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The purpose of this study is to design a robotic inline measurement system for spot welding quality control to achieve process requirement without any operator during the manufacturing flow.

A robot manipulator carries a stereo-camera and an ultrasonic control probe. The center position of the spot welding point is determined by evaluating the results of the edge, gradient and symmetry approaches from the methods proposed up to now in the literature to increase reliability. The center position of the spot welding point, determined in the camera reference plane, is transferred to the robot base plane coordinates with the hand–eye calibration proposed in this manuscript. Weld quality is checked by the ultrasonic test probe located at the spot welding point.

While operators can only control welding quality, the developed station can also evaluate the quality based on geometric accuracy by processing the deviation of the position of the spot welding points. The proposed calibration method and the results of other methods in the literature are presented in this study by comparing it with synthetic data in simulations and in practical application.

The quality control is performed not only for the spot welding made with robots but also for the manual welds as well. Because of vision configuration, and reliability issues, maximum allowable offset by the correct spot position is limited to 20 mm to position the manipulator for testing. The installation and pretest works of the developed robotic welding quality control station are completed in the Body Shop Area of Ford Otosan factory in Kocaeli/Turkey. The results of the robotic control process are monitored by the quality assurance team. Integration of automation with the production line will be completed and an inline measurement will be done.

In this paper, a new hand–eye calibration method based on simple and closed-form analytical solutions has been presented. The objective function is defined as reducing the deviation in the point projection, rather than reducing the error in the calibration equation. To increase reliability, combining the results of existing centering algorithms for the detection of the strongly deformed spot welding spot center, although it is normally in a circular form, has been suggested.

The purpose of this paper is to present and implement an algorithm to prioritize welding quality deterioration factors for improving welding personnel performance. A case study is carried out in a piping components fabrication process which supplies these components to oil and gas production and processing facilities on the Norwegian continental shelf (NCS).

The quality deterioration factors' prioritization is carried out using statistical methods in conjunction with the data recorded in the welding inspection database (WIDB) of the case study company. Data cleaning and rearrangements were performed to reflect final objective. Based on the welding procedure specifications (WPSs) and quality imperfection groups classified in NS‐EN ISO 6520‐1, the analysis is performed to prioritize the welding quality deterioration factors.

Based on the WPSs and quality imperfection groups classified in NS‐EN ISO 6520‐1, it is possible to prioritize the welding quality deterioration factors. These factors are possible to use for improving the performance of welding personnel to assure the quality of welds in steel fabrications.

The factors prioritized are possible to use for improving the performance of welding personnel to assure the quality and reliability of welds in a steel fabrication.

Assuring quality as proposed in the manuscript, the catastrophic failures that are potential in production and process plants can be mitigated. This enhances health, safety and environmental performance of welds in steel fabrications.

The value of this paper is to illustrate an innovative approach to a real life quality problem; it demonstrates how the application of qualitative and quantitative quality instruments in accordance with technical specification can help in increasing and maintaining product compliance and in optimizing the management of resources.

The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.

External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.

In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.

However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.

Penetration state is one of the most important factors for judging the quality of a gas tungsten arc welding (GTAW) joint. The purpose of this paper is to identify and classify the penetration state and welding quality through the features of arc sound signal during robotic GTAW process.

This paper tried to make a foundation work to achieve on‐line monitoring of penetration state to weld pool through arc sound signal. The statistic features of arc sound under different penetration states like partial penetration, full penetration and excessive penetration were extracted and analysed, and wavelet packet analysis was used to extract frequency energy at different frequency bands. The prediction models were established by artificial neural networks based on different features combination.

The experiment results demonstrated that each feature in time and frequency domain could react the penetration behaviour, arc sound in different frequency band had different performance at different penetration states and the prediction model established by 23 features in time domain and frequency domain got the best prediction effect to recognize different penetration states and welding quality through arc sound signal.

This paper tried to make a foundation work to achieve identifying penetration state and welding quality through the features of arc sound signal during robotic GTAW process. A total of 23 features in time domain and frequency domain were extracted at different penetration states. And energy at different frequency bands was proved to be an effective factor for identifying different penetration states. Finally, a prediction model built by 23 features was proved to have the best prediction effect of welding quality.

The purpose of this paper is to develop an efficient online monitoring system. It can improve the welding quality through the real‐time analysis of the welding image information.

In this paper, a set‐variable precision rough set (VPRS) modeling method was improved and the prediction model of back‐side bead width dynamic response for robotic arc welding process is proposed. Back‐side width based on the vision sensor in the compound controller is used. Meantime, a compound intelligent controller is designed with the wire‐feeding rate compensation.

The dynamical information of the top‐side weld pool can be captured in real‐time. Moreover, VPRS prediction model could provide the back‐side bead width of the weld, which integrated with the fuzzy neural network controller. Therefore, more uniform weld penetration can be achieved with the variation of the assembling gap.

The monitor system requires that the information of the weld pool and the front gap can be extracted precisely. Moreover, the front assembling gap should be limited in a certain interval [0∼3 mm]. This puts forward high request to image processing algorithm and the assembling of the work‐piece.

This monitor system is applicable to the manufacturing of the spherical head petals in rocket storage tank.

The paper demonstrates that the compound intelligent controller based on the VPRS prediction is possible and effective. The experiments show that the real‐time and precision requirements for monitoring and control of weld quality can be satisfied by using this online control system during welding process.

The purpose of this paper was to use visual and arc sensors to simultaneously obtain the underwater wet welding information, and a weld seam-forming model was made to predict the weld seam's geometric parameters. It is difficult to obtain a fine welding quality in underwater welding because of the intense disturbances of the water environment. To automatically control the welding quality, the weld seam-forming model should first be established. Thus, the foundation was laid for automatically controlling the underwater welding seam-forming quality.

Visual and arc sensors were used simultaneously to obtain the weld seam image, current and voltage information; then signal algorithms were used to process the information, and the back propagation (BP) neural network was used to model the process.

Experiment results showed that the BP neural network model could precisely predict the weld seam-forming parameters of underwater wet welding.

A weld seam-forming model of underwater wet welding process was made; this laid the foundation for establishing a controller for controlling the underwater wet welding process automatically.

The purpose of this study is to present a comprehensive review of the fundamental concepts and terminologies pertaining to different types of aluminium metal matrix composites, their joining techniques and challenges, friction stir welding (FSW) process, post-welding characterizations and basic control theory of FSW, followed by the discussions on the research reports in these areas.

Joining of aluminium metal matrix composites (Al-MMC) poses many challenges. These materials have their demanding applications in versatile domains, and hence it is essential to understand their weldability and material characteristics. FSW is a feasible choice for joining of Al-MMC over the fusion welding because of the formation of narrow heat affected zone and minimizing the formation of intermetallic compounds at weld interface. The goal in FSW is to generate enough thermal energy by friction between the workpiece and rotating tool. Heat energy is generated by mechanical interaction because of the difference in velocity between the workpiece and rotating tool. In the present work, a detailed survey is done on the above topics and an organised conceptual context is presented. A complete discussion on significance of FSW process parameters, control schemes, parameter optimization and weld quality monitoring are presented, along with the analysis on relation between the interdependent parameters.

Results from the study present the research gaps in the FSW studies for joining of the aluminium-based metal matrix composites, and they highlight further scope of studies pertaining to this domain.

It is observed that the survey done on FSW of Al-MMCs and their control theory give an insight into the fundamental concepts pertaining to this research area to enhance interdisciplinary technology exploration.

Quality control of arc welding process is the key component in robotic welding system. The purpose of this paper is to address vision‐sensing technology and model‐free adaptive control (MFC) of weld pool size during automatic arc welding system.

The shape and size parameters for the weld pool are used to describe the weld pool geometry, which is specified by the backside weld width. The welding current and wire‐feeding speed are selected as the control variable, and the backside width of weld pool is selected as the controlled variable. To achieve the goal of full penetration and fine weld seam formation, a multiple input single output (MISO) MFC is designed for control of the backside pool width.

The research findings show that it is feasible to develop such a MISO MFC of weld pool size, which is independent on mathematic model of weld pool dynamics. And the control algorithm is simple to use and has a minimal computational burden.

This is a work in progress. The controlled process results are mainly influenced by the period of competing control algorithm and image processing, which could be improved by the hardware and enhancing computation speed. The closed‐loop control is a two inputs‐one output system. Thus, the means by which the multiple input multiple output (MIMO) control method is applied to weld pool dynamics is work for the future.

The control system is applicable to automatic gas tungsten arc welding (GTAW).

The MISO MFC has been set up for automatic GTAW to overcome the nonlinear and uncertainty of GTAW process, in which two welding parameters can be adjusted simultaneously. In addition, this controller is independent on welding pool dynamic model.

Surface forming control of welding bead is the fundamental study in automated welding. Considering that the vision sensing system cannot extract the height information of weld pool in pulsed GTAW process, so this paper designed a set of automatic measurement and control technology to achieve real-time arc height control via audio sensing system. The paper aims to discuss these issues.

The experiment system is based on GTAW welding with acoustic sensor and signal conditioner. A combination denoising method was used to reduce the environmental noise and pulse interference noise. After extracting features of acoustic signal, the relationship between arc height and arc sound pressure was established by linear fitting. Then in order to improve the prediction accuracy of that model, the piecewise linear fitting method was proposed. Finally, arc height linear model of arc sound signal and arc height is divided into two parts and built in two different arc height conditions, which are arc height 3-4 and 4-5-6 mm.

The combination denoising method was proved to have great effect on reducing the environmental noise and pulse interference noise. The experimental results showed that the prediction accuracy of linear model was not stable in different arc height changing state, like 3-4 and 4-5-6 mm. The maximum error was 0.635588 mm. And the average error of linear model was about 0.580487 mm, and the arc sound signal was accurately enough to meet the requirement for real-time control of arc height in pulse GTAW.

This paper tries to make a foundation work to achieve controlling of depth of welding pool through arc sound signal, then the welding quality control. So a new idea of arc height control based on automatic measuring and processing system through arc sound signal was proposed. A new way to remove environmental noise and pulse interference noise was proposed. The results of this thesis had proved that arc sound signal was an effective features and precisely enough for online arc height monitoring during pulsed GTAW.

The purpose of this paper is to analyze the technology of capturing and processing weld images in real‐time, which is very important to the seam tracking and the weld quality control during the robotic gas tungsten arc welding (GTAW) process.

By analyzing some main parameters on the effect of image capturing, a passive vision sensor for welding robot was designed in order to capture clear and steady welding images. Based on the analysis of the characteristic of the welding images, a new improved Canny algorithm was proposed to detect the edges of seam and pool, and extract the seam and pool characteristic parameters. Finally, the image processing precision was verified by the random welding experiments.

It was found that the seam and pool images can be clearly acquired by using the passive vision system, and the welding image characteristic parameters were accurately extracted through processing. The experiment results show that the precision range of the image processing can be controlled about within ±0.169 mm, which can completely meet the requirement of real‐time seam tracking for welding robot.

This system will be applied to the industrial welding robot production during the GTAW process.

It is very important for the type of teaching‐playback robots with the passive vision that the real‐time images of seam and pool are acquired clearly and processed accurately during the robotic welding process, which helps determine follow‐up seam track and the control of welding quality.