7,726 research outputs found

    Pattern Recognition for Nondestructive Evaluation

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    The issues involved in automating nondestructive evaluation (NDE) techniques are outlined. Attention is given to research focused on the application of machine learning techniques to the construction and maintenance of knowledge-based systems which are capable of evaluating the readings from nondestructive tests that have been performed on aircraft components. Preliminary results obtained from this research are described. In particular, the authors discuss the application of a symbolic machine learning algorithm, ID3, to the NDE problem. ID3 has been used by Douglas Aircraft to classify defects in sets of standard NDE reference blocks. Based on the preliminary results, a need for an improved method of distinguishing features in the test waveforms is identified. The authors also outline a feature extraction approach from pattern recognition, called scale-space filtering, which can be used to preprocess data for input into a classification algorithm such as ID3

    Shape-based defect classification for Non Destructive Testing

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    The aim of this work is to classify the aerospace structure defects detected by eddy current non-destructive testing. The proposed method is based on the assumption that the defect is bound to the reaction of the probe coil impedance during the test. Impedance plane analysis is used to extract a feature vector from the shape of the coil impedance in the complex plane, through the use of some geometric parameters. Shape recognition is tested with three different machine-learning based classifiers: decision trees, neural networks and Naive Bayes. The performance of the proposed detection system are measured in terms of accuracy, sensitivity, specificity, precision and Matthews correlation coefficient. Several experiments are performed on dataset of eddy current signal samples for aircraft structures. The obtained results demonstrate the usefulness of our approach and the competiveness against existing descriptors.Comment: 5 pages, IEEE International Worksho

    Automatic detection of welding defects using the convolutional neural network

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    Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects

    A Reconstruction Procedure for Microwave Nondestructive Evaluation based on a Numerically Computed Green's Function

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    This paper describes a new microwave diagnostic tool for nondestructive evaluation. The approach, developed in the spatial domain, is based on the numerical computation of the inhomogeneous Green’s function in order to fully exploit all the available a-priori information of the domain under test. The heavy reduction of the computational complexity of the proposed procedure (with respect to standard procedures based on the free-space Green’s function) is also achieved by means of a customized hybrid-coded genetic algorithm. In order to assess the effectiveness of the method, the results of several simulations are presented and discussed

    ARMD Workshop on Materials and Methods for Rapid Manufacturing for Commercial and Urban Aviation

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    This report documents the goals, organization and outcomes of the NASA Aeronautics Research Mission Directorates (ARMD) Materials and Methods for Rapid Manufacturing for Commercial and Urban Aviation Workshop. The workshop began with a series of plenary presentations by leaders in the field of structures and materials, followed by concurrent symposia focused on forecasting the future of various technologies related to rapid manufacturing of metallic materials and polymeric matrix composites, referred to herein as composites. Shortly after the workshop, questionnaires were sent to key workshop participants from the aerospace industry with requests to rank the importance of a series of potential investment areas identified during the workshop. Outcomes from the workshop and subsequent questionnaires are being used as guidance for NASA investments in this important technology area
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