Acoustic Emission Nondestructive Testing, Evaluation, Analysis, and Consulting - AURA Vector

Dr. Eric von Krumreig Hill's Research Applications

AURA Vector - Acoustic Emission Nondestruction Testing, Evaluation, Analysis, and Consulting

1. Ultimate Strength Prediction from Low Level Proof Loads

Both statistical and backpropagation neural network (BPNN) techniques for predicting burst pressures in filament wound composite pressure vessels have been developed using acoustic emission (AE) data taken at low hydrostatic proof loads. These analyses included undamaged [1] and impact damaged [2] bottles, both propellant filled (simulating rocket motor cases) and unfilled, as well as some containing manufacturing defects [3-5]. Predictions were successfully made in 5.75 inch diameter graphite- [1], Kevlar®- [2], and fiberglass-epoxy [3-5] pressure vessels plus 18 inch diameter graphite-epoxy [3] vessels. The same techniques were employed in predicting burst pressures in undamaged, impact damaged, and cut hoop fiber damaged 15 inch diameter graphite-epoxy overwrapped pressure vessels (COPVs) at ambient and cryogenic temperatures [6]. The effects of size, material, temperature, and damage state were all accounted for in the statistical and neural network analyses. Burst pressure predictions in every case were obtained with a worst case error within ±5%. Thus, a technique has been developed to accurately predict burst pressures in filament wound composite pressure vessels using AE flaw growth data generated at the beginning (<=25% Pburst) of the hydrostatic pressurization cycle.

This technique has also been applied to the prediction of ultimate loads in graphite-epoxy tensile test specimens [7], fiberglass-epoxy I-beams in cantilever loading [8], and rectangular cross-section fiberglass-epoxy beams in three-point bending [9]. Moreover, it has been used to predict ultimate compressive loads in impact damaged composite coupons [10,11], and finally, it has been employed to predict ultimate strengths in 2195 aluminum-lithium welds [12]. Hence, this method for predicting failure strengths from low proof load AE flaw growth data in composites can be applied to metal structures as well. Study of the underlying mathematics should lead to the development of a microscopic failure theory for real world structures.

2. Fatigue Life Prediction

In a technique similar to that used for predicting burst pressures and ultimate strengths, the fatigue lives of inconel and stainless steel aerospace bellows were predicted with a worst case error within ±5% from AE flaw growth data taken during the first 10% of life [13]. Again this was accomplished through the use of both BPNNs and multivariate statistical analysis. More recently this technique was effectively applied to the prediction of the fatigue lives in notched 7075-T6 aluminum fatigue specimens undergoing three sets of low cycle fatigue loadings: 0-4,000, 0-3,000, and 0-2,000 lbf (R = 0.0) [14-16]. Multiple hit data (noise) and sparse data sets (<250 hits) led to worst case errors of 16.4% and -13.9% for the first two loading conditions; however, the 0-2,000 lbf cyclic loading had results similar to the bellows, a 3.66% worst case prediction error. For this last loading condition the AE data used for prediction were taken during the first 25% of the average cyclic life of the notched specimens. It is expected that this technique will also work in predicting fatigue lives in composites.

3. In-Flight Fatigue Crack Growth Monitoring in Aircraft

Fatigue crack growth, rivet fretting, and rubbing noises in aluminum aircraft structures have been classified from AE data using Kohonen self-organizing map (SOM) neural networks. Similar results were obtained from the power spectra of the raw AE waveforms [17,18] and from the six AE waveform quantification parameters [19]: counts, energy, amplitude, duration, risetime, and counts-to-peak. In-flight fatigue crack monitoring systems have been successfully flown on the engine cowling of a Piper PA-28 Cadet [20] and on the vertical tail of a Cessna T-303 Crusader aircraft [21]. Such systems can be used to promote maintenance schemes based on replacement for cause rather than replacement at conservatively calculated intervals using fracture mechanics. This research should help minimize maintenance costs and extend the service lives of aging aircraft.

4. Classification and Modeling of Failure Mechanism Data

Classification and modeling of acoustic emission flaw growth data is important in the prediction of failure stresses/loads in both metal [12] and composite [3,4] structures. Knowing the percentages of the various failure mechanisms has allowed the development of ultimate strength prediction equations. However, in order for the prediction to be accurate, the failure mode classification must be correct [22,23], which requires SOM neural networks [24,25] plus mathematical modeling to eliminate outliers [26]. This research has shown that the six AE waveform quantification parameter distributions - counts, energy, amplitude, duration, risetime, and counts-to-peak - can best be modeled by either lognormal [25] or bounded Johnson [26] distributions, the latter being more accurate.

5. Source-Receiver Problem

The source-receiver problem in acoustic emission nondestructive evaluation involves determining the waveform source from the transducer output of a received signal that has propagated through an elastic medium. Identifying the source involves a deconvolution in three parts: the transducer response, the specimen response, and the transducer-specimen interface response. A reciprocity technique for calibrating piezoelectric transducers in a diffuse field was developed [27], which represents the transducer response portion of the source-receiver problem. Exact normal mode solutions for the forced vibrational response of the rectangular parallelepiped (block) with uniformly-loaded and stress-free boundary conditions have also been obtained for the specimen response portion of the source-receiver problem. These solutions include rigid, rigid-lubricated [28], mixed [29], stress-free [30], and uniformly-loaded [31] boundary conditions. Knowing the transducer output and the transducer and specimen responses allows the solution of the transducer-specimen interface response. The next step in this research is to verify these solutions using experimental and computational techniques. Once verified, the solutions for the real world problem of an unknown source with an unknown location in a given three-dimensional structure will have been obtained.

References

  1. E.v.K. Hill, J.L. Walker II and G.H. Rowell, "Burst Pressure Prediction in Graphite/Epoxy Pressure Vessels Using Neural Networks and Acoustic Emission Amplitude Data," Materials Evaluation, Vol. 54, No. 6, 1996, pp. 744-748, 754.


  2. J.L. Walker, S.S. Russell, G.L. Workman and E.v.K. Hill, "Neural Network/Acoustic Emission Burst Pressure Prediction for Impact Damaged Composite Pressure Vessels," Materials Evaluation, Vol. 55, No. 8, 1997, pp. 903-907.


  3. E.v.K. Hill, "Predicting Burst Pressures in Filament Wound Composite Pressure Vessels by Using Acoustic Emission Data," Materials Evaluation, Vol. 50, No. 12, 1992, pp. 1439-1445.


  4. M.E. Fisher and E.v.K. Hill, "Burst Pressure Prediction in Filament Wound Composite Pressure Vessels Using Acoustic Emission," Materials Evaluation, Vol. 56, No. 12, 1998, pp. 1395-1401.


  5. E.v.K. Hill, "Acoustic Emission Prediction of Burst Pressures in Fiberglass Epoxy Pressure Vessels," Nondestructive Testing Handbook, 3rd Edition: Volume 6, Acoustic Emission Testing, R.K. Miller and E.v.K. Hill, Technical Editors, and P.O. Moore, Editor, American Society for Nondestructive Testing, Columbus, OH, 2005, pp. 383-390.


  6. E.v.K. Hill, S.-A.T. Dion, J.O. Karl, N.S. Spivey and J.L. Walker II, "Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels," Journal of Acoustic Emission, Vol. 25, 2008, pp. 187-193.


  7. E.v.K. Hill and J.L. Walker II, "Backpropagation Neural Networks for Predicting Ultimate Strengths of Unidirectional Graphite/Epoxy Tensile Specimens," Advanced Performance Materials, Vol. 3, No. 1, 1996, pp. 75-83.


  8. E.C. Fatzinger and E.v.K. Hill, "Low Proof Load Prediction of Ultimate Loads of Fiberglass/Epoxy Resin I-Beams Using Acoustic Emission," Journal of Testing and Evaluation, Vol. 33, No. 5, September 2005, 8 pages (on line).


  9. E.v.K. Hill, M.D. Dorfman and Y. Zhao, "Ultimate Strength Prediction in Fiberglass/Epoxy Beams Using Acoustic Emission Amplitude Distribution Data," Advances in Acoustic Emission - 2007: Proceedings of the 6th International Conference on Acoustic Emission, K. Ono, Editor, Acoustic Emission Working Group and Acoustic Emission Group, Encino, CA, 2007, pp. 330-335.


  10. Y. Zhao, C.D. Hess, E.v.K. Hill and C.-S. Wang, "Prediction of Residual Strength of Laminated Composites Subjected to Low Velocity Impact," Proceedings of the Society of Plastics Engineers 62nd Annual Technical Conference (ANTEC 2004), Society of Plastics Engineers, Chicago, 2004, pp. 1369-1373.


  11. Y. Zhao, T.-K.D. Nguyen and E.v.K. Hill, "After-Impact Compressive Strength Prediction for Laminated Composites," 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials (SDM) Conference, Newport, RI, May 2006.


  12. E.v.K. Hill, P.L. Israel and G.L. Knotts, "Neural Network Prediction of Aluminum-Lithium Weld Strengths from Acoustic Emission Amplitude Data," Materials Evaluation, Vol. 51, No. 9, 1993, pp. 1040-1045, 1051.


  13. D.L.W. Ballard, E.v.K. Hill and J.B. Allen, "Acoustic Emission Detection of Fatigue Crack Growth in Edge-Welded Metal Bellows," Second International Conference on Nonlinear Problems in Aviation and Aerospace, Vol. 1, S. Sivasundaram, Editor, European Conference Publications, Cambridge, 1999, pp. 73-78.


  14. E.v.K. Hill and E.C. Ibekwe, "Neural Network Prediction of Fatigue Life in 7075-T6 Aluminum Specimens from Acoustic Emission Data," ASNT Fall Conference & Quality Testing Show 2004, American Society for Nondestructive Testing, Columbus, OH, 2004, p. 44.


  15. "Prediction of Fatigue Life in 7075-T6 Aluminum from Neural Network Analysis of Acoustic Emission Data," N.S. Spivey, MSAE Thesis, Embry-Riddle Aeronautical University, Daytona Beach, FL, 2007.


  16. J. Suleman, E.v.K. Hill, E. Villa and M.A. Okur, "Neural Network Fatigue Life Prediction in Aluminum from Acoustic Emission Data," Aging Aircraft 2009, The 12th Annual Joint FAA/DoD/NASA Conference on Aging Aircraft, Kansas City, MO, May 2009, 29 pages (on line).


  17. A.F. Almeida and E.v.K. Hill, "Neural Network Detection of Fatigue Crack Growth in Riveted Joints Using Acoustic Emission," Materials Evaluation, Vol. 53, No. 1, 1995, pp. 76-82.


  18. W.P. Thornton and E.v.K. Hill, "Identification of Acoustic Emission Signals from Fatigue Crack Growth in Thin Aluminum Pressure Vessels," ASNT 1995 Spring Conference, American Society for Nondestructive Testing, Columbus, OH, 1995, pp. 112-114.


  19. M.L. Marsden and E.v.K. Hill, "Classification of Fatigue Cracking Data in a Simulated Aircraft Fuselage Using a Self Organizing Map," First International Conference on Nonlinear Problems in Aviation and Aerospace, S. Sivasundaram, Editor, Embry-Riddle Aeronautical University Press, Daytona Beach, FL, 1997, pp. 405-410.


  20. E.v.K. Hill and S.G. Vaughn III, "Fatigue Crack Monitoring of an Aircraft Engine Cowling in Flight," Nondestructive Testing Handbook, 3rd Edition: Volume 6, Acoustic Emission Testing, R.K. Miller and E.v.K. Hill, Technical Editors, and P.O. Moore, Editor, American Society for Nondestructive Testing, Columbus, OH, 2005, pp. 367-376.


  21. E.v.K. Hill and C.L. Rovik, "In-Flight Fatigue Crack Growth Monitoring in an Aircraft Vertical Tail," scheduled for publication in Nondestructive Testing Handbook, Aerospace Applications, R. Bossi, Technical Editor, and P.O. Moore, Editor, American Society for Nondestructive Testing, Columbus, OH, 2009.


  22. T.M. Ely and E.v.K. Hill, "Longitudinal Splitting and Fiber Breakage Characterization in Graphite/Epoxy Using Acoustic Emission Data," Materials Evaluation, Vol. 53, No. 2, 1995, pp. 134-140.


  23. M. Kouvarakos and E.v.K. Hill, "Isolating Tensile Failure Mechanisms in Fiberglass/Epoxy from Acoustic Emission Signal Parameters," Materials Evaluation, Vol. 54, No. 9, 1996, pp.1025-1031.


  24. K.M. Kostreva and E.v.K. Hill, "Classification of Acoustic Emission Data from Graphite/Epoxy Failure Mechanisms Using Neural Networks," Fourth International Conference on Nonlinear Problems in Aviation & Aerospace, European Conference Publications, Cambridge, 2003, pp. 281-288.


  25. E.v.K. Hill and R.J. Demeski, "Classification of Failure Mechanism Data from Fiberglass Epoxy Tensile Specimens," Nondestructive Testing Handbook, 3rd Edition: Volume 6, Acoustic Emission Testing, R.K. Miller and E.v.K. Hill, Technical Editors, and P.O. Moore, Editor, American Society for Nondestructive Testing, Columbus, OH, 2005, pp. 167-171.


  26. E.v.K. Hill, D.R. Lendzioszek R.J. Demeski and M. Kouvarakos, "Modeling of Acoustic Emission Failure Mechanism Data from a Unidirectional Fiberglass/Epoxy Tensile Test Specimen," in preparation for publication in Research in Nondestructive Evaluation.


  27. E.v.K. Hill and D.M. Egle, "A Reciprocity Technique for Estimating the Diffuse Field Sensitivity of Piezoelectric Transducers," Journal of the Acoustical Society of America, Vol. 67, No. 2, 1980, pp. 666 672.


  28. E.v.K. Hill and D.M. Egle, "The Forced Vibrational Response of the Rectangular Parallelepiped with Rigid Lubricated Boundaries," Journal of Sound and Vibration, Vol. 80, No. 1, 1982, pp. 61 69.


  29. E.v.K. Hill and D.M. Egle, "The Response of the Rectangular Parallelepiped to a Simulated Acoustic Emission Burst," Journal of the Acoustical Society of America, Vol. 71, No. 4, 1982, pp. 891 901.


  30. E.v.K. Hill, "The Vibrational Response of the Rectangular Parallelepiped With Completely Stress Free Boundaries," Journal of the Acoustical Society of America, Vol. 75, No. 2, 1984, pp. 442 446.


  31. E.v.K. Hill, "A General Normal Mode Solution for the Free Vibration of the Rectangular Parallelepiped," Journal of the Acoustical Society of America, Vol. 78, No. 4, 1985, pp. 1344 1347.


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