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The utilization of thermoplastics is extensively prevalent in modern industrial sectors owing to their distinctive mechanical features. Friction stir welding is recognized as a distinctive joining technology that addresses the weaknesses of heat-induced fusion welding. This friction-stirred solid-state welding technology can be effectively employed to join various difficult-to-weld polymeric materials. This paper examines the weldability of friction stir butt welding utilizing a cylindrical tapered threaded tool on a 3 mm thick Acrylonitrile Butadiene Styrene (ABS) and Polycarbonate (PC) polymers. The impact of tool rotational speed (800 and 1200 rpm) and tool traverse speed (10 mm/min to 50 mm/min) on the joint strength of welded samples has been analyzed. The maximum joint efficiency achieved is 52.71% for ABS while using a rotational speed of 1200 RPM and a traverse speed of 10 mm/min. For PC, the maximum joint efficiency is 54% with a rotational speed of 800 RPM and a traverse speed of 40 mm/min. The joint efficiency of polymer is significantly improved as a result of the effective heat distribution and fusion during the welding. The tensile strength of ABS polymer decreases as the traverse speed increases from 10 mm/min to 50 mm/min at both rotational speeds of 800 and 1200 rpm. However, the tensile strength of PC polymer exhibits fluctuations as the traverse speed increases from 10 mm/min to 50 mm/min. This behavior may be attributed to the fluctuating heating and cooling conditions that occur during the welding process at varying rotation and traverse speeds. In contrast to the polymeric base material, the weld zone demonstrated a lower hardness value. The heated tool induces material softening, which results in a reduction in hardness. An examination of alterations in the microstructure of the weld zone was conducted using scanning electron microscopy and stereo microscopy. The observed microstructures were applied to determine the reasons for the decrease in strength. The micrographs illustrate the formation of a fragmentation, attributable to the residual stress generated during the rapid cooling of the liquid polymer. Moreover, a highly increased temperature or traverse speed may result in the formation of voids at the joint interface.

The H-beam riveting and welding work cell is an automated unit used for processing H-beams. By coordinating the gripping and welding robots, the work cell achieves processes such as riveting and welding stiffener plates, transforming the H-beam into a stiffened H-beam. In the context of intelligent manufacturing, there is still significant potential for improving the productivity of riveting and welding tasks in existing H-beam riveting and welding work cells. In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. The algorithm employs recurrent neural networks to capture and process historical information. Action masking is used to filter out invalid states and actions, while a shared reward mechanism is adopted to balance cooperation efficiency among agents. Additionally, value function normalization and adaptive learning rate strategies are applied to accelerate convergence. This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). Experimental results show that, compared with other baseline methods, the rMAPPO-based agent scheduling method can reduce robot waiting times more effectively, demonstrate greater adaptability in handling different riveting and welding tasks, and significantly enhance the manufacturing efficiency of stiffened H-beam.

Cu/Al composite plate was manufactured by underwater explosive welding method. The interface characteristics and mechanical properties of Cu/Al composite plate were evaluated and analyzed through phased array ultrasonic inspection, microstructure, uniaxial tensile test, three-point bending test, tensile shearing test and microhardness test. The results showed that the welding of thin Cu and Al plates is achieved by underwater explosive welding, with a Cu plate thickness of only 0.5 mm. A well bonded interface between Cu and Al plate is obtained, at a detonation velocity of 4 000 m/s, when the distance between Cu foil and Al plate is 0.2 mm. There are wavy fusion zones at the bonding interface of Cu/Al composite plate. No delamination or cracks are found at the bonding interface between Cu and Al during tensile and bending tests, and local cracking only occurs at the necking part in the tensile test due to severe deformation. The tensile strength and minimum tensile shearing strength of Cu/Al composite plate reaches 133 and 72.9 MPa, respectively. The hardness values of fusion zone, Cu and Al at the interface reach 385, 135 and 52 HV, respectively. The increase in hardness of Cu and Al near the interface is mainly caused by severed deformation induced by intense shock pressure.

Arc sound signals are considered appropriate for detecting penetration states in cold metal transfer (CMT) welding because of their noninvasive nature and immunity to interference from splatter and arc light. Nevertheless, the stability of arc sound signals is suboptimal, the conventional feature extraction methods are inefficient, and the significance of arc sound attributes for determining penetration statuses is often overlooked. In this study, a compact convolutional neural network (CNN) model is proposed for the adaptive extraction of features from arc sound signals. The model uses the Mel spectrum diagram of an arc sound signal obtained through a short-time Fourier transform (STFT) and a Mel filter bank conversion step as its input. To improve the recognition capabilities of the model, a novel CNN-selective kernel (SK) model for weld penetration recognition is introduced, which integrates the dynamic selection kernel network (SKNet) into the CNN architecture. The experimental results indicate that the CNN-SK model outperforms the traditional models, achieving an accuracy of 98.83% on the validation dataset. This model holds promise for assessing weld penetration in CMT welding applications. The project is available at https://github.com/ZWL58/data/tree/master .

The paper presents a comprehensive identification and assessment of occupational hazards related to the operation of robotic welding systems. New technology fundamentally improves quality and accuracy of welds, especially precise narrow seams connecting thin sheets or profiles. The paper draws attention to typical occupational hazards occurring in the workplace, as well as those resulting from the use of robotic welding devices. Risk assessment was performed using the commonly used RISK SCORE method. Moreover, as part of our own research, the employee’s reliability levels were determined by determining the probability of making a mistake. The human factor reliability study was carried out using HRA (Human Reliability Analysis) methods. The classic TESEO and HEART approaches have been expanded to include additional factors, such as human-machine interface (HMI), cognitive load, quality of documentation and procedures, and task ambiguity. This addition addresses a significant methodological gap in classic HRA analyses, which have so far overlooked these key aspects that influence the effectiveness and safety of human-robot interactions.

There have been numerous reports of welder’s worker exposure to metal fumes. Carcinogenic and non-carcinogenic (neurological, dermal, and etc.) effects are the adverse outcomes of exposure to welding fumes. In this review study, data were collected from previous studies conducted in Iran from 1900 to 2020. The risk of carcinogenicity and non-carcinogenicity due to exposure to welding metal fumes was assessed using the United States Environmental Protection Agency (USEPA) method based on the Monte Carlo simulation (MCS). Results showed mean of metal fume concentration in gas welding was in the range of 1.8248 to 1060.6 (µg/m 3 ) and in arc welding was 54.935 to 4882.72 (µg/m 3 ). The mean concentration of fumes in gas welding is below the recommended American Conference of Governmental Industrial Hygienists (ACGIH) standard exposure limit except for manganese, and in the arc welding, all metal fume concentrations are below the standard exposure limit except for manganese and aluminum. The results showed that the risk of carcinogenicity due to exposure to nickel, manganese in both gas and arc welding, and cadmium in gas welding was higher than standard level (hazard quotient (HQ) more than 1). Cancer risk due to exposure to nickel in both gas and arc welding was probable (1 × 10 −6  < cancer risk (CR) < 1 × 10 −4 ). Health risk assessment showed that welders are exposed to health risks. Preventive measures should be applied in welding workplaces to reduce the concentrations of metal fumes.

This study investigates the influence of key technological parameters on the mechanical characteristics and microstructure of heat-assisted friction stir welded (FSW) joints of AA6061 aluminum alloy pipes. Specifically, the effect of tool rotation speed, transverse speed, and tool shoulder diameter was evaluated. An experimental campaign was conducted using a three-level, three-factor composite design, with the primary objective of determining the optimal combination of these parameters to maximize the tensile strength of the weldments. AA6061 aluminum alloy pipes, with a thickness of 5 mm and an outer diameter of 80 mm, were joined using a resistance preheating FSW (RPFSW) process. The input parameters were varied at three distinct levels: rotation speed (1250, 1500, 1750 rpm), transverse speed (75, 87.5, 100 mm/min), and shoulder diameter (12, 15, 18 mm). Tensile tests were conducted to evaluate mechanical strength, and optical microscopy together with scanning electron microscopy (SEM), was employed to examine the microstructure of the weldments. The results indicate that the weldments achieved a tensile strength ranging from 49.7% to 72.4% of the base material. The optimal processing parameters were identified to achieve the highest predicted tensile strength of 198.15 MPa, corresponding to a transverse speed of 100 mm/min, a rotation speed of 1629 rpm, and a shoulder diameter of 13 mm. Microstructural analysis revealed that appropriate RPFSW parameters lead to suitable temperature and material flow, which in turn reduces weld defects and enhances the overall mechanical properties of the joint.

In long-distance pipelines, the connection between pipelines is often the weakest link. During the manufacturing process of pipelines, roundness errors or defects are inevitable, and the greater the difference between the maximum and minimum diameters, the greater the impact on the strength of pipeline connections. At present, the main method to improve the pipe end is casting upsetting. However, there are currently no reports on using additive manufacturing technology to optimize pipeline ellipticity and improve pipe end strength and stiffness. This article solves the problem of weak weld strength at the connection end of oil pipelines and proposes for the first time the method of using arc additive manufacturing technology to thicken and reinforce the pipe end. By analyzing the ellipticity of the pipe end, arc based additive manufacturing technology is used to thicken the pipe end, thereby improving the strength and stiffness of the pipeline connection weld. This paper establishes a mathematical model for optimizing the design of pipeline connection ends, with remanufacturing thickness, length, and pipe end radius as design variables and pipe end strength and stiffness as constraints, and obtains the optimal parameter combination. The optimization results show that under typical working conditions, the optimized pipe end strength is significantly improved, the strain is significantly reduced, and the pipeline can withstand higher pressure, thereby improving the overall reliability and safety of the pipeline.

A new process of continuous and synchronous calibration process of ovality and straightness for LSAW (Longitudinally Submerged Arc Welding, LSAW) pipes with three rollers is proposed. Specifically, the process is introduced from three aspects: roller-shape, loading parameters and axial and circumferential deformation paths. The process is verified by numerical simulation and physical experiments. Further, the stress-strain in the Sections Ⅱ and Ⅳ is analyzed. The relationship between the process parameters and the residual ovality and residual straightness by experiments is discussed. The calibration scheme of LSAW pipes is put forward by using the control variable method. The results show that the shear stress is the principal stress direction in the Sections Ⅱ and Ⅳ. The residual ovality and residual straightness decrease with the increase of the radial reduction and times of reciprocating bending. The reciprocating bending process can eliminate the difference of the initial curvature, make the curvature of each section tend to be uniform. After calibration, the residual straightness is less than 0.2% and the residual ovality is less than 1%, demonstrating a good feasibility of this process.

An implantable electrode based on bioresorbable Mg-Nd-Zn-Zr alloy was developed for next-generation radiofrequency (RF) tissue welding application, aiming to reduce thermal damage and enhance anastomotic strength. The Mg alloy electrode was designed with different structural features of cylindrical surface (CS) and continuous long ring (LR) in the welding area, and the electrothermal simulations were studied by finite element analysis (FEA). Meanwhile, the temperature variation during tissue welding was monitored and the anastomotic strength of welded tissue was assessed by measuring the avulsion force and burst pressure. FEA results showed that the mean temperature in the welding area and the proportion of necrotic tissue were significantly reduced when applying an alternating current of 110 V for 10 s to the LR electrode. In the experiment of tissue welding ex vivo, the maximum and mean temperatures of tissues welded by the LR electrode were also significantly reduced and the anastomotic strength of welded tissue could be obviously improved. Overall, an ideal welding temperature and anastomotic strength which meet the clinical requirement can be obtained after applying the LR electrode, suggesting that Mg-Nd-Zn-Zr alloy with optimal structure design shows great potential to develop implantable electrode for next-generation RF tissue welding application.