Automation Technology in Welding Seam Welding Machining Design

With the continuous development of society,the gradual improvement of mechanical automation technology has been introduced into the production of many enterprises in China,which has had a great impact on the increase of China′s gross national product.Applying mechanical automation technology greatly saves labor cost,improves working efficiency and the production level of enterprises,effectively promotes the development of enterprises,and increases the enterprise′s income.In addition,mechanical automation technology is closely related to product production⁃related technology and plays a very important role in the innovation of enterprise products.This article will briefly introduce the design and application of automation technology in welding seam welding design,and understand the development background,research status,current application and simulation design.

Friction assisted solid state lap seam welding and additive manufacturing method

This paper describes results of seam welding of relatively high temperature melting materials, AISI 304, C-Mn steels, Ni-based alloys, CP Cu, CP Ni, Ti6Al4V and relatively low temperature melting material, AA6061. It describes the seam welding of multi-layered similar and dissimilar metallic sheets. The method described and involved advancing a rotating non-consumable rod(CP Mo or AISI 304) toward the upper sheet of a metallic stack clamped under pressure. As soon as the distal end of the rod touched the top portion of the upper metallic sheet, an axial force was applied. After an initial dwell time, the metallic stack moved horizontally relative to the stationery non-consumable rod by a desired length, thereby forming a metallurgical bond between the metallic sheets. Multi-track and multi-metal seam welds of high temperature metallic sheets, AISI 304, C-Mn steel,Nickel-based alloys, Cp Cu, Ti6Al4V and low temperature metallic sheets, AA6061 were obtained. Optical and scanning electron microscopy examination and 180 degree U-bend test indicated that defect free seam welds could be obtained with this method. Tensile- shear testing showed that the seam welds of AISI 304, C-Mn steel, Nickel-based alloy were stronger than the starting base metal counterparts while AA6061 was weaker due to softening. The metallurgical bonding at the interface between the metallic sheets was attributed to localized stick and slip at the interface, dynamic recrystallization and diffusion. The method developed can be used as a means of welding, cladding and additive manufacturing.

Weld Seam Deviation Prediction of Gas Metal Arc Welding Based on Arc Sound Signal

Weld seam deviation prediction is the key to weld seam tracking control, which is of great significance for realizing welding automation and ensuring welding quality. Aiming at the problem of weld seam deviation prediction in GMAW (gas metal arc welding), a method of weld seam deviation prediction based on arc sound signal is proposed. By analyzing the feature of the arc sound signal waveform, the time domain feature of the arc sound signal is extracted. The wavelet packet analysis method is used to analyze the time-fre- quency domain feature of the arc sound signal, and the wavelet packet energy feature is extracted. The time domain feature and wavelet packet energy feature are used to establish the feature vector, and the BP (back propagation) neural network is used to realize the weld seam deviation prediction. The results show that the method proposed in this paper has a good weld seam deviation prediction effect, with a mean absolute error of 0.234 mm, which provides a new method for GMAW weld seam recognition.

Vision Sensing-Based Online Correction System for Robotic Weld Grinding

The service cycle and dynamic performance of structural parts are afected by the weld grinding accuracy and surface consistency. Because of reasons such as assembly errors and thermal deformation, the actual track of the robot does not coincide with the theoretical track when the weld is ground ofine, resulting in poor workpiece surface quality. Considering these problems, in this study, a vision sensing-based online correction system for robotic weld grinding was developed. The system mainly included three subsystems: weld feature extraction, grinding, and robot real-time control. The grinding equipment was frst set as a substation for the robot using the WorkVisual software. The input/output (I/O) ports for communication between the robot and the grinding equipment were confgured via the I/O mapping function to enable the robot to control the grinding equipment (start, stop, and speed control). Subsequently, the Ethernet KRL software package was used to write the data interaction structure to realize realtime communication between the robot and the laser vision system. To correct the measurement error caused by the bending deformation of the workpiece, we established a surface profle model of the base material in the weld area using a polynomial ftting algorithm to compensate for the measurement data. The corrected extracted weld width and height errors were reduced by 2.01% and 9.3%, respectively. Online weld seam extraction and correction experiments verifed the efectiveness of the system’s correction function, and the system could control the grinding trajectory error within 0.2 mm. The reliability of the system was verifed through actual weld grinding experiments. The roughness, Ra, could reach 0.504 µm and the average residual height was within 0.21 mm. In this study, we developed a vision sensing-based online correction system for robotic weld grinding with a good correction efect and high robustness.

WeldNet:A voxel-based deep learning network for point cloud annular weld seam detection

Weld seam detection is an important part of automated welding.At present,few studies have been conducted on annular weld seams,and a lot of defects exist in the point cloud model of the tube sheet obtained by RGB-D cameras and photography methods.Aiming at the above problems,this paper proposed an annular weld seam detection network named WeldNet where a voxel feature encoding layer was adaptively improved for annular weld seams,the sparse convolutional network and region proposal network(RPN)were used to detect annular weld seam position,and an annular weld seam detection loss function was designed.Further,an annular weld seam dataset was established to train the network.Compared with the random sampling consistency(RANSAC)method,WeldNet has a higher detection accuracy,as well as a higher detection success rate which has increased by 23%.Compared with U-Net,WeldNet has been proven to achieve a better detection result,and the intersection over the union of the weld seam detection is improved by 17.8%.

Influence of shielding gas and non-consumable electrode configuration on weld formation

The effect of the shielding gas composition and the cathode processing history on the weld formation quality during welding with a non-consumable electrode at high current was studied. The major reasons for pores, “waists” and undercuts formation during welding at high currents and speed are discrete melt movement to the solidification front due to the arc decline from the cathode axis and significant melt overhanging in the pool tail part caused by excessive peak pressure on the discharge axis. Cathode flow dispersion causes the lack of displacement of the molten metal which results in its laminar flow in the weld pool, uniform flow of the metal to the crystallization front and sound weld formation. The melt movement in the weld pool and eventually the welded joint quality is determined by the pressure distribution pattern on the welded metal surface and the anode spot lag from the electrode. It was demonstrated that non-consumable electrode configurations that provide arcing with a diffuse cathode spot and increased helium concentration in the inert atmosphere during welding with a conical electrode allow sound weld formation.

Particulars of Use of New 850 Standard Palladium Alloys for Jewelry Production

The researches of new palladium-based alloys have been conducted, and the cast and deformed bars for jewels have been obtained. The mechanism of occurrence of welding defects (pore formation) in the production of jewelry chains of silver-containing palladium alloys has been studied. The analysis of the welded joints of the chains, obtained by argon-arc welding, has been carried out, and the properties of metal have been analyzed. Conclusions have been drawn on the possibility of use of the developed palladium alloys, which do not contain silver, for the production of wired semi-products having a low propensity for pore formation in the weld seams.

Microstructural and mechanical properties of ZA10 alloy tubes and their weld seams prepared by Conform continuous extrusion

In the present work,Zn-10 Al-2 Cu-0.05 Ti(ZA10)alloy tubes with a diameter of 12.5 mm and wall thickness of 1.2 mm were fabricated by one-pass and double-pass Conform continuous extrusion.A stabilizing heat treatment[350℃,30 min(furnace cooling)+120℃,12 h(air cooling)]was also applied to some of the double-pass tubes to improve the quality of their weld seams.The yield strength,ultimate tensile strength,elongation and expansion ratio of the one-pass continuous extrusion tube were 268.4 MPa,294.3 MPa,13.8%and 5.5%,respectively.Double-pass continuous extrusion improved these values to 278.4 MPa,317.2 MPa,15.4%and 11.4%,respectively.Double-pass tubes also had fewer aggregations of Al-αprecipitates along the welding seam,which improved seam quality and caused cracks to appear in the matrix,away from the weld-affected zone,during expansion testing.Heat-treated double-pass tubes exhibited superior yield strength(283.9 MPa)and ultimate tensile strength(328.5 MPa)but lower elongation(10.2%)and expansion ratios(10.3%).Additionally,the heat-treated tubes exhibited markedly lower elongation at room temperature due to the remarkable blockage of dislocation motions by fine-scale lamellar(α+η)eutectoid structures and a lower size effect when stretched.