May 1, 2021 · Machine learning methods like convolutional neural networks enable computer-aided defect detection in additive manufacturing. Transfer learning methods enable classification of powder bed defects during selective laser sintering using small datasets.

Dec 10, 2024 · research focused solely on classification methods in addressing defect detections. This research will extend the knowledge around defect detection by combining classification and segmentation approach to build a robust system.

Mar 27, 2018 · An intelligent defect inspection and classification system based on deep learning algorithm has been developed. The artificial network system was constructed with ZF-Net neural model to perform defect image training and evaluation.

To address these issues, we propose to use a multitask deep one-class CNN for defect classification. Compared with supervised classification methods, this CNN does not require abnormal images and annotated data for training. Specifically, we build a stacked encoder–decoder autoencoder for learning feature representation from normal images.

First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and …

2 days ago · The application of Machine Learning (ML)-based technologies, especially Convolutional Neural Networks (CNN), offered an innovative approach to overcome these challenges. CNN had the ability to automatically extract visual features from images with high accuracy, making it an effective tool in defect classification.

Jan 1, 2020 · In this paper, we evaluate the use of machine learning algorithms (k-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Nearest Centroid, Random Forest and Recurrent Neural Networks) for automatic classification of software defects using ODC, based on unstructured textual bug reports.

The issues in this project study are data m odeling, Machine Learning (ML) model - neural networks (NN) modeling and reliability of such models for automatic detection and classification of defects of hot stri ps.

Oct 8, 2008 · A set of surface flaw detection algorithms based on machine learning including object segmentation, flaw feature extraction, and classification and size calibration of flaw are proposed to ...

Automatic defect classification (ADC) can reduce the number of defect images that need to be reviewed by operators. The ADC process can also be integrated with AOI engines to reduce nuisance defect images to reduce AOI image capturing time.