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This paper aims to compare the wear performance of carbon steel specimens clad with TiC, WC and TiN powders by the gas tungsten arc welding (GTAW) method under optimum processing conditions.

Various ceramic powders (TiC, WC and TiN) with equal percentages by weight were prepared for use as cladding materials to compare their effects on wear resistance. The wear behaviors of different cladding specimens were evaluated with a rotating-type tribometer under dry sliding conditions. The cladding microstructures were characterized by optical microscopy, scanning electron microscopy and X-ray energy dispersive spectrometry.

The experimental results confirmed that the hardness was also much higher in the carbon steel with cladding than in carbon steel without cladding. The pin-on-disc wear test showed that the wear-resistance of ceramics clad with TiC is better than that in ceramics clad with WC or TiN. The wear scar area of the specimen with TiC cladding was only one-tenth that of carbon steel without cladding.

The experiments confirm that the cladding surfaces of ceramic particles reduce wear rate and friction.

The SiC reinforced Al composite is perhaps the most successful class of metal matrix composites (MMCs) produced to date. They have found widespread application for aerospace, energy, and military purposes, as well as in other industries – for example, they have been used in electronic packaging, aerospace structures, aircraft and internal combustion engine components, and a variety of recreational products. In all these applications, welding plays a vital role. Little attention has been paid to SiC reinforced aluminium matrix composites joined by gas tungsten arc (GTA) welding. The purpose of this paper is to outline the manufacturing method for producing MMCs, GTA welding of MMCs and pitting corrosion analysis of welded MMCs.

This paper focuses upon production and welding of metal matrix composites. The welded composites have been treated at elevated and cryogenic temperatures for experimental studies. Pitting corrosion analysis of welded plates was carried out as per Box Benkehn Design.

From the results, it should be noted that maximum pitting resistance was observed with MMCs containing 10% SiC treated at cryogenic temperature. Corrosion resistance of welded composites treated at elevated temperature was found to be higher than that of as‐welded and at cryogenic temperature treated composites. The pitting potential increases with increase in % SiC to certain level and decreases with further increase in % SiC. Corrosion potential of composites treated at elevated temperature is high compared to other composites. Maximum pitting resistance is observed when the welding current was kept at 175 amps for 10% addition of SiC in LM25 matrix treated at cryogenic temperature.

The paper outlines the manufacturing method for producing MMCs, GTA welding of MMCs and pitting corrosion analysis of welded MMCs. The results obtained may be helpful for the automobile and aerospace industries.

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.

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.

The status of welding process is difficult to monitor because of the intense disturbance during the process. The purpose of this paper is to use multiple sensors to obtain information about the process from different aspects and use multi‐sensor information fusion technology to fuse the information, to obtain more precise information about the process than using a single sensor alone.

Arc sensor, visual sensor, and sound sensor were used simultaneously to obtain weld current, weld voltage, weld pool's image, and weld sound about the pulsed gas tungsten‐arc welding (GTAW) process. Then special algorithms were used to extract the signal features of different information. Fuzzy measure and fuzzy integral method were used to fuse the extracted signal features to predict the penetration status about the welding process.

Experiment results show that fuzzy measure and fuzzy integral method can effectively utilize the information obtained by different sensors and obtain better prediction results than a single sensor.

Arc sensor, visual sensor, and sound sensor are used in pulsed GTAW at the same time to obtain information, and fuzzy measure and fuzzy integral method are used to fuse the different features in welding process for the first time; experiment results show that multi‐sensor information can obtain better results than single sensor, this provides a new method for monitoring welding status and to control the welding process more precisely.

The purpose of this paper is to develop a kind of low cost measuring system based on binocular vision sensor to detect both the weld pool geometry and root gap simultaneously for robot welding process.

Two normal charge coupled device cameras are used for capturing clear images from two directions; one of them is used to measure the root gap and another one is used to measure the geometric parameters of the weld pool. Efforts are made from both hardware and software aspects to decrease the strong interferences in pulsed gas tungsten arc welding process, so that clear and steady images can be obtained. The grey level distribution characteristics of root gap edge and weld pool edge in images are analyzed and utilized for developing the image processing algorithms.

A solid foundation for seam tracking and penetration control of robot welding process can be established based on the binocular vision sensor.

The results show that the algorithms can extract the root gap edges and the contour of weld pool effectively, and then some geometric parameters can be calculated from the results.

The binocular vision system provides a new method for sensing of robot welding process.

The purpose of this paper is to test the wear behavior of a carbon steel surface after cladding by gas tungsten arc welding (GTAW) method to enhance wear resistance.

The microstructures, chemical compositions, and wear characteristics of cladded surfaces were analyzed by scanning electron microscopy (SEM), and energy dispersive X‐ray spectroscopy (EDX). A rotating‐type tribometer was used to evaluate the wear characteristics of cladded specimens under dry sliding conditions at room temperature. The dry sliding wear resistance of the coatings was tested as a function of applied load and sliding time, and wear mechanisms were elucidated by analyzing wear surfaces.

The experimental results revealed an excellent metallurgical bond between the composite coating and substrate. The coating was uniform, continuous, and almost defect‐free, and particles were evenly distributed throughout the cladding layer. Hardness was increased from 200 HV in the substrate to 650‐800 HV in the modified layer due to the presence of the hard TiC phase. The excellent wear resistance and very low load sensitivity observed in the dry sliding wear test of the intermetallic matrix composite coating were due to the high hardness of TiC and the strong atomic bonds of the intermetallic matrix.

The experiments in this study confirm that, by reducing friction and anti‐wear, the cladding layer prepared using the proposed methods can prolong machinery operating life.

The control of weld penetration in gas tungsten arc welding (GTAW) is required for a “teach and playback” robot to overcome the gap variation in the welding process. This paper aims to investigate this subject.

This paper presents a robotic system based on the real‐time vision measurement. The primary objective has been to demonstrate the feasibility of using vision‐based image processing to measure the seam gap in real‐time and adjust welding current and wire‐feed rate to realize the penetration control during the robot‐welding process.

The paper finds that vision‐based measurement of the seam gap can be used in the welding robot, in real‐time, to control weld penetration. It helps the “teach and playback” robot to adjust the welding procedures according to the gap variation.

The system requires that the seam edges can be accurately identified using a correlation method.

The system is applicable to storage tank welding of a rocket.

The control algorithm based on the knowledge base has been set up for continuous GTAW. A novel visual image analysis method has been developed in the study for a welding robot.

Vibratory weld conditioning parameters have a great influence on the improvement of mechanical properties of weld connections. The purpose of this paper is to understand the influence of vibratory weld conditioning on the mechanical and microstructural characterization of aluminum 5052 alloy weldments. An attempt is made to understand the effect of the vibratory tungsten inert gas (TIG) welding process parameters on the hardness, ultimate tensile strength and microstructure of Al 5052-H32 alloy weldments.

Aluminum 5052 H32 specimens are welded at different combinations of vibromotor voltage inputs and time of vibrations. Voltage input is varied from 50 to 230 V at an interval of 10 V. At each voltage input to the vibromotor, there are three levels of time of vibration, i.e. 80, 90 and 100 s. The vibratory TIG-welded specimens are tested for their mechanical and microstructural properties.

The results indicate that the mechanical properties of aluminum alloy weld connections improved by increasing voltage input up to 160 V. Also, it has been observed that by increasing vibromotor voltage input beyond 160 V, mechanical properties were reduced significantly. It is also found that vibration time has less influence on the mechanical properties of weld connections. Improvement in hardness and ultimate tensile strength of vibratory welded joints is 16 and 14%, respectively, when compared without vibration, i.e. normal weld conditions. Average grain size is measured as per ASTM E 112–96. Average grain size is in the case of 0, 120, 160 and 230 is 20.709, 17.99, 16.57 and 20.8086 µm, respectively.

Novel vibratory TIG welded joints are prepared. Mechanical and micro-structural properties are tested.

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.