A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels

Welding production has a pivotal role in the modern manufacturing industry. However, welding defects are frequently generated during the complex welding production process which will bring a certain effect to the welding quality. Therefore, the issue of welding defect detection has received considerable critical attention. However, traditional methods, based on handcrafted features or shallow-learning techniques could only detect welding defects under specific detection conditions or priori knowledge. In this paper, to serve the evaluation of the harmfulness of welding defects to different objects, based on the strong feature expression ability of deep learning, an automatic welding defect location method is proposed based on the improved U-net network from digital X-ray images which includes data augmentation and welding defect location. To acquire better location performance, the data augmentation is realized to enlarge the data set of welding defects to serve the network training. On the basis, a defect location method based on the improved U-net network is proposed to realize automatic and high-precision welding defect location. Experiments show that the proposed method could acquire the detection precision up to 88.4 % on the public data set (GDXray Set) which shows a remarkable location performance compared with other related detection methods.

An automatic welding defect location algorithm based on deep learning

Manufacturing is undergoing transformation driven by the developments in process technology, information technology, and data science. A future manufacturing enterprise will be highly digital. This...

Convolutional and generative adversarial neural networks in manufacturing

A vision-based method for lap weld defects monitoring of galvanized steel sheets using convolutional neural network

Defect detection in welding radiographic images based on semantic segmentation methods

In this paper, we propose a convolutional neural network (CNN) based method that inspects non-patterned welding defects (craters, pores, foreign substances and fissures) on the surface of the engine transmission using a single RGB camera. The proposed method consists of two steps: first, extracting the welding area to be inspected from the captured image, and then determining whether the extracted area includes defects. In the first step, to extract the welding area from the captured image, a CNN based approach is proposed to detect a center of the engine transmission in the image. In the second stage, the extracted area is identified by another CNN as defective or non-defective. To train the second stage CNN stably, we propose a class-specific batch sampling method. With our sampling method, biased learning caused by data imbalance (number of collected defective images is much less than that of non-defective images) is effectively prevented. We evaluated our system with a large amount of samples (about 32,000 images) collected manually from the production line, and our system shows a remarkable performance in all experiments.

Convolutional Neural Network Based Surface Inspection System for Non-patterned Welding Defects

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Convolutional Neural Network Based Surface Inspection System for Non-patterned Welding Defects