Inversion of Physically Recorded Ultrasonic Waveforms Using Adaptive Learning Network Models Trained on Theoretical Data

The objective of this work has been to demonstrate the feasibility of estimating automatically the size and orientation of subsurface defects in metals. The approach has been to (1) obtain computer-generated spectra from various elastic scattering theories, (2) use these spectra to train empirical nonlinear Adaptive Learning Network (ALN) models, and (3) evaluate the theoretically trained ALN\u27s on eight physically recorded defect specimens via a blind test. The results demonstrate that very good defect characterization is possible and that a fully automatic and general purpose NDE system can be implemented. An average orientation error of 10.2 degrees has been achieved and the defect average volume error is 17.5 percent

Application of Adaptive Learning Networks for the Characterization of Two-Dimensional and Three-Dimensional Defects in Solids

The objective of the work was to develop an ultrasonic inversion procedure which (1) discriminates, (2) sizes, and (3) determines the orientation of two-dimensional (crack-like) and· three-dimensional (void-like) defects in materials. Adaptive learning networks (ALN\u27s) were used to estimate directly the defect size and orientation parameters from the spectrum of the echo transient. A 19-element hexagonal synthetic array measured the scattered field within a 60-degree solid angle aperture. The ALN\u27 s were trained on theoretically generated spectral data where the crack forward scattering model was based on the Geometrical Diffraction Theory and the void model was based on the exact Scattering Matrix Theory. The theoretically trained models were evaluated on both theoretical and experimental data. Excellent results were obtained, and the errors for size and odentation estimates were, in general, less than 10%. The significance of this work is that: (1) the ALN approach to defect characteristics provides a systematic procedure for discovering relationships in the data which could otherwise be overlooked, and (2) significant economic benefits can be gained by simulating difficult-to-produce defect reflector scenarios. Furthermore, a result of this work has been the development of an algorithm which can ultimately be applied in field and industrial use

Inversion of Ultrasonic Scattering Data to Measure Defect Size, Orientation, and Acoustic Properties

Empirical solutions via the adaptive learning network methodology have been obtained to measure characteristics of three-dimensional defects (spherical and spheroidal) from the analysis of theoretically-modeled scattered waveforms. The solutions have been successfully applied to measure defects from actually observed ultrasonic scattering data. Spherical voids and inclusions in Ti-6-4, varying in diameter from 0.02 em to 0.12 em, and varying In acoustic impedance ratio !with respect to the host alloy (Ti-6-4)] from zero for air cavities to four for tungsten-carbide inclusions, can be directly measured via: (i) The phase cepstrum - which yields an unambiguous measurement of defect diameter and is independent of its acoustic impedance ratio; (ii) Adaptive Learning Networks (ALN) - synthesized from the amplitude spectrum and which yield accurate measurements of defect diameter and the acoustic impedance ratio of the included material. The two empirical solutions. synthesized from the scattering data from an exact model for spheres, yield similar accurate results when applied to actual scattering observed from the defects. Spheroidal defects (oblate spheroids) varying in aspect ratio from 1.67 to 6, varying in volume from 20 to 310 millionths of a cubic centimeter, and varying in orientation from 0°· to 360° in azimuth and 0° to 90° in elevation, can be measured by adaptive learning networks synthesized from scattering data produced by the Born approximation as the theoretical model. Scattering data used to train the ALNs were obtained via computer simulation. As in the case of spheres, the ALNs were trained-using the synthetic waveforms--to predict the defect size and orientation. Once the empirical models were obtained, eight actual defect sizes and orientations were found via the models and these results compare well with the true values. This paper will describe the means by which the inversion of ultrasonic scattering to defect characteristics was accomplished and its NDE implications

Application of Adaptive Learning Networks to Quantitative Flaw Definition

Adaptive Learning Networks (ALNs) are algebraic, nonlinear multinomials whose structure and coefficients are learned from empirical data. Over the past several years, their application to quantitative NDE problems has become widespread. The major advantage of the ALN approach is that only a modest data base of experiments is needed, from which the ALN models can be trained. In this work, ALNs are used as a nonlinear, empirical inversion procedure for various defect geometries. Measurements from a sparselypopulated ultrasonic transducer array are input to the ALNs which estimate the defect characteristics. The defects considered are (1) elliptical cracks, (2) irregular-shaped voids, and (3) surface-breaking semielliptical cracks. The models are synthesized from theoretically-generated, forward-scattering data, then evaluated on actual experimental data recorded from titanium and carbon steel samples. The advantage of using theoretical data to train the models is that ultrasonic responses can be generated quickly and inexpensively in a digital computer, thereby avoiding, or greatly minimizing, the expense of calibration sample fabrication. The size and orientation estimates for the experimental evaluation are in excellent agreement with the true defect characteristics

Metereological conditions and psychiatric emergency visits in Messina, Italy

Objective: The aim of this study is to examine the association between psychiatric disease, climatic and seasonal factors in patients recorded to the Emergency Unit, in Messina Hospital (Italy). Method: A total of 6565 psychiatric patients were recorded to the Emergency Unit in Messina from January 2005 and December 2010. Each psychiatric visit in emergency, was categorized by date of appearance and admitting diagnosis according to diagnostic categories: Anxiety, Mood Disorders and Psychosis. Local weather data were obtained from the Metereological Instituted "Aereonautica Militare" station in Messina, Sicily, In addition, to gathering data on the state of the sky, temperature, atmosphericpressure with the normalized value at sea level, relative humidity, rainfall, wind direction and speed, the station is connected to a buoy located on the eastern sector of Tyrrhenian Sea. Results: In anxiety disorders we have found relevant results comparison between winter and spring (p=.007) and spring and fall (p=.001). In affective disorders the differences occur in relation to winter and fall (p=.002), spring and fall (p=001), spring and summer (p=002). The psychotic disorder presents significant differences between summer and fall (p=.001) and spring and fall (p=.002). Conclusions: We can observe a similarity of affective disorders, i.e. anxiety and mood disorders compared to psychosis, which have different influences and probably according to dissimilar etiopathogenetic ways. In our research, the distribution of anxiety disorders is higher than depressive disorders in terms of delivering emergency room visits. The major differences occur comparing spring and fall, the seasons when all pathological classes have significant differences. It follows that the most abrupt climate change, typical of these seasons, as a whole, cause psychopathological emergencies. The study is important for planning a more effective assistance for patients needing psychiatric support

Inversion of Physically Recorded Ultrasonic Waveforms Using Adaptive Learning Network Models Trained on Theoretical Data

The objective of this work has been to demonstrate the feasibility of estimating automatically the size and orientation of subsurface defects in metals. The approach has been to (1) obtain computer-generated spectra from various elastic scattering theories, (2) use these spectra to train empirical nonlinear Adaptive Learning Network (ALN) models, and (3) evaluate the theoretically trained ALN's on eight physically recorded defect specimens via a blind test. The results demonstrate that very good defect characterization is possible and that a fully automatic and general purpose NDE system can be implemented. An average orientation error of 10.2 degrees has been achieved and the defect average volume error is 17.5 percent.</p

Inversion of Physically Recorded Ultrasonic Waveforms Using Adaptive Learning Network Models Trained on Theoretical Data

The objective of this work has been to demonstrate the feasibility of estimating automatically the size and orientation of subsurface defects in metals. The approach has been to (1) obtain computer-generated spectra from various elastic scattering theories, (2) use these spectra to train empirical nonlinear Adaptive Learning Network (ALN) models, and (3) evaluate the theoretically trained ALN's on eight physically recorded defect specimens via a blind test. The results demonstrate that very good defect characterization is possible and that a fully automatic and general purpose NDE system can be implemented. An average orientation error of 10.2 degrees has been achieved and the defect average volume error is 17.5 percent.</p

Application of Adaptive Learning Networks for the Characterization of Two-Dimensional and Three-Dimensional Defects in Solids

The objective of the work was to develop an ultrasonic inversion procedure which (1) discriminates, (2) sizes, and (3) determines the orientation of two-dimensional (crack-like) and· three-dimensional (void-like) defects in materials. Adaptive learning networks (ALN's) were used to estimate directly the defect size and orientation parameters from the spectrum of the echo transient. A 19-element hexagonal synthetic array measured the scattered field within a 60-degree solid angle aperture. The ALN' s were trained on theoretically generated spectral data where the crack forward scattering model was based on the Geometrical Diffraction Theory and the void model was based on the exact Scattering Matrix Theory. The theoretically trained models were evaluated on both theoretical and experimental data. Excellent results were obtained, and the errors for size and odentation estimates were, in general, less than 10%. The significance of this work is that: (1) the ALN approach to defect characteristics provides a systematic procedure for discovering relationships in the data which could otherwise be overlooked, and (2) significant economic benefits can be gained by simulating difficult-to-produce defect reflector scenarios. Furthermore, a result of this work has been the development of an algorithm which can ultimately be applied in field and industrial use.</p

Application of Adaptive Learning Networks for the Characterization of Two-Dimensional and Three-Dimensional Defects in Solids

The objective of the work was to develop an ultrasonic inversion procedure which (1) discriminates, (2) sizes, and (3) determines the orientation of two-dimensional (crack-like) and· three-dimensional (void-like) defects in materials. Adaptive learning networks (ALN's) were used to estimate directly the defect size and orientation parameters from the spectrum of the echo transient. A 19-element hexagonal synthetic array measured the scattered field within a 60-degree solid angle aperture. The ALN' s were trained on theoretically generated spectral data where the crack forward scattering model was based on the Geometrical Diffraction Theory and the void model was based on the exact Scattering Matrix Theory. The theoretically trained models were evaluated on both theoretical and experimental data. Excellent results were obtained, and the errors for size and odentation estimates were, in general, less than 10%. The significance of this work is that: (1) the ALN approach to defect characteristics provides a systematic procedure for discovering relationships in the data which could otherwise be overlooked, and (2) significant economic benefits can be gained by simulating difficult-to-produce defect reflector scenarios. Furthermore, a result of this work has been the development of an algorithm which can ultimately be applied in field and industrial use.</p