Journal Article•
Developing a gtaw penetration control system for the titan iv program
About: This article is published in Welding and metal fabrication.The article was published on 1998-01-01 and is currently open access. It has received 19 citations till now.
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TL;DR: A feature-level data fusion methodology was presented to automatically evaluate seam quality in real time for Al alloy in gas tungsten arc welding by means of online arc sound, voltage and spectrum signals, which indicates that multisensory-based classifier has higher accuracy than single sensor-based one.
Abstract: Sensor technology application is the key for intelligent welding process. Multiple sensors fusion has shown their significant advantages over single sensor which can only provide limited information. In this paper, a feature-level data fusion methodology was presented to automatically evaluate seam quality in real time for Al alloy in gas tungsten arc welding by means of online arc sound, voltage and spectrum signals. Based on the developed algorithms in time and frequency domain, multiple feature parameters were successively extracted and selected from sound and voltage signals, while spectrum distribution of argon atoms related to seam penetration were carefully analyzed before feature parameters selection. After the synchronization of heterogeneous feature parameters, the feature-level-based data fusion was conducted by establishing a classifier using support vector machine and 10-fold cross validation. The test results indicate that multisensory-based classifier has higher accuracy i.e., 96.5873 %, than single sensor-based one in term of recognizing seam defects, like under penetration and burn through from normal penetration.
69 citations
TL;DR: Wang et al. as mentioned in this paper used multi-sensor information fusion technology in pulsed gas tungsten arc welding process, and special algorithms were designed to extract the respective signal features of different sensors' information.
Abstract: 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.
65 citations
Cites methods from "Developing a gtaw penetration contr..."
...To control the welding quality precisely, welding status information should first be obtained, and so various welding sensors have been developed such as arc sensor [1], visual sensor [2–5], temperature sensor [6], acoustic emission sensor [7], and so on....
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TL;DR: Wang et al. as discussed by the authors proposed a feature selection approach, i.e., hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions.
64 citations
TL;DR: In this article, a set of arc sensor system for height tracking of weld seam, which can effectively acquire arc voltage signals, was designed for teaching-playback robot gas tungsten argon welding (GTAW).
Abstract: Aiming at the deficiencies of the vision technology that canot be used in the height tracking of the 3D weld seam during teaching-playback robot gas tungsten argon welding (GTAW) process, this paper designed a set of arc sensor system for height tracking of weld seam, which can effectively acquire arc voltage signals. The characteristic values of arc voltage signals are extracted and a linear relational model between arc voltage and arc length was established by using appropriate denoising algorithm. The experimental results demonstrate that the error between the arc length calculated by linear model and the real arc length was very small, which is accurate enough to meet the requirements of the follow-up height tracking and controlling during the welding robot GTAW process.
53 citations
TL;DR: A set of composite sensor system for tracking the three-dimensional welding seam based on visual sensor and arc sensor technology, which can effectively acquire three- dimensional welding seam information, such as clear images of seam and pool and stable arc voltage signals.
Abstract: Aiming at the shortcomings of teaching-playback robot that can't track the three-dimensional welding seam in real time during GTAW process, this paper designed a set of composite sensor system for tracking the three-dimensional welding seam based on visual sensor and arc sensor technology, which can effectively acquire three-dimensional welding seam information, such as clear images of seam and pool and stable arc voltage signals. The characteristic values of weld image and arc voltage signals were accurately extracted by using proper processing algorithm, and the experiments have been done to verify the precision of processing algorithms. The results demonstrate that the error is very small, which is accurate enough to meet the requirements of the subsequent real-time tracking and controlling during the welding robot GTAW process.
44 citations