Detection of flaws in close proximity using convolutional neural ...

The machine learning algorithm was trained using finite element simulations and was then tested on experimental measurements. The convolutional ...

Full article: A Convolutional Neural Network for Detecting Faults in ...

The main drawback of using drones is to modify the image's quality due to external factors such as distance, balance, excessive vibration, or low lighting. Also ...

Convolutional Neural Network for Interface Defect Detection ... - MDPI

This study presents an ultrasonic non-destructive method with convolutional neural networks (CNN) used for the detection of interface defects in adhesively ...

Neural network classification of flaws detected by ultrasonic means

For example, both Fisher linear discriminant and three-layered neural network are applied in [5] for the detection of welding defects in steel plates. The input ...

Ultrasound classification of interacting flaws using finite element ...

For example, Niu and Srivastava [154, 155] used FEA-trained CNN to accurately identify internal cracks from ultrasonic measurements ( Fig. 3(b i )) ...

Automating Visual Inspection with Convolutional Neural Networks

detection on aircraft and experimentally discuss its performance against ... Detecting Defects in PCB using Deep Learning via Convolution Neural Networks,.

Small Defect Detection Using Convolutional Neural Network ...

We assume that we have a training set containing example images each con- taining typical defects, together with binary label images indicating the pixels.

Defects detection of GMAW process based on convolutional neural ...

Convolution neural network (CNN) is a kind of feedforward neural network with deep structure, which gives better results in image classification ...

Deconvolution of ultrasonic signals using a convolutional neural ...

The originality of the proposed framework consists in its training of the neural network using data generated in simulations. The framework was validated ...

[1808.02518] Detection and Segmentation of Manufacturing Defects ...

Recently, Convolutional Neural Networks (CNNs) have shown outstanding performance in both image classification and localization tasks. In this ...

Ultrasound classification of interacting flaws using finite element ...

... close proximity and ... Ultrasound classification of interacting flaws using finite element simulations and convolutional neural network.

novel deep convolutional neural network algorithm for surface defect ...

A convolution neural network framework is designed for surface defect detection in complex industrial scenes. A dense cross-stage partial Darknet backbone ...

Deep learning-based detection, classification, and localization of ...

In particular, we train convolutional neural network-based models using ... defects and achieve robust defect detection and classification ...

Deconvolution of ultrasonic signals using a convolutional neural ...

... In [7], CNN is used for multi-class detection of ultrasonic flaw/defect echoes in presence of high clutter noise. The deconvolution method in ...

Automated Flaw Detection in Multi-channel Phased Array Ultrasonic ...

There's a clear trend in the industry to even richer data sets with full matrix capture (FMC) and related techniques. Convolutional neural ...

IIOT fault detection using multi deep convolutional neural networks

The proposed MDCNN architecture effectively processes multi-modal sensor input with spatial and temporal interdependence model may detect small flaws or ...

Deconvolution of ultrasonic signals using a convolutional neural ...

Successfully employing ultrasonic testing to distinguish a flaw in close proximity to another flaw or geometrical feature depends on the wavelength and the ...

Boosting Defect Detection in Manufacturing using Tensor ... - arXiv

In this work, we introduce a Tensor Convolutional Neural Network (T-CNN) and examine its performance on a real defect detection application in one of the ...

Detection and Classification of Rolling Bearing Defects Using Direct ...

The selected structure of the convolutional neural network is characterized by the high detection of damage and the assessment of the degree of fault. In ...

Flaw Detection on Surface-treated Steels Using Convolutional ...

The model learned to identify and label the pores and layers as evidenced in Figure 2(a), (b) and (c) as a function of the patterns present in ...