In-Situ NDE of Navy Sonar Domes Via X-Ray Backscatter Tomography

X-ray backscatter tomography (XBT) is a relatively new NDE technology which is quantitative in its ability to detect a flaw location in three dimensions. The volume to be inspected is interrogated by a collimated x-ray beam and one or more collimated detectors to measure the Compton scatter signal produced by each volume element. XBT is particularly useful where access is available only to one side of the object. Although a number of novel backscatter inspection techniques have been demonstrated [1–4], there is a notable dearth of real applications. This can be attributed to both the development of other, lower cost, one-sided methods and the lack, until recently, of a commercial XBT scanner. In applications where the low cost alternatives are inferior or unfeasible and failure costs are high, XBT affords a solution

A Practical Algorithm for Reconstruction From X-Ray Backscatter Data

Although numerous applications of x-ray backscatter tomography (XBT) have been demonstrated, only a few have been fully developed to practical implementation [1–5]. In some applications the images produced by direct data acquisition and display methods are plagued with superposition artifacts that can interfere with interpretation [6]. Non-homogeneous materials such as composites or layered structures are particularly susceptible. Reconstruction methods have been proposed to correct the datum from each volume element (voxel) by exploiting the information in data from overlying voxels [7]. Practical inspection systems, however, present a more challenging problem than the monoenergetic highly collimated laboratory demonstration systems. In particular, the use of a bremmstrahlung source and a fan beam, or slit collimated, detector geometry, deprives us of knowledge of the backscattered photon energies and paths that are needed for a true reconstruction. In this paper, we present our work towards a reconstruction using data from a commercial XBT system (Philips ComScan) and a real composite inspection application. Our approach uses pre-processing to remove system artifacts, a priori information about the material, and an iterative method to determine the composition of each voxel.</p

Status and Future Aspects of X-Ray Backscatter Imaging

Since the market introduction of the commercial system ComScan 160 [1] X-ray backscatter imaging has become an established inspection technique in certain areas of nondestructive testing, e.g. corrosion inspection on aircrafts. Several preceding publications on X-ray backscatter imaging have been focussed on the current status of the ComScan system and on topical applications [2,3,4]. In the present article the horizon shall be opened to all relevant results which have been obtained worldwide with X-ray backscatter techniques. Due to space limitations it is certainly not possible to give a complete overview, but some selected results will be reported. In reference [5] additional information and many references to this topic can be found. Furthermore, in that work reference is also given to the patent situation. Additionally to this, an overview on the history of X-ray backscatter techniques, on physical and technical foundations of the techniques and its numerous variations will be given in chapter 3.1.5 of the to-be-published handbook on NDT [6] (in German).</p

“燭影斧聲”事件考索

Artificial neural networks have been studied over a 30 year period and are a well developed computational technology applicable to a variety of difficult problems [1]. All neural networks are simulations of neurons and synapses based upon a primitive understanding of these biological structures. The distinctive feature of these networks is that they are trainable. By various iterative schemes, a set of well characterized data can be used to create a network which will produce a correct output function of an input vector. The learning is generalized, resulting in the ability to provide correct results for input vectors not contained in the training data. The term neural network has become nearly synonymous with a particular type: the feed-forward backpropagation neural network. We will use the term network in that sense here