Search Results (487)

The utilisation of composite materials has the potential to play a vital role in the development of lightweight structures for future generations of aircraft, with the objective to reduce emissions. Ultrasonic welding is a process that has been proven to exhibit advantageous qualities, including the capacity to achieve welds with a comparatively short process time. Furthermore, its capacity to function as both a static and a continuous process makes it a viable candidate for facilitating the realisation of this objective. The present study investigates the potential of a novel explicit modelling approach for the static ultrasonic welding process to more accurately represent the welding process by incorporating a more precise representation of the hammering effect. The hammering effect describes the partial loss of contact between the sonotrode and the upper adherend. The model’s validation was achieved through a multifaceted approach that incorporates high-speed camera recording, encompassing digital image correlation, laser displacement sensor measurements, and static ultrasonic welding experiments. These experiments encompassed varying welding times, followed by fracture surface analysis. The findings showed that an explicit time-domain model can effectively represent the static welding process of unidirectional materials utilising a film energy director. The experimental validation demonstrated a high degree of correlation between the thermal behaviour of the welding interface and the simulation results. The study demonstrated that the neutral position of the sonotrode exhibited an increase during the initial phase of the welding process due to dynamic stresses. This phenomenon enables reduced constraint movement of the adherends and the energy director, which results in the disconnection of the sonotrode from both the upper adherend and the energy director, as well as the adherends and the anvil. The higher neutral position of the sonotrode was then implemented in an explicit simulation of the static ultrasonic welding process. Full article

Sustainable manufacturing performance is increasingly influenced by the quality of decisions embedded in Quality Management System (QMS) activities, particularly those related to problem analysis and preventive action. In industrial environments such as welded bus chassis production, recurring quality defects—although involving small components—can generate sustainability impacts through rework, inspection effort, and energy consumption. Although artificial intelligence (AI) is increasingly adopted to support quality-related tasks, its contribution is often assessed in terms of automation rather than its effect on decision quality. This study presents an AI-supported, prompt-driven decision framework designed to strengthen preventive performance within QMS. The framework is implemented through a deterministic software application that formalizes prompt engineering as a rule-based process, transforming informal human problem descriptions into structured prompts suitable for external AI reasoning tools. The application itself does not embed AI and does not generate decisions; instead, it functions as a transparent decision interface that reduces variability in problem formulation and supports methodological consistency. The framework was validated through an industrial case study conducted in a bus chassis manufacturing plant experiencing recurring defects related to missing or incorrectly positioned welded brackets. Quantitative evaluation using Key Performance Indicators demonstrates reduced analysis cycle time, improved completeness of problem definitions, higher corrective action implementation rates, and lower defect recurrence. Full article

For the failure issue of the weak part of the safety end of the nuclear power pressure vessel connection, the J-integral method was used to test the fracture toughness of the weak part at the bottom of the dissimilar metal welded joints (DMWJs) of SA508-III and 316L in the temperature range of 25 °C to 320 °C, and the mechanism of temperature-induced crack instability and propagation was studied. The research results indicate that at all test temperatures, the position of the weld near the 316L steel is the failure site of the welded joint. The fracture toughness of the joint decreases with increasing temperature, with a maximum decrease of 42.0%. Analysis shows that as the temperature increases, the dislocation density decreases, the tensile strength decreases, and the yield strength ratio decreases, making it easier for secondary cracks to initiate near the crack tip, thereby accelerating the unstable propagation of cracks. At the same time, as the temperature increases, the number of twin crystals that can promote crack turning and prolong the crack propagation path decreases, the energy absorbed before fracture decreases, and the fracture toughness value decreases accordingly, further accelerating the unstable propagation of cracks. Full article

The geometric variability of industrial components represents a persistent challenge in robotic arc welding, particularly in high-volume manufacturing environments where parts are positioned in fixtures based on nominal CAD assumptions. Even moderate deviations in dimensions or seating conditions can lead to weld defects, rework, and reduced process capability when conventional offline programming is employed. This paper presents an applied industrial workflow for adaptive robotic welding trajectory correction that integrates full-field 3D optical metrology with a data-driven deep reinforcement learning (DRL) model. Prior to welding, each component is scanned using a structured-light 3D system, and critical geometric deviations are extracted relative to the nominal CAD model. These deviations define a compact state representation that is mapped, via a trained DRL agent, to corrective translational and rotational adjustments of the welding trajectory. Importantly, all trajectory corrections are computed offline, ensuring compatibility with standard industrial robot controllers and avoiding real-time computational overheads. The proposed approach is validated using real production data from an industrial batch of 5000 components characterized by significant dimensional variability and limited process capability. Experimental results demonstrate a reduction in welding defects exceeding 90%, elimination of rework associated with improper part positioning, and an improvement of the overall process performance to a sigma level of 5.219. The results show that combining 3D optical metrology with learning-based trajectory adaptation enables robust compensation of part-level geometric deviations without mechanical fixture modifications. The proposed method provides a practical and scalable solution for improving welding quality in manufacturing environments affected by upstream variability and imperfect part positioning. Full article

This paper examines the use of high-rigidity Tau-robots in friction stir welding, where process loads are very high. The rigidity of Tau-robots increases at the expense of the workspace. Therefore, the right configuration of the Tau-robot is sought to reconcile rigidity and workspace requirements. This is studied by use of kinematics, followed by static and modal analysis. In particular, by extending an existing kinematic model employing free vectors, the robot workspace was derived in non-dimensional parametric form and was then maximized through evolutionary optimization. However, finite element static and modal analysis that were carried out subsequently may prove, as in a case demonstrated here, that the optimized configuration may not withstand high loads, typically axial forces of 15 kN and torques of 80 Nm, and it may also be susceptible to forced vibrations in the typical spindle rotation range up to 3000 rpm. As a rectification measure, it was shown how a modified configuration by placing robot kinematic chain bases further apart and shortening robot links achieves higher rigidity, axial displacement being reduced by one or two orders of magnitude to below 1 mm and increases critical modal frequency 3 to 5 times depending on the workspace position, of course sacrificing part of the workspace, i.e., reducing it 3-fold to enclose welding lines in a rectangle of dimensions 700 × 800 mm. In the quest for the appropriate robot configuration desired dimensions of parts to be welded and available standard components are briefly considered, too. Full article

Laser-based manufacturing processes—including marking, hardening, cutting, and welding—demand the precise selection of processing parameters, as the resulting surface state is critically dependent on the delivered power density and beam–material interaction time. This study presents a unified predictive framework for estimating the critical surface power density thresholds for melting q_scm and evaporation q_scv as functions of scanning speed v for the following four technologically important metallic materials: titanium, C26000 brass, SS304 stainless steel, and 42CrMo4 alloy steel. The principal novelty of this work is twofold. First, it provides the first directly comparative analysis of these four materials under identical, standardized laser conditions (λ = 1064 nm, d = 40 μm, constant absorptivity A = 0.4), eliminating the confounding effects of variable beam geometries and optical assumptions that hinder cross-study comparisons. Second, it translates fundamental thermophysical principles into a practical engineering tool, such as a validated spreadsheet calculator that outputs material-specific threshold curves in real time, enabling rapid, physics-based parameter estimation without recourse to complex numerical simulations. The computed threshold curves exhibit a consistent non-linear increase with scanning speed for all materials, governed by the inverse relationship between interaction time and required power density. The following clear material hierarchy emerges: C26000 brass exhibits the highest thresholds (e.g., q_scm = 0.94 × 10¹⁰ W/m², q_scv = 10.74 × 10¹⁰ W/m² at v = 100 mm/s) due to its high thermal conductivity, while titanium shows the lowest (q_scm = 0.19 × 10¹⁰ W/m², q_scv = 0.48 × 10¹⁰ W/m² at v = 100 mm/s) as a consequence of strong heat confinement. SS304 and 42CrMo4 occupy intermediate positions, with 42CrMo4 demonstrating notably higher evaporation resistance than SS304 despite similar melting thresholds. The resulting dual-threshold framework delineates three distinct process regimes—sub-melting heating, melting-dominant processing, and evaporation—providing a quantitative basis for parameter selection in applications ranging from surface hardening to micromachining. By bridging the gap between theoretical material science and applied manufacturing, this work offers a robust, first-order reference for process design and establishes a methodological template for future comparative studies of laser–material interactions. Full article

Currently, the non-uniformity of girth weld positions makes their limit state a crucial determinant of pipeline safety. The design method based on the limit state is pivotal in ensuring the integrity and reliability of the pipeline system. Challenges often emerge when determining the limit states of girth welds using semi-empirical formula methods, primarily due to difficulties in accurately identifying influential factors. The quantitative impact of each influence parameter on the crack driving force and the results determined by the semi-empirical formula remain unclear. This study utilizes numerical simulation methods to systematically analyze the quantitative sensitivity laws of critical factors such as crack depth on the crack driving force to address this challenge. The findings revealed that the strength matching coefficient, crack depth, and misalignment are the most significant factors influencing the crack driving force, followed by crack length, softening rate, yield-to-strength ratio, internal pressure, and wall thickness. The effects of tensile strength and outer diameter are relatively minor. A comprehensive database of crack driving forces is constructed using a parameter matrix approach. Combined with the LightGBM machine learning algorithm, a full-scale prediction model for the strain capacity of pipeline girth welds is developed. Predictions for 18 sets of wide-plate test results from the literature confirm the high accuracy of the prediction model, with a prediction accuracy of 6.48%. This research provides a robust reference for accurately determining the limit state of pipeline girth welds and effectively meets the demands of rapidly advancing welding technologies and increasingly complex service environments. Full article

With the increasingly significant impact of high-wind-load environments on power equipment, the wind stability of the corona ring has become a key issue to ensure the safe operation of power grids. The wind-induced vibration response and fatigue characteristics of the corona ring in power equipment under different wind speeds, wind direction angles and wind attack angles are systematically studied via wind tunnel tests and numerical simulation. The results show that the peak acceleration and displacement of the corona ring are positively correlated with the increase in wind speed, and the wind-induced response is the most significant under the condition of 0° wind direction angle and 5° wind attack angle. In the wind speed range of 5 m/s to 8 m/s, the corona ring is prone to vortex-induced vibration. Through fatigue analysis, it is determined that the vertical support rod and the welding position and the bolt connection of the support frame are the stress concentration areas. The research results reveal the key weak points of the corona ring and provide an important basis for optimization design and safety monitoring, and they are of great significance for improving the wind resistance of power equipment. Full article

The present study systematically investigates, for the first time, the combined influences of detonator position (top-edge and bottom-edge initiations) and anvil material (steel and sand) on the interfacial microstructure and mechanical performance of explosively welded A1050/AZ31 dissimilar joints. When welding was conducted using a steel anvil with the detonator positioned at the top edge, significant cracking occurred both at the surface and along the weld interface. In contrast, placing the detonator at the bottom edge noticeably reduced these defects. Moreover, the use of a sand anvil nullified these defects by damping the reflecting shockwaves and minimizing vibrations. Hardness measurements revealed substantial increase at the interface under all the conditions, with the highest value observed with the steel anvil. Welds subjected to top-edge detonation showed higher hardness values compared to those of welds subjected to bottom-edge detonation. Overall, the results suggest that sand anvils with bottom-edge detonation provide the optimal weld quality. The rigid steel anvil reflects the shockwave, generating high pressure and velocity at the interface, whereas the sand anvil absorbs a part of the shock energy, suppressing high-magnitude reflections. The position of the detonator influences the propagation dynamics of the detonation wave and the resulting collision velocity, which in turn, affect the interfacial morphology and overall quality of the weld. Full article

Friction stir welding (FSW) was employed to achieve a reliable joining of 2 mm thick dissimilar metals, 6061 aluminum alloy and AZ31B magnesium alloy. This study revealed the evolution of interfacial interlocking features and their impact on the mechanical properties of the joints under different welding speeds (25–35 mm/min). The results indicate that the Al/Mg FSW joint interface exhibits a strip-like interlaced structure, the morphological characteristics of which are closely related to the welding speed. For quantitative analysis, the ratio of interlocking length to plate thickness (embedding ratio) was used as a quantitative indicator of the structural interlocking feature. As the welding speed increased from 25 mm/min to 35 mm/min, the embedding ratio decreased from 13.2 to 7.9, and the average thickness of the intermetallic compound (IMC) layer decreased from 2.71 μm to 2.19 μm. Transmission Electron Microscopy (TEM) results confirmed that the Al/Mg FSW joint interface consists of a bilayer of IMCs, specifically Al₃Mg₂ and Al₁₂Mg₁₇, with thicknesses of 220 nm and 470 nm, respectively. Tensile testing of joints with different embedding ratios demonstrated that the tensile strength of the welded joint exhibits a positive correlation with the embedding ratio, reaching a maximum of 178 MPa. Full article

As the core component in intelligent manufacturing systems, the precise control of the welding clamp’s electrode pressure plays a decisive role in ensuring the quality of spot welding. This paper proposes a novel pressure control strategy for robotic welding clamp based on partitioned adaptive RBF neural networks: (1) Deformation of the clamp body can lead to deviations in workpiece positioning. To address this issue, a deflection compensation method for robot welding clamp based on the PSO-RBF neural network is proposed. By leveraging pre-calibrated empirical data, the intrinsic mapping relationships are identified, and the derived deflection compensation value is integrated into the real-time position command of the robot end-effector. (2) During electrode motion, the system is subjected to external disturbances such as friction and gravitational forces. So, a sliding mode control strategy incorporating adaptive RBF disturbance compensation is proposed to achieve robust speed regulation. Furthermore, the electrode’s reference velocity is dynamically adjusted based on the welding force error and improved admittance control algorithm, enabling indirect regulation of the welding force to reach the desired set value. The results demonstrate that the proposed composite control strategy reduces electrode pressure overshoot to less than 5% and enhances steady-state control accuracy to ±1.5%. Full article

Under long-term dynamic water pressure, weld zones in vertical shaft pressure-bearing steel pipes are prone to cracking induced by welding residual stresses (WRSs), which may further propagate and threaten structural safety. This study investigates the effects of initial crack angle and position on crack tip stress and propagation path under the influence of WRSs. Using the XFEM combined with a DFLUX-based thermomechanical simulation, a numerical model of crack growth in vertical shaft steel pipes is developed. Results indicate that increasing the initial crack angle raises the stress intensity factor, while crack-tip residual stress initially increases and then decreases, reaching a maximum value of 457.9 MPa when the initial crack angle is 30°. When WRSs are considered, localized stress concentration at the crack tip intensifies, leading to higher stress, stress amplitude, and stress intensity factor, with the amplitude peaking at 365.49 MPa. Moreover, cracks located outside the weld exhibit higher stress intensity factors than those inside. Overall, WRS, crack angle, and crack location all contribute to crack propagation, with crack angle being the dominant factor. Cracks within welds and oriented between 15° and 45° exhibit a significantly higher likelihood of propagation. These findings aid in identifying hazardous crack scenarios and provide guidance for the operation and monitoring of pressure pipelines. Full article

Cup rupture failures in strip steel welds can lead to strip breakage, resulting in unplanned downtime of high-speed continuous rolling mills and scrap steel losses. Manual visual inspection suffers from a high false positive rate and cannot meet the production cycle time requirements. This paper proposes a lightweight online cup rupture visual inspection method based on an improved YOLOv10 algorithm. The backbone feature extraction network is replaced with ShuffleNetV2 to reduce the model’s parameter count and computational complexity. An ECA attention mechanism is incorporated into the backbone network to enhance the model’s focus on cup rupture micro-cracks. A Slim-Neck design is adopted, utilizing a dual optimization with GSConv and VoV-GSCSP, significantly improving the balance between real-time performance and accuracy. Based on the results, the optimized model achieves a precision of 98.8% and a recall of 99.2%, with a mean average precision (mAP) of 99.5%—an improvement of 0.2 percentage points over the baseline. The model has a computational load of 4.4 GFLOPs and a compact size of only 3.24 MB, approximately half that of the original model. On embedded devices, it achieves a real-time inference speed of 122 FPS, which is about 2.5, 11, and 1.8 times faster than SSD, Faster R-CNN, and YOLOv10n, respectively. Therefore, the lightweight model based on the improved YOLOv10 not only enhances detection accuracy but also significantly reduces computational cost and model size, enabling efficient real-time cup rupture detection in industrial production environments on embedded platforms. Full article

Molten pool flow and keyhole status during Variable Polarity Plasma Arc (VPPA) welding directly affect the weld quality and stability. The lack of a clear correlation between them, however, prevents this process approach from being developed further. To investigate the keyhole morphology and liquid metal flow, the experimental examination of fluid flow by the X-ray imaging method and numerical simulation of plasma arc under the effect of the keyhole were carried out. By changing the tungsten electrode setback while keeping all other parameters, it is possible to vary the keyhole status and maintain the consistency of heat input to the base metal. This work establishes a dual-bifurcation flow model to characterize the keyhole molten pool, where the bifurcation point on the keyhole rear wall significantly affects the stability of the keyhole molten pool. The rear wall of the keyhole is divided into three sections from top to bottom, with the arc pressure in the middle section being significantly higher than in the upper and lower sections. As the degree of arc constriction increases—i.e., as arc stiffness or arc force increases—the middle section becomes more vertical. By the calculated distribution of driving forces, the arc pressure has a high possibility of being one of the dominances for the metal flow in keyhole welding of aluminum alloys. Arc pressure is also important for the bifurcation point position, which is closely related to the three welding states: blind keyhole, keyhole, and cutting. Full article

Background: Manganese (Mn) exposure is common in welding and metal-processing occupations and has been implicated in both thyroid disruption and endothelial dysfunction through oxidative and nitric-oxide–related pathways. However, endocrine and vascular biomarkers have rarely been examined together in occupational settings. Methods: In this cross-sectional study, 95 Mn-exposed workers and 95 non-exposed controls were evaluated. Whole-blood Mn, triiodothyronine (T3), thyroxine (T4), thyroid-stimulating hormone (TSH), asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), arginine and citrulline were measured using validated Inductively Coupled Plasma—Mass Spectrometer and chemiluminescent immunoassays. Group differences were assessed using independent samples t-tests, and exposure–biomarker associations were evaluated using Pearson correlations (p < 0.05). Results: Mn-exposed workers had significantly higher blood Mn levels than controls (19.82 ± 4.54 vs. 10.22 ± 3.07 µg/L; p < 0.001). Thyroid hormones (T3, T4, and TSH) were significantly lower among Mn workers, representing a non-classical hormonal pattern, including T3 (2.47 ± 0.31 vs. 3.14 ± 0.42 ng/L; p < 0.001), T4 (1.02 ± 0.13 vs. 1.21 ± 0.18 ng/L; p < 0.001), and TSH (1.75 ± 0.53 vs. 2.88 ± 0.37 mIU/L; p < 0.001). Endothelial biomarkers also differed: ADMA (0.26 ± 0.14 vs. 0.19 ± 0.08 µmol/L; p < 0.001) and SDMA (0.24 ± 0.06 vs. 0.20 ± 0.03 µmol/L; p < 0.001) were higher, while citrulline was lower (18.77 ± 10.23 vs. 22.82 ± 6.70 µmol/L; p = 0.002). In Mn workers, blood Mn showed negative correlations with T3 (r = –0.535, p < 0.01), T4 (r = –0.331, p < 0.01), and TSH (r = –0.652, p < 0.01), and positive correlations with ADMA (r = 0.205, p < 0.05) and SDMA (r = 0.193, p < 0.05). Conclusions: These findings indicate measurable differences in thyroid hormones and dimethylarginine-related endothelial markers among Mn-exposed workers. While the cross-sectional design precludes causal inference, the combined pattern suggests a possible unusual biological response involving both endocrine regulation and nitric-oxide–related pathways. Further longitudinal studies incorporating oxidative stress markers, co-exposure assessment, and functional endothelial testing are needed to clarify the biological relevance of these associations. Full article