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Russell, Samuel S.

Publications and source records attributed to Russell, Samuel S..

27 records · Page 2

The Effect of Penetration Depth on Thermal Contrast of NDT by Thermography

Nondestructive evaluation by Thermography (TNDE) is generally classified into two categories, the passive approach and the active approach. The passive approach is usually performed by measuring the natural temperature difference between the ambient and the material or structure to be tested. The active approach, on the other hand, requires the application of an external energy source to stimulate the material for inspection. A laser, a heater, a hot air blower, a high power thermal pulse, mechanical, or electromagnetic energy may provide the energy sources. For the external heating method to inspect materials for defects and imperfection at ambient temperature, a very short burst of heat can be introduced to one of the surfaces or slow heating of the side opposite to the side being observed. Due to the interruption of the heat flow through the defects, the thermal images will reveal the defective area by contrasting against the surrounding good materials. This technique is called transient Thermography, pulse video Thermography, or thermal wave imaging. As an empirical rule, the radius of the smallest defect should be at least one to two times larger than its depth under the surface. Thermography is being used to inspect void, debond, impact damage, and porosity in composite materials. It has been shown that most of the defects and imperfection can be detected. However, the current method of inspection using thermographic technique is more of an art than a practical scientific and engineering approach. The success rate of determining the defect location and defect type is largely depend on the experience of the person who operates thermography system and performs the inspection. The operator has to try different type of heat source, different duration of its application time, as well as experimenting with the thermal image acquisition time and interval during the inspection process. Further-more, the complexity of the lay-up and structure of composites makes it more difficult to determine the optimal operating condition for revealing the defects. In order to develop an optimal thermography inspection procedure, we must understand the thermal behavior inside the material subjected to transient heat in order to interpret the thermal images correctly. Fabrication of finite element models of characteristic defects in composite materials subjected to transient heat will enable the development of appropriate procedure for thermography inspection. Design of phantom defects could be modeled and behavior characterized prior to physically building these test parts. Since production of phantom test parts can be very time consuming and laborious, it is important to design good representative defects.

Chu, Tsuchin Philip

Thermographic Leak Detection of the Space Shuttle Main Engine Nozzle

The Space Shuttle Main Engines Nozzles consist of over one thousand tapered Inconel coolant tubes brazed to a stainless steel structural jacket. Liquid Hydrogen flows through the tubing, from the aft to forward end of the nozzle, under high pressure to maintain a thermal balance between the rocket exhaust and the nozzle wall. Three potential problems occur within the SSME nozzle coolant tubes as a result of manufacturing anomalies and the highly volatile service environment including poor or incomplete bonding of the tubes to the structural jacket, cold wall leaks and hot wall leaks. Of these conditions the identification of cold wall leaks has been the most problematic. The methods and results presented in this summary addresses the thermographic identification of cold wall "interstitial" leaks between the structural jacket and coolant tubes of the Space Shuttle Main Engines Nozzles.

Walker, James L.

Thermographic Qualification of Graphite/Epoxy Instrumentation Racks

A nondestructive evaluation method is desired for ensuring the 'as manufactured' and 'post service' quality of graphite/epoxy instrumentation rack shells. The damage tolerance and geometry of the racks dictate that the evaluation method be capable of identifying defects, as small as 0.25 inch 2 in area, over large acreage regions, tight compound radii and thickness transition zones. The primary defects of interest include voids, inclusions, delaminations and porosity. The potential for an infrared thermographic inspection to replace ultrasonic testing, for qualifying the racks as 'defect free' is under investigation. The inspection process is validated by evaluating defect standard panels built to the same specifications as the racks, except for the insertion of artificial fabricated defects. The artificial defects are designed to match those which are most prevalent in the actual instrumentation racks. A target defect area of 0.0625 inch 2 (a square with 0.25 inch on a side) was chosen for the defect standard panels to ensure the ability to find all defects of the critical (0.25 inch squared) size.

Walker, James L.

Detection, Location, and Classification of Space Shuttle Main Engine Nozzle Leaks by Transient Thermographic Inspection

Leak checking and evaluation of pressure vessels by observing the slight temperature changes resulting from structural anomalies has been made possible through developments in high resolution infrared cameras and advanced image processing. These developments have made thermal nondestructive analysis a very practical and efficient method to determine material consistency and structural quality as well as monitor processes. The Space Shuttle Main Engine Nozzle has regions which can not be inspected with standard leak check methods. The Thermographic methods being developed to nondestructively test the Nozzle for leaks in inaccessible regions are reported. Also, a flash heating Thermographic investigation of the braze line bonding the cooling tubes to the outer structural jacket of the nozzle is reported.

Russell, Samuel S.

Neural Network Prediction of Failure of Damaged Composite Pressure Vessels from Strain Field Data Acquired by a Computer Vision Method

This effort used a new and novel method of acquiring strains called Sub-pixel Digital Video Image Correlation (SDVIC) on impact damaged Kevlar/epoxy filament wound pressure vessels during a proof test. To predict the burst pressure, the hoop strain field distribution around the impact location from three vessels was used to train a neural network. The network was then tested on additional pressure vessels. Several variations on the network were tried. The best results were obtained using a single hidden layer. SDVIC is a fill-field non-contact computer vision technique which provides in-plane deformation and strain data over a load differential. This method was used to determine hoop and axial displacements, hoop and axial linear strains, the in-plane shear strains and rotations in the regions surrounding impact sites in filament wound pressure vessels (FWPV) during proof loading by internal pressurization. The relationship between these deformation measurement values and the remaining life of the pressure vessels, however, requires a complex theoretical model or numerical simulation. Both of these techniques are time consuming and complicated. Previous results using neural network methods had been successful in predicting the burst pressure for graphite/epoxy pressure vessels based upon acoustic emission (AE) measurements in similar tests. The neural network associates the character of the AE amplitude distribution, which depends upon the extent of impact damage, with the burst pressure. Similarly, higher amounts of impact damage are theorized to cause a higher amount of strain concentration in the damage effected zone at a given pressure and result in lower burst pressures. This relationship suggests that a neural network might be able to find an empirical relationship between the SDVIC strain field data and the burst pressure, analogous to the AE method, with greater speed and simplicity than theoretical or finite element modeling. The process of testing SDVIC neural network analysis and some encouraging preliminary results are presented in this paper. Details are given concerning the processing of SDVIC output data such that it may be used as back propagation neural network (BPNN) input data. The software written to perform this processing and the BPNN algorithm are also discussed. It will be shown that, with limited training, test results indicate an average error in burst pressure prediction of approximately six percent,

Russell, Samuel S.

Computing Displacements And Strains From Video Images

Subpixel digital video image correlation (SDVIC) technique for measuring in-plane displacements on surfaces of objects under loads, without contact. Used for analyses of experimental research specimens or actual service structures of virtually any size or material. Only minimal preparation of test objects needed, and no need to isolate test objects from minor vibrations or fluctuating temperatures. Technique implemented by SDVIC software, producing color-graduated, full-field representations of in-plane displacements and partial derivatives with respect to position along both principal directions in each image plane. From representations, linear strains, shear strains, and rotation fields determined. Written in C language.

Russell, Samuel S.

A Neural Network/Acoustic Emission Analysis of Impact Damaged Graphite/Epoxy Pressure Vessels

Acoustic emission (AE) signal analysis has been used to measure the effects of impact damage on burst pressure in 5.75 inch diameter, inert propellant filled, filament wound pressure vessels. The AE data were collected from fifteen graphite/epoxy pressure vessels featuring five damage states and three resin systems. A burst pressure prediction model was developed by correlating the AE amplitude (frequency) distribution, generated during the first pressure ramp to 800 psig (approximately 25% of the average expected burst pressure for an undamaged vessel) to known burst pressures using a four layered back propagation neural network. The neural network, trained on three vessels from each resin system, was able to predict burst pressures with a worst case error of 5.7% for the entire fifteen bottle set.

Walker, James L.

Determination of high temperature strains using a PC based vision system

With the widespread availability of video digitizers and cheap personal computers, the use of computer vision as an experimental tool is becoming common place. These systems are being used to make a wide variety of measurements that range from simple surface characterization to velocity profiles. The Sub-Pixel Digital Image Correlation technique has been developed to measure full field displacement and gradients of the surface of an object subjected to a driving force. The technique has shown its utility by measuring the deformation and movement of objects that range from simple translation to fluid velocity profiles to crack tip deformation of solid rocket fuel. This technique has recently been improved and used to measure the surface displacement field of an object at high temperature. The development of a PC based Sub-Pixel Digital Image Correlation system has yielded an accurate and easy to use system for measuring surface displacements and gradients. Experiments have been performed to show the system is viable for measuring thermal strain.

Mcneill, Stephen R.

Image correlation nondestructive evaluation of impact damage in a glass fiber composite

Presented in viewgraph format, digital image correlation, damage in fibrous composites, and damaged coupons (cross-ply scotchply GI-Ep laminate) are outlined. It was concluded that the image correlation accuracy was 0.03 percent; strains can be processed through Tsai-Hill failure criteria to qualify the damage; the statistical data base must be generated to evaluate certainty of the damage estimate; size effects need consideration; and better numerical techniques are needed.

Russell, Samuel S.