Use of sensors in robotic welding for controlling the weld quality leads to replacement of manual welding operation in dangerous work environment in presence of high temperature and fumes even in small or medium scale enterprises. The seam tracking operation is very essential for extracting weld seam position which can be fed to robot controller for instructing robot along the weld seam path. The seam tracking operation can be executed by different types of sensors having their own merits and demerits. In this paper, different sensors and techniques used for seam tracking task in robotic welding have been discussed in detail. Each sensor has different method or technology of weld seam feature extraction which have been described by different authors in different ways. The chief tasks for seam tracking have been found to be weld starting and end point detection, weld edge detection, joint width measurement and weld path position determination with respect to robot co-ordinate frame. Thus sensors have a very important role in robotic welding for fully automating the system with in process real time monitoring of weld process parameters with the sensor feedback. In further discussion the practical use of different sensors in industries with a comparison of their advantages and disadvantages have been discussed. This Paper presents the role of sensors in robotic welding and a detail study of methodologies of weld seam position and geometry feature extraction by different sensors typically used for weld seam tracking.

Advances in weld seam tracking techniques for robotic welding: A review

A novel system which allows arc-welding defect detection and classification is presented in this paper. The spectroscopic analysis of the plasma spectra produced during the welding process is a well-known technique to monitor the quality of the resulting weld seams. The analysis of specific emission lines and the subsequent estimation of the electronic temperature T e profile offers a direct correlation between this parameter and the corresponding weld seams. However, the automatic identification and classification of weld defects has proven to be difficult, and it is usually performed by means of statistical studies of the electronic temperature profile. In this paper, a new approach that allows automatic weld defect detection and classification based in the combined use of principal component analysis (PCA) and an artificial neural network (ANN) is proposed. The plasma spectra captured from the welding process is processed with PCA, which reduces the processing complexity, by performing a data compression in the spectral dimension. The designed ANN, after the selection of a proper data training set, allows automatic detection of weld defects. The proposed technique has been successfully checked. Arc-weld tests on stainless steel are reported, showing a good correlation between the ANN outputs and the classical interpretation of the electronic temperature profile.

Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks

Real-time seam tracking control technology during welding robot GTAW process based on passive vision sensor

Selective laser melting (SLM) is one of the most important and successfully additive manufacturing processes in 3D metal printing technologies. Critical quality issues such as porosity, surface roughness, crack, and delamination continue to present challenges within SLM-manufactured parts. Monitoring and in-process defect diagnosis are the key to improving the final part quality. Currently, it greatly hinders the adaptability and the development within the defect detection system since the setup restricts the vision and photo diode applications in the SLM process monitoring. Additionally, defect detection with traditional classification approaches makes the system rather complex due to introducing a series of steps. To meet these needs, this study proposes a novel method for the defect detection within the SLM parts. The setup was flexibly conducted using a microphone, and the defect detection was obtained by the framework of deep belief network (DBN). It is implemented by a simplified classification structure without signal preprocessing and feature extraction. The experimental results showed that the utilization of acoustic signals was workable for quality monitoring, and the DBN approach could reach high defect detection rate among five melted states without signal preprocessing.

Defect detection in selective laser melting technology by acoustic signals with deep belief networks

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

The automatic control in square-wave alternating current (AC) gas tungsten arc welding (GTAW) is significant for a “teach and playback” robot to overcome the variation of the seam trajectory and the seam gap in the welding process. This paper presents a welding robot system based on the real-time visual measurement in the different levels of the welding current. The primary objective is to measure the offset of the torch to the seam center and the size of seam gap by passive vision, track the seam and control the weld pool in real time. A novel visual image analysis algorithm was developed for seam tracking and seam gap measuring, free from robot calibration. The control algorithm based on the knowledge base was established to control the weld formation by regulating the welding current and wire feed rate. The welding practice for the rocket storage tank demonstrates the efficiency of the proposed system.

https://works.bepress.com/jing-wu/4/download/

Arc welding robot system with seam tracking and weld pool control based on passive vision

A seam tracking system with visual sensing free from calibration was developed for the robot applied in gas tungsten arc welding. A visual sensor with double-layer filter system was researched for the different levels of the welding current. An image processing in C+?+ language was developed to extract the seam trajectory and the offset of the torch to the seam in the welding images of aluminum alloys plates with grooves. The rectifying rule of the robot used in this paper was found based on the experimental data, and the seam tracking controller was also analyzed and constructed. The experimental results on straight line seam and curve seam demonstrated the efficiency of the proposal seam tracking system.

Real-Time Seam Tracking Technology of Welding Robot with Visual Sensing

An image sensing system for the TIG (tungsten inert-gas arc) welding process of aluminium alloy was established. The relationships between the image sensing system and the characteristic of welding current were discussed in detail. Front and back images of the weld pool were obtained with different welding parameters. In order to process the image, the characteristics of an aluminium alloy were analysed. Image processing and pattern recognition were first used to obtain information from the TIG welding process for aluminium alloy. The image of the weld pool was pre-processed using a series of methods: a weighted median filter, a statistical threshold by expectation and the projection method. A neural network method was used to extract the edge of the images of the weld pool. The result of detecting the edge with a BP neural network were excellent. The symmetry of the weld pool for aluminium alloy was studied when the welding current is large. The edge of the image of the whole weld pool is obtained in a single side image and an accurate method for measuring the weld pool geometry parameter is provided. Experiments show that using the image sensing to control the TIG weld width for aluminium alloy is an effective method.

Obtaining weld pool vision information during aluminium alloy TIG welding

This paper presents a new technology of real-time arc length monitoring and sag-depression prediction through arc sound signal, which is essential to realize the welding penetration control during gas tungsten arc welding of arc length. A set of automatic measurement and control system have been proposed to achieve real-time arc length control via audio sensing system. After preprocessing of arc sound signal, the piecewise linear models of arc sound signal were established under two different arc length variation 3–4 and 4–5–6 mm, analyzing the prediction errors of linear model, which were proved to be good enough for online monitoring of arc length in pulse GTAW. Based on the linear relationship between arc sound and arc length, the linear fitting model was implemented on predicting the surface height of weld pool. A segmented self-adaptive PID controller was proposed to achieve the monitoring and controlling of arc length;, the confirmatory experiments have been designed to test the control effect of arc-length monitoring based on arc sound signal.

Real-time control of welding penetration during robotic GTAW dynamical process by audio sensing of arc length

A passive vision sensor is added to a teaching and playback welding robot for pulsed gas tungsten arc welding, which is normally used in the welding of thin plate closed-gap butts. This paper presents a seam tracking method based on this sensor through period visual measurement of the offset between the torch and the seam center. A robust image processing algorithm is developed to extract the seam center. A kind of ARX(auto-regressive with exogenous input) model is studied to describe the relationship between the rectifying voltage and the offset. The fuzzy PID(proportional-integral-derivative) seam tracking controller is analyzed and designed in consideration of various offsets based on the ARX model. The experimental results on straight line weld display a good seam tracking capability of the proposed method.

Tao Lin

Papers

Arc welding robot system with seam tracking and weld pool control based on passive vision

Real-Time Seam Tracking Technology of Welding Robot with Visual Sensing

Obtaining weld pool vision information during aluminium alloy TIG welding

Real-time control of welding penetration during robotic GTAW dynamical process by audio sensing of arc length

Robot welding seam tracking method based on passive vision for thin plate closed-gap butt welding