Department of Aerospace Engineering, Indian Institute of Science, Bangalore, INDIA $ Corresponding Author

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1 Fatigue Crack Growth Monitoring in Ti-6Al-4V Alloy Using Acoustic Emission Technique and Digital Image Correlation Shivanand Bhavikatti a $, M R Bhat a, and CRL Murthy a a Department of Aerospace Engineering, Indian Institute of Science, Bangalore, INDIA $ Corresponding Author bkatti@aero.iisc.ernet.in Abstract Crack growth due to fatigue loading can be monitored using the accurate measurement of surface displacement and hence strains during deformation using Digital image correlation (DIC). While other techniques require point-by-point scanning over the area to be tested for the object, a whole field strain measurement can be made in one go both inside as also on the surface of the specimens by DIC. Thus, DIC could provide a cheaper and quicker testing technique for crack propagation. DIC works by measuring stain by interfering a reference image with images taken at different loads from the surface of the structure. Through the study presented in this paper an attempt is made to establish correlation between Acoustic Emission Data and strain measured by DIC for detecting crack initiation and monitoring its propagation for prediction of failure of a structural component under fatigue loading. Experiments have been carried out on a set of Ti-6Al-4V alloy specimens subjected to constant amplitude fatigue loading with continuous AE monitoring. The results obtained show that DIC can be a reliable off-line tool to validate AE data for establishment of the technique. Thus, the objective of experiments has been to identify and validate genuine AE signals due to fatigue damage and characterize them to identify progressive stages leading to the final failure. 1. Introduction Fatigue tests typically require long testing times. This is due to periodic interruption of the fatigue test to manually measure crack lengths which can be avoided by using DIC where in the measurements are made without interrupting the test. Recently several researchers reported the use of DIC for transient fracture [1]. The use of high-speed and high resolution cameras, thus, enables new possibilities for DIC testing. The idea behind the method is to measure the displacement of the material under test by tracking the deformation through painted spots applied to the component's surface and obtain the speckle patterns from digital images acquired during loading. Over the past few decades acoustic emission (AE) monitoring has been explored as an effective non-destructive technique for the detection, location and monitoring of active defects and dynamic processes in a variety of structures including full scale tests of airframes, petro chemical components and civil structures. This method has enormous potential as a tool for structural integrity evaluation, extraction of useful information from complex data which include various extraneous noise is a real challenge [2,3]. AE technique is based upon the detection of elastic waves generated when a material

2 undergoes plastic deformation and/or cracking during which rapid release of energy from a localized source within a stressed material occurs[4,5]. These transient elastic waves can be detected by highly sensitive piezo-electric sensors. While the sources of interest of acoustic emission are defect related processes such as crack propagation and plastic deformation of material, background noises that normally exist in laboratory environment and field conditions pose a major problem for extraction of true AE activity. AET has very attractive advantages such as the capability to not only detect active microscopic and macroscopic failure mechanisms but also to obtain the location of these energy releasing sources in a larger structural components without scanning the entire component surface[6]. These are the features which still lure the structural engineers to try the technique to study the complex fatigue and fracture phenomena in critical structural components in different engineering fields. Thus, this attempt is to utilize the AET as an on-line monitoring tool to study the fatigue crack initiation and propagation in Titanium alloy extensively used in aerospace structural components. 2. Experiments Fatigue tests were carried out on Ti-6Al-4V alloy choosing three point bend as test configuration; geometrical dimensions of specimen are shown in Fig1. Specimen preparation includes scribing of graduated scale on the surface to facilitate crack length measurements in increments of 1mm from notch to 50% of the depth of crack propagation in the specimen. A high speed camera with 12 megapixel is used to take the snaps for DIC. Fig 1.: Schematic Three point bend Ti-6Al-4V Specimen, dimensions in mm Fatigue tests were carried out using INSTRON-1341 configured with ± 100 KN capacity, ± 50 mm stroke, computer controlled servo hydraulic dynamic testing machine. Fatigue test parameters were also chosen based on the preliminary tests carried out in the laboratory, loading frequency of 18 Hz, amplitude range of 7kN-1.4kN with stress ratio of 0.2 is maintained for all the samples[7]. A 10 X magnification lens was used to trace the crack growth to the subsequent divisions. Acoustic emission data from all the test specimens was continuously acquired and recorded using multi-channel acoustic emission monitoring system with sensors, associated preamplifiers and accessories. Two peak resonant type acoustic emission sensors with highest sensitivity around 300kHz were used in the linear location mode for detection for AE events due to crack initiation and propagation in the specimen. The sensors were bonded to the specimen surface using adhesive tape with high vacuum silicon grease as the couplant interface between the sensor and the specimen surface. Preamplifiers with 40 db fixed gain and a band pass filter in the range of KHz were used along with the AE sensors. Acoustic

3 emission data measured and recorded includes hits (emission detected by a single sensor), events (emissions detected and located by two sensors), energy, time of the event, rise time, parameter (load), duration and amplitude throughout the experiment. Through a set of preliminary tests, threshold of 55dB was found to be optimum under laboratory test environment. AE sensors have been bonded on the specimen to locate crack initiation and propagation. Notched sample is preferred as it is obvious that the crack initiation has to take place from the high stressed zone at the notch tip. The emphasis has been on AE signal generated from this region. Non- metallic liners with a combination of Glass Fiber Reinforced Plastic and Teflon film were used as contact interfaces in between loading points and the specimen surface to avoid noise generated due to friction being picked by the sensors. The AE monitoring system was calibrated for linear location of AE source by breaking a 2H, 0.5-mm-diameter pencil lead at each side of the center notch. The method of using a pencil lead break to calibrate AE transducer and system sensitivities is detailed in ASTM E [8], Standard Guide for Determining the Reproducibility of Acoustic Emission Sensor Response. Breaking pencil lead at tip of the notch generates pulse signal that is similar to AE signal generated during crack growth. AE monitoring system records signal arrival time, parameters and calculate the difference of arrival time t, between the two sensors. Obtained t value and other characteristics of signal are later used as inputs to the system which establishes location of active AE source and its characteristics. 3. Results and Discussion : Time domain parameter analysis of AE Signals in correlation to fatigue process Fig 2. Distribution of AE (a) Events Vs location (b) Cumulative Counts Vs Time Fig 2(a) shows the overall distribution of the AE activity recorded during the fatigue tests. It is clearly seen in Fig2(a) that majority of AE events were generated in the notched region. Cumulative AE activity in this region with reference to number of applied fatigue cycles is presented in Fig 2(b) and the clear change in slope corresponding to the initiation of crack at the notch tip can be observed. This is verified with optical aid. Significant rise in AE activity during crack initiation is observed in all the samples tested. Eventually once crack is initiated, crack propagation enters stable crack growth stage, here AE activity exhibits reduced emission rate. Loading was stopped when crack propagated 50% of the depth. AE data at crack initiation and at crack propagation for each specimen was analyzed and the AE profile for crack initiation phase was discriminated from the complete AE data profile in the play back mode. This was done primarily on the basis of the cumulative count plot against the time scale (indirectly reflecting the load cycles by multiplying with the corresponding load cycle frequency).

Fig 2(b) shows Cumulative counts vs Time, from this graph it can be observed that crack was initiated very much prior to visual observation. Crack initiation was observed visually at around 1300 sec.

4 Fig 2(b) shows Cumulative counts vs Time, from this graph it can be observed that crack was initiated very much prior to visual observation. Crack initiation was observed visually at around 1300 sec. Considering time, crack was actually initiated at around 300 sec to 1100 sec. So it is necessary to find out at which point crack has started. While the detection of crack initiation and identification of stable crack growth was possible through simple approach of monitoring overall AE activity generated with reference to number of fatigue load cycles applied. Further signal analysis revealed advanced information in terms of characteristic signatures of AE in these stages. It is clearly evident from Fig 2(b) that crack initiated at the centre of notch along the width is well in advance compared to initiation observed on surface, which can be substantially explained referring to Fig 3(a) and Fig 3 (b). Fig 3(a). Fatigue Crack Initiation as observed on the Surface of the Specimen, (b). Distribution of AE Cumulative Counts Vs Time before Crack propagation Experiments were conducted using identical set of samples to verify crack observed on surface of the specimen applying fatigue load. Fig 3(a) shows truncated specimen in which crack initiation was observed and three points namely a, b, and c are marked. Significance of a, b and c are as follows. Crack was first initiated at a due to plain strain condition at this moment, AE counts were more compared to previous loading cycles (at around 400 sec) as Shown in Fig3(b). Digital Image Correlation (DIC) Digital image correlation (DIC), sometimes called electronic speckle photography, digital image processing was used to solve the crack identification problem. A DIC software VIC2D from Correlated Solutions[9] has been used for obtaining the strain change on the surface specimen through speckle interferometry. The camera was positioned approximately 500 mm from the three point bend specimen in the loading position as shown in Fig 4, perpendicular to facing the specimen. The camera was carefully focused and Images have been obtained and analyzed using VIC2D software. By using this technique full field measurements made on the specimen can be analyzed. Images were taken after every 150 cycles over the full test and the data thus recorded has been analyzed using VIC2D. Once calibrated, the DIC software is able to calculate the displacement field of a specimen at any point during loading with a corresponding image. A strain field is obtained by differentiation of the associated displacement field. The displacement field is calculated by comparing the loaded and reference images. The amount of crack growth between any two intervals of 150 cycles loading points was determined by the shift between their associated strain fields in front of the crack tip by displacement vector.

small subset of the images taken while no load was applied. By repeating this process for a large number of subsets, full-field deformation data can be obtained.

However, the specimen surface must have a fairly random pattern, which can either be naturally occurring or applied to the specimen before the test.

DIC (a) Strain in specimen at crack tip (b) enlarged view of crack Tip Using the software, first a reference image, called a base image with the unloaded specimen was selected.

5 Fig 4. Schematic of Digital Image correlation Mathematically, this is accomplished by finding the region in a deformed image that maximizes the normalized cross-correlation co-efficient with regard to a small subset of the images taken while no load was applied. By repeating this process for a large number of subsets, full-field deformation data can be obtained. The DIC method does not require the use of lasers and the specimen can be illuminated by means of a white-light source. However, the specimen surface must have a fairly random pattern, which can either be naturally occurring or applied to the specimen before the test. Among the many methods for pattern application are self-adhesive, pre-printed patterns, stamps and application of paint speckles with air-brushes, spray cans or brushes. (a) (b) Fig 5.DIC (a) Strain in specimen at crack tip (b) enlarged view of crack Tip Using the software, first a reference image, called a base image with the unloaded specimen was selected. Using the software tool, a quadrilateral area of interest was chosen on the specimen in order to reduce computation time and define the region to be analyzed. Finally, the rest of images of the specimen over the entire loading sequence, called the deformed images, were selected for comparison with the base image for calculation of each of their respective strain fields which can be seen in Fig 5(a) near the crack tip. and Fig 5(b) the enlarged view near the crack Tip. Fig 6 shows the strain in the specimen after 10000, 20000, and cycles, as indicated (a), (b), (c), (d) respectively and gradual increase in the strain at interval of cycles can be observed. i.e. the plastic zone after cycles is almost nil, and after cycles it's about

1mm along the loading direction. With this Fig 6 (d) clearly shows a high strain in at the notch compared to other images and it has been observed manually the crack has initiated on the surface.

Estimated x-direction strain after(a)10000, (b)20000,(c)30000,and (d) 40000 cycles at the region near to crack tip Fig 7.Strin vs.

At the macroscopic level, one can use painting in order to add an artificial speckle to the surface of the specimen to find displacement.

One was a lineplotter, which plots the values of strain for a selected line on the base image for all of the deformed images as shown in Fig 7- Strain Vs No of Cycles.

6 1mm along the loading direction. With this Fig 6 (d) clearly shows a high strain in at the notch compared to other images and it has been observed manually the crack has initiated on the surface. From this it is evident that crack tip strains increased after some number of cycles leading to crack initiation. (a) (b) (c) (d) Fig 6. Estimated x-direction strain after(a)10000, (b)20000,(c)30000,and (d) cycles at the region near to crack tip Fig 7.Strin vs. No of Cycles using DIC The DIC tools, such as Correli-Q4 [10,11,12] that are used here, are more effective when the images are made of random patterns. At the macroscopic level, one can use painting in order to add an artificial speckle to the surface of the specimen to find displacement. A number of strain plotting tools are available in the VIC-2D software. One was a lineplotter, which plots the values of strain for a selected line on the base image for all of the deformed images as shown in Fig 7- Strain Vs No of Cycles. Here strain are less than 100 micro strain before the crack starts and once the crack starts it is of the order of 300 micro strains or more. The plot shows sudden change in strain at about cycles, which means that the crack has started at the surface of the specimen. Whereas from Fig 2(b) one can see the sudden increase in cumulative counts at same number of cycles from AE data. Thus we can compare the data from AE counts with DIC strain values. Fig 7 shows the axial strain (εyy) in the direction of loading along the crack-line for a series increasing crack lengths. The strain profiles along the un-cracked line were high near the crack tip and decreased as function a distance away from the perceived crack tip.

7 4. Conclusion Acoustic Emission Technique results from experiments carried out are used as an on-line monitoring tool to study fatigue crack initiation and propagation. Along with this, a semi on-line DIC tool has been used to monitor the strain associated with number of cycles on the surface of the specimen. The main objective of the investigations carrier out and presented in this paper aim at developing AE as an online predictive tool at the very early stages of crack initiation and growth in Ti6Al4V material. consequently crack growth experiments have been carried on a set of three point bend specimens and AE data recorded and analyzed. One most interesting aspect that has been observed is that while AE data could be recorded even before the crack append on the surface, proof of crack initiation and growth show confirmation for test of a complementary method, which can register and trace the crack grow while the phenomenon has occurring with in the thickness of the specimen. Thus, DIC has contemplated and implemented to surface strain changes all though the experiment right from the first cycle of the fatigue crack growth experiments. The results obtained and presented show direct correlation between the strain on the surface and Acoustic emission count are well in comparison with a same trend with number of cycles. Thus we use DIC as a validation tool for crack initiation and propagation for AE data. With this we are successful in validating the AE data which is an online monitoring tool to measure and trace the crack. References 1. Kirugulige MS, Tippur H, Denney T. Measurement of transient deformations using digital image correlation method and high-speed photography: application todynamicfracture.applopt2007;46(22): Carle. Hartbower, et al, Acoustic emission from low-cycle high-stress-intensity fatigue, Engineering Fracture Mechanics. Vol. 5. (1973) pp J.R. Kennedy, Acoustic emission during deformation of Ti-6Al-4V, Scripta Metallurgica Vol 16 (1982), pp D.H.Kohn, P.Ducheyne, Sources of acoustic emission during fatigue of Ti-6Al-4V: Effect of micro structure, Journal of material science 27 (1992), D.H.Kohn, P.Ducheyne, Acoustic emission during fatigue of Ti-6Al-4V: Incipient fatigue crack detection limits and generalized data analysis methodology Journal of material science 27 (1992), T. M. Morton, R. M. Harrington and J. G. Bjeletich, Acoustic emissions of fatigue crack growth Engineering Fracture mechanics, Vol. 5 (1973), pp Avraham berkovits and Daining fang, study of fatigue crack characteristics by acoustic emission Engineering fracture mechanics vol. 51,( 1995), no. 3, pp , 8. Standard Test Method for Measurement of Fatigue Crack Growth Rates, ASTM standard Designation: E Correlated Solutions Marion Risbet et al.,digital Image Correlation technique: application to early fatigue damage detection in stainless steel, Procedia Engineering 2 (2010) G. Besnard, et al. Finite-element displacement fields analysis from digital images : application to Portevin-Le Châtelier bands. Expl Mechanics, 46 (6) , SteveVanlanduit et al, A digital image correlation method for fatigue test experiments, Optics and Lasers in Engineering 47(2009)

AE Feature Analysis on Welding Crack Defects of HG70 Steel Used by Truck Crane

AE Feature Analysis on Welding Crack Defects of HG70 Steel Used by Truck Crane Yantao Dou 1, Xiaoli Xu 2, Wei Wang 3,*, and Siqin Pang 1 1 Beijing Institute of Technology, Beijing, P.R. China 2 Beijing