A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system

Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding

Intelligent welding system technologies: State-of-the-art review and perspectives

Due to ever increasing demand in precision in robotic welding automation and its inherent technical difficulties, seam tracking has become the research hotspot. This paper introduces the research in application of computer vision technology for real-time seam tracking in robotic gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW). The key aspect in using vision techniques to track welding seams is to acquire clear real-time weld images and to process them accurately. This is directly related to the precision of seam tracking. In order to further improve the accuracy of seam tracking, in this paper, a set of special vision system has been designed firstly, which can acquire clear and steady real-time weld images. By analyzing the features of weld images, a new and improved edge detection algorithm was proposed to detect the edges in weld images, and more accurately extract the seam and pool characteristic parameters. The image processing precision was verified through the experiments. Results showed that the precision of this vision based tracking technology can be controlled to be within ?0.17mm and ?0.3mm in robotic GTAW and GMAW, respectively. Using computer vision technology, we have solved some problems of seam tracking in robotic welding automation.We have designed a set of special vision sensor system for seam tracking and it has worked very well.A new improved Canny edge detection algorithm was proposed to detect the edges of weld image.We have further improved the precision of image processing in seam tracking.This paper systematically studied the application of computer vision technology in robotic GTAW and GMAW.

Computer vision technology for seam tracking in robotic GTAW and GMAW

A wire-arc additive manufacturing (WAAM) system is used to fabricate iron rich Fe–Al intermetallics with 25 at% aluminum content. The alloy is produced in situ through controlled addition of the elemental iron and aluminum components into the welding process. The properties of the fabricated material are assessed using optical microstructure analysis, hardness testing, tensile testing, X-ray diffraction phase characterization and electron dispersive spectrometry. It is shown that the WAAM system is capable of producing iron rich Fe–Al intermetallics with higher yield strength and similar room temperature ductility when compared to equivalent materials produced using powder metallurgy.

Fabrication of iron-rich Fe–Al intermetallics using the wire-arc additive manufacturing process

This paper used multi-sensor information fusion technology in pulsed gas tungsten arc welding. Arc sensor, visual sensor, and sound sensor were used simultaneously to obtain weld current, voltage, weld pool image, and weld sound information about the pulsed gas tungsten arc welding process, and special algorithms were designed to extract the respective signal features of different sensors’ information. Then D-S evidence theory was used to fuse the different signal features to predict the penetration status about the welding process. Aimed at the difficulty of obtaining basic probability assignment in D-S evidence theory, back-propagation (BP) neural network was used to obtain the basic probability assignment. Experiments were done to obtain data for training the BP neural network and test the prediction reliability of D-S evidence theory information fusion, and comparison results showed that D-S evidence theory could effectively use the information obtained by different sensors and obtain better prediction result than single sensor.

Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion.

Purpose – The purpose of this paper is to analyze the characteristics of sound during gas tungsten argon welding (GTAW), which is very important to effectively monitor the welding quality in future by using the information extracted from sound.Design/methodology/approach – The hardware used in the experiment is described. Then the paper researches the influence of welding techniques (gas flow, welding speed, welding current, and arc length) on arc sound and the distribution of the welding sound field. Finally, the relation between welding power and sound are studied based on Fourier transforms and recursive least square methods.Findings – The sound pressure is affected greatly by gas flow, arc length, and current; welding sound source obeys the dipole model; the sound can be better predicted when the three orders derivative of the welding power are combined together.Originality/value – This paper provides a new insight into welding sound resource model and a detailed analysis of the influence of the weldi...

Analysis of arc sound characteristics for gas tungsten argon welding

To improve the prediction results of fusing multi-sensor information in pulsed GTAW, a new method for obtaining the basic probability assignment of D-S evidence theory was proposed, the method was based on fuzzy membership functions and BP network. Experiment results showed that this method could improve the prediction accuracy than single BP network model, and so as to improve the prediction results of multi-sensor information fusion.

A study on application of multi‐sensor information fusion in pulsed GTAW

Purpose – The purpose of this paper is to study the relationship between arc sound signal and arc height through arc sound features of GTAW welding, which is aimed at laying foundation work for monitoring the welding penetration and quality by using the arc sound signal in the future.Design/methodology/approach – The experiment system is based on GTAW welding with acoustic sensor and signal conditioner on it. The arc sound signal was first processed by wavelet analysis and wavelet packet analysis designed in this research. Then the features of arc sound signal were extracted in time domain, frequency domain, for example, short‐term energy, AMDF, mean strength, log energy, dynamic variation intensity, short‐term zero rate and the frequency features of DCT coefficient, also the wavelet packet coefficient. Finally, a ANN (artificial neural networks) prediction model was built up to recognize different arc height through arc sound signal.Findings – The statistic features and DCT coefficient can be absolutely ...

Audio sensing and modeling of arc dynamic characteristic during pulsed Al alloy GTAW process

Purpose – Welding process is a complicated process influenced by many interference factors, a single sensor cannot get information describing welding process roundly. This paper simultaneously uses different sensors to get different information about the welding process, and uses multi‐sensor information fusion technology to fuse the different information. By using multi‐sensors, this paper aims to describe the welding process more precisely.Design/methodology/approach – Electronic and welding pool image information are, respectively, obtained by arc sensor and image sensor, then electronic signal processing and image processing algorithms are used to extract the features of the signals, the features are then fused by neural network to predict the backside width of weld pool.Findings – Comparative experiments show that the multi‐sensor fusion technology can predict the weld pool backside width more precisely.Originality/value – The multi‐sensor fusion technology is used to fuse the different information o...

Bo Chen

Papers

Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion.

Analysis of arc sound characteristics for gas tungsten argon welding

A study on application of multi‐sensor information fusion in pulsed GTAW

Audio sensing and modeling of arc dynamic characteristic during pulsed Al alloy GTAW process

Modeling of pulsed GTAW based on multi‐sensor fusion