331 research outputs found

    Experimental Validation of a Phased Array Ultrasonic Testing Probe Model and Sound Field Optimization

    Get PDF
    New manufacturing technologies are developed to facilitate flexible product designs and production processes. However, the quality of the final products should not be compromised, especially for safety prioritized industries, e.g. aerospace industry. The assessment of product quality and integrity lies on various nondestructive inspection methods and the ultrasonic testing method, among others, is widely used as an effective approach. The phased array technique in the ultrasonic testing area shows more advantages comparing to conventional ones and is revealing more benefits to industrial applications. To incorporate new technique into practical operations, it needs to be qualified with practical experiments. Due to the extensive costs and considerable challenges with experimental works, the necessity of researching on numerical simulation models arises and several models had therefore been developed. The numerical simulation model implemented in the software, simSUNDT, developed at the Scientific Center of NDT (SCeNDT) at Chalmer University of Technology is one of these models for ultrasonic inspection. However, the validity of the models should be proved before supporting or replacing the experiments, and this validation work should be accomplished by experiments ultimately.In the current work, the main purpose is to further validate the phased array probe model in simSUNDT by comparing simulation results with corresponding experiments. An experimental platform is built with the intention to fully control the operation conditions and the set of testing results. Well-defined artificial defects in test specimens are considered in both simulations and experiments. Comparisons in the end validate the current phased array probe model and could be treated as an alternative to experiments.With the aid of this validated probe model, optimization of the generated sound field from a phased array probe is then conducted. The optimization aims at searching for a proper combination of main beam angle and focus distance of the probe at this stage, so that the echo amplitude from a certain defect reaches its potential maximum

    Experimental Validation and Applications of a Phased Array Ultrasonic Testing Probe Model

    Get PDF
    New manufacturing technologies are developed to facilitate flexible product designs and production processes. However, the quality of the final products should not be compromised. The assessment of product quality and integrity lies on various inspection methods. Ultrasonic testing, among other nondestructive testing methods, is widely used as an effective and cost-efficient approach. The phased array technique in the field of ultrasonic testing shows more advantages over conventional technique and is revealing more benefits to industrial applications. To incorporate new technique into practice, it needs to be qualified with experiments. Due to the extensive costs and considerable challenges in experiments, the necessity of researching on reliable numerical models arises and several models had therefore been developed. The mathematical model implemented in the software, simSUNDT, developed at the Scientific Center of NDT (SCeNDT) at Chalmers University of Technology is one of these models for ultrasonic inspection. However, the validity of the models should be proved before supporting or replacing some of the experiments, which should ultimately be accomplished by experiments.In this thesis, the main purpose is thus to further validate the phased array probe model in simSUNDT by comparing simulation with corresponding experiments. An experimental platform is built to fully control the operation conditions and the set of testing results. Well-defined and representative artificial defects in test specimens are manufactured and inspected under some inspection cases in both simulations and experiments. Comparisons in the end show good correlations.Upon validation of the probe model, it is used in several application attempts for possible technique developments. This includes optimization of the generated sound field from a phased array probe and verifying the validity of the used log-normal probability of detection model. The basic ability of generating full matrix capture inspection dataset is also explored. This could provide a simulation scheme for parametric studies to investigate an ultrasonic imaging algorithm, total focusing method, in terms of its defect characterization capabilities

    Using Augmented Reality to Cognitively Facilitate Product Assembly Process

    Get PDF

    Learning Discriminative Features with Class Encoder

    Full text link
    Deep neural networks usually benefit from unsupervised pre-training, e.g. auto-encoders. However, the classifier further needs supervised fine-tuning methods for good discrimination. Besides, due to the limits of full-connection, the application of auto-encoders is usually limited to small, well aligned images. In this paper, we incorporate the supervised information to propose a novel formulation, namely class-encoder, whose training objective is to reconstruct a sample from another one of which the labels are identical. Class-encoder aims to minimize the intra-class variations in the feature space, and to learn a good discriminative manifolds on a class scale. We impose the class-encoder as a constraint into the softmax for better supervised training, and extend the reconstruction on feature-level to tackle the parameter size issue and translation issue. The experiments show that the class-encoder helps to improve the performance on benchmarks of classification and face recognition. This could also be a promising direction for fast training of face recognition models.Comment: Accepted by CVPR2016 Workshop of Robust Features for Computer Visio

    S3^3FD: Single Shot Scale-invariant Face Detector

    Full text link
    This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3^3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchor-based detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects: 1) proposing a scale-equitable face detection framework to handle different scales of faces well. We tile anchors on a wide range of layers to ensure that all scales of faces have enough features for detection. Besides, we design anchor scales based on the effective receptive field and a proposed equal proportion interval principle; 2) improving the recall rate of small faces by a scale compensation anchor matching strategy; 3) reducing the false positive rate of small faces via a max-out background label. As a consequence, our method achieves state-of-the-art detection performance on all the common face detection benchmarks, including the AFW, PASCAL face, FDDB and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images.Comment: Accepted by ICCV 2017 + its supplementary materials; Updated the latest results on WIDER FAC
    corecore