity during the crack propagation phase of the fatigue spectrum. This is followed by a rapid increase in the total counts as the crack becomes critically active and the data acquisition system experiences data avalanche near failure.

The Kohonen SOM, being an unsupervised network, classifies the AE data per the user's comprehension and familiarity with the problem. Here the AE fatigue data were separated into the three failure modes of plastic deformation, plane strain fracture, and plane stress fracture. This was evident by the amplitude histogram, Fig. 17, where two dominant humps represented failure mechanisms with plane stress being an indeterminate subset without a well-defined distribution. The absolute energy versus cycles-to-failure plots (Fig. 20) showed the presence of the lower energy mode (plastic deformation) prevalent throughout the crack development process followed immediately by the high energy mode of plane strain (Mode-I tensile) and plane stress (Mode-III tearing) fracture. However, by observing the fatigue process and fracture surfaces of the specimens at failure, severe in-plane bending and plastic deformation were observed just prior to final failure at which point plane stress or shear lips were also developing simultaneously. There was some mid range noise misclassified as one of these failure mechanisms as it is clearly present in the noise test seen in Fig. 7.

The backpropagation network for predicting the fatigue life of A572-G50 steel under five different loading conditions was trained and had a 30% error. While at first blush this appeared to be substantial, given the variance in the fatigue life from under 5,000 to above 24,000 cycles, the results matched reasonably well. As such, attention was focused on only the 3.81-mm notch samples, which met the diverse fatigue life spectrum criteria. The worst-case prediction results for the 3.81-mm notch HCF based on first 25% data were under [+ or -]20% and based on third quarter the error was reduced to [+ or -]12%.

The noise, which has been filtered out, would have had a negative impact on the networks ability to predict accurately; however, depending on if the noise is constant during all the tests, then the network can be trained to essentially ignore the noise. On the other hand, if the noise changes from specimen to specimen and overlaps the failure mechanism, then the noise first has to be classified for example by using SOM network and then taken out so that the network only trains on clean data. This step was unnecessary in data presented herein as the filters eliminated most of the noise signals, and the small amount of noise that remained had no significance to the BPNN network. In future tests, linear source location will be utilized as an additional method of filtering.

The one downfall of the BPNN networks presented herein is that if a network is trained well on certain data, it will perform poorly on data, which is dissimilar. Thus, a test was attempted to use the network, which was trained based on the 50-75% (steady crack growth) data to test or predict on the 0-25% (strain hardening plus crack initiation) data (Table 4). This, however, yielded a worse case error of almost 50% largely due to the fact that the failure mechanisms in the two regions are significantly different: fatigue cracking is more prevalent in later part of the test, whereas strain hardening dominates the early part of life. The next step in the research is to isolate the plastic deformation signal using a SOM and predict on these data only, as that would reduce the data variance.

4. Conclusions and Future Work

Acoustic emission graphs of cumulative absolute energy versus fatigue cycles captured the fatigue process exceptionally well and may be considered as characteristic curves to view fatigue crack growth behavior with time. These types of principal graphs can be used to see the severity of the AE activity associated with the crack growth process. In this way AE monitoring can be highly effective in determining the extent of damage in the structure.

Herein the Kohonen SOM neural network classified AE data into the three primary categories of plastic deformation, plane strain, and plane stress fractures. It was observed that the plastic deformation failure mode occurred continually throughout the fatigue crack development process, and that once the plastic zones became strain hardened due to mechanical work, the plane strain and plane stress fracture modes dominated. This research provided a deeper understanding of the fatigue process and failure mode identification in structural steel members in tension.

One objective of this research was to create a backpropagation neural network (BPNN) for fatigue life prediction in structural steel. From the results discussed above, it can be concluded that a BPNN, if trained properly, can accurately predict fatigue life provided sufficient test data are available to perform the training phase. From the large complex structural fatigue prediction viewpoint, it would be appropriate to develop several BPNN networks based on the AE data acquired from the regions of stress concentrations as well as from the AE activity resulting due to different temperatures. In this way the environmental effects and their contributions could be included in predicting residual life. It is fitting to conclude at this point in the development that one neural network may not be able to solve the entire fatigue problem of an operational structure, but rather several neural networks working in conjunction might offer a sound prediction of fatigue life with a high confidence level. Despite the challenges mentioned above, an attempt will be made to design a BPNN that can capture simultaneously various initial notch lengths as well as various applied loads.

In order to validate the applicability of the NDE approach described in this paper--to monitor and evaluate typical structural steel bridge members in bending--fourteen notched A572G50 steel I-beams (S4x7.7) in flexure were tested under fatigue loading [15] subsequent to this research. These I-beams were thought to be good representatives of bridge stringers subjected to transverse cyclic loads from moving traffic. AE data were recorded for high cycle fatigue (HCF); that is, the beam would undergo more than 10,000 load cycles before a fatigue failure occurred [16]. These data were processed using a Kohonen SOM neural network to identify failure mechanisms, then a back-propagation neural network was be used to predict fatigue lives. This resulted in even better results: a +13.4% worst-case error for the 0-25% data and a +4.5% worst case error for the 50-75% data. This was due primarily to the fact that the I-beams were less highly loaded than the tensile specimens, and therefore, the BPNN was able to predict on a much larger number of AE hits and the data contained less noise.


This work was supported in part by Florida Center for Advanced Aero-Propulsion (FCAAP) and Embry-Riddle Aeronautical University (ERAU) College of Engineering Research Grants. Specimens used for experimental work were contributed by the ERAU Mechanical and Civil Engineering Department. The authors are also grateful for the support of Mr. William Russo of the ERAU-College of Engineering for preparing the specimens and test setups for all the experimental work presented in this paper. In addition, the authors would like to extend special thanks to Mr. Michael Potash for his unending support and guidance pertaining to use of the MTS machine.


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Multidisciplinary NDE Group, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114
Table 1: Pocket AE setup parameters.

Standard Setup                    Timing Parameters

Amplitude Threshold Ch1 = 30 dB   Max Duration = 10 ms

Amplitude Threshold Ch2 = 30 dB   Peak Definition Time (PDT) = 200

Standard Setup                    Timing Parameters

Amplitude Threshold Ch1 = 30 dB   Hit Definition Time (HDT) = 400
Amplitude Threshold Ch2 = 30 dB   Hit Lockout Time (HLT) = 900

Table 2: Experimental fatigue life for 3.81-mm notch specimen.

Applied Stress   3 X1 nun Notch Experimental Life

S(Mpa)           Testl    Test2    Test3    Test4    Test5    Test6
276              4394     4730     4981     4886     4896     4494
248              6950     7084     6629     6103     7125
230              10218    10833    10969    10462    9771
207              14901    13861    15808    14160
190              22206    20740    24245
172                       30030    35843

Applied Stress   Mean     Coefficient of Variation

S(Mpa)           Cycles              %
276              4730               5.0
248              6778               6.3
230              10451              4.6
207              14683              5.9
190              22397              7.9
172              32937              12.5

Table 3: 25% HCF BPNN training file.

             Amplitude Histogram
Sample       30-100 dB @ldB incriments

0.15 inch    0 0 0 0 10 21 39 37 37 49 43 68 51 25 21 17 18 9 9 7 12
0.55 yield   7 9 5 5 5 3 4 2 4 2 0 1 1 2 0 1 1 1 1 0 0 0 0 0 0
Test 1       0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22206
0.15 inch    0 0 0 23 167 407 320 354522 854 1296 1222 864473 212 76
             19 15 16 10 4
055 yield    4 1 1 1 4 1 2 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Test 3       0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24246
0.15 inch    0 0 0 3 9 58 72 69 43 31 19 29 31 36 29 34 45 40 36 26 18
0j6 yield    10 10 11 10 5 9 3 2 5 6 1 2 0 2 0 0 0 1 0 0 0 0 0 0 0
Test 2       0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13861
0.15 inch    0 0 0 0 18 163 535 1127 1204966 588 417 600 855 906 531
             16441 19 9 12
06 yield     12 12 5 7 1 2 7 1 3 4 5 2 0 2 1 0 0 0 1 0 0 0 0 0 0
Test 3       0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15808
0.15 inch    00 1 72 129 137 57 55 46 20 35 37 3431 22 27 27 17 22
             13 13
0j6 yield    16 6 9 11 5 5 4 2 1 3 1 2 3 4 0 0 0 0 0 1 1 0 0 0 0
Test 4       0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14160
0.15 inch    0 00 10 2881 73 101 150 315 532 325 76 21 23 22 21 11 13 9
0.66 yield   8 9 0 7 5 1 4 0 3 4 3 0 0 0 1 1 0 2 0 2 1 2 0 0 0
Test 3       0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10969
0.15 inch    0 0 0 0 7 19 28 34 53 43 37 59 45 45 37 26 26 22 18 16 14
0j66 yield   6 12 5 8 1 2 7 2 0 3 3 1 0 1 1 0 0 0 0 0 0 0 0 1 1
Test 4       0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10462
0.15 inch    0 0 0 2 32 36 24 18 15 14 10 12 10 11 6 5 5 2 5 6 1
0j66 yield   4 4 3 2 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Test 5       0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9771

Table 4: 25% HCF BPNN testing file.

Sample       Amplitude Histogram
             30-100 dB @ldB incriments

0.15 inch    0 0 0 1 0 11 13 24 31 31 2331 13 16 11 107 8859
055 yield    8 1 3 6 1 2 1 10 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Test 2       0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0.15 inch    0 0 0 0 1 7 7 26 21 19 20 18 27 22 18 17 147 10 11 11
0j6 yield    8 7 5 8 6 1 4 3 2 3 1 2 2 1 0 1 1 0 0 0 1 0 0 0 0
Test 1       1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0.15 inch    00005 10 18 24 23 29 27 21 23 18 17 18 18 16 15 5 11
0.66 yield   5 7 8 5 2 5 3 3 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0
Test 1       0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 5: 25% HCF BPNN testing file.

BPNN Parameters                    Trained HCF BPNN

# of pro ce sing neurons                  5
Hidden layer Learning coeficient          1
Output Layer Learning coeficient        0.001
Transition Point                         125
Learning coeficient ratio                0.5
F1 offset                                0.05
Momentum                                 0.4
Learning rule                       Norm-Cum-Delta
Transfer function                        TanH

Table 6: 25% HCF BPNN results.

Tested          Experimental   Trained HCF BPNN
Sample          Fatigue life   Predicte% error

0.15-0.55Y-T2   20740          17204   17.0
0.15-0.60Y-T1   14901          12026   19.3
0.15-0.66Y-T1   10218          8240    19.4

Table 7: 50% to 75% HCF BPNN training parameters.

BPNN Parameters                    Trained HCF BPNN

# of pro ce sing neurons                  3
Hidden layer Learning coeficient         0.3
Output Layer Learning coeficient         0.05
Transition P oint                        3700
Learning coeficient ratio                0.5
F1 offset                                0.1
Momentum                                 0.4
Learning rule                       Norm-Cum-Delta
Transfer function                        TanH

Table 8: 50% to 75% HCF BPNN results.

Tested          Experimental   Trained HCF BPNN
Sample          Fatigue life   Predicted  % error

0.15-0.55Y-T1      22206         24063     -8.4
0.15-0.60Y-T1      14901         13207     11.4
0.15-0.66Y-Tl      10218         11423    -11.8
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Article Details
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Author:Barsoum, Fady F.; Suleman, Jamil; Korcak, Andrej; Hill, Eric V.K.
Publication:Journal of Acoustic Emission
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2009
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