Whole-brain mapping of increased manganese levels in welders and its association with exposure and motor function.

Although manganese (Mn) is a trace metal essential for humans, chronic exposure to Mn can cause accumulation of this metal ion in the brain leading to an increased risk of neurological and neurobehavioral health effects. This is a concern for welders exposed to Mn through welding fumes. While brain Mn accumulation in occupational settings has mostly been reported in the basal ganglia, several imaging studies also revealed elevated Mn in other brain areas. Since Mn functions as a magnetic resonance imaging (MRI) T1 contrast agent, we developed a whole-brain MRI approach to map in vivo Mn deposition differences in the brains of non-exposed factory controls and exposed welders. This is a cross-sectional analysis of 23 non-exposed factory controls and 36 exposed full-time welders from the same truck manufacturer. We collected high-resolution 3D MRIs of brain anatomy and R1 relaxation maps to identify regional differences using voxel-based quantification (VBQ) and statistical parametric mapping. Furthermore, we investigated the associations between excess Mn deposition and neuropsychological and motor test performance. Our results indicate that: (1) Using whole-brain MRI relaxometry methods we can generate excess Mn deposition maps in vivo, (2) excess Mn accumulation due to occupational exposure occurs beyond the basal ganglia in cortical areas associated with motor and cognitive functions, (3) Mn likely diffuses along white matter tracts in the brain, and (4) Mn deposition in specific brain regions is associated with exposure (cerebellum and frontal cortex) and motor metrics (cerebellum and hippocampus).

Reconfiguration, Welding, Reprogramming, and Complex Shape Transformation of An Optical Shape Memory Polymer Network Enabled by Patterned Secondary Crosslinking.

Stimuli-triggered generation of complicated 3D shapes from 2D strips or plates without using sophisticated molds is desirable and achieving such 2D-to-3D shape transformation in combination with shape reconfiguration, welding, and reprogramming on a single material is very challenging. Here, a convenient and facile strategy using the solution of a disulfide-containing diamine for patterned secondary crosslinking of an optical shape-memory polymer network is developed to integrate the above performances. The dangling thiolectones attached to the backbones react with the diamine in the solution-deposited region so that the secondary crosslinking may not only weld individual strips into assembled 3D shapes but also suppress the relaxation of the deformed polymer chains to different extents for shape reconfiguration or heating-induced complex 3D deformations. In addition, as the dynamic disulfide bonds can be thermally activated to erase the initial programming information and the excessive thiolectones are available for subsequent patterned crosslinking, the material also allows shape reprogramming. Combining welding with patterning treatment, it is further demonstrated that a gripper can be assembled and photothermally controlled to readily grasp an object.

Pressureless Welding of Temperature-Invariant Multifunctionality Body Based on Hydroxyl-Functionalized Boron Nitride Nanosheets and Bifunctional Monoethanolamine Cross-linker.

Bulk hexagonal boron nitride (h-BN) ceramics with structural integrity, high-temperature resistance and low expansion rate are expected for multifunctional applications in extreme conditions. However, due to its sluggish self-diffusion and intrinsic inertness, it remains a great challenge to overcome high-energy barrier for h-BN powder sintering. Herein, a cross-linking and pressureless-welding strategy is reported to produce bulk boron nitride nanosheets (BNNSs) ceramics with well-crystalized and dense B-N covalent-welding frameworks. The essence of this synthesis strategy lies in the construction of >B─O─H2C─H2C─H2N:→B< bond bridge connection structure among hydroxyl functionalized BNNSs (BNNSs-OH) using bifunctional monoethanolamine (MEA) as cross-linker through esterification and intermolecular-coordination reactions. The prepared BNNSs-interlaced ceramics have densities not less than 1.2 g cm-3, and exhibit exceptional mechanical robustness and resiliency, excellent thermomechanical stability, ultra-low linear thermal expansion coefficient of 0.06 ppm °C-1, and high thermal diffusion coefficient of 4.76 mm2 s-1 at 25 °C and 3.72 mm2 s-1 at 450 °C. This research not only reduces the free energy barrier from h-BN particles to bulk ceramics through facile multi-step physicochemical reaction, but also stimulates further exploration of multifunctional applications for bulk h-BN ceramics over a wide temperature range.

TERT-independent telomere elongation and shelterin dysregulation after pulmonary exposure to stainless-steel welding fume in-vivo.

Telomeres are inert DNA sequences (TTAGGG) at the end of chromosomes that protect genetic information and maintain DNA integrity. Emerging evidence has demonstrated that telomere alteration can be closely related to occupational exposure and the development of various disease conditions, including cancer. However, the functions and underlying molecular mechanisms of telomere alteration and shelterin dysregulation after welding fume exposures have not been broadly defined. In this study, we analyzed telomere length and shelterin complex proteins in peripheral blood mononuclear cells (PBMCs) and in lung tissue recovered from male Sprague-Dawley rats following exposure by intratracheal instillation (ITI) to 2 mg/rat of manual metal arc-stainless steel (MMA-SS) welding fume particulate or saline (vehicle control). PBMCs and lung tissue were harvested at 30 d after instillation. Our study identified telomere elongation and shelterin dysregulation in PBMCs and lung tissue after welding fume exposure. Mechanistically, telomere elongation was independent of telomerase reverse transcriptase (TERT) activation. Collectively, our findings demonstrated that welding fume-induced telomere elongation was (a) TERT-independent and (b) associated with shelterin complex dysregulation. It is possible that an alteration of telomere length and its regulatory proteins may be utilized as predictive biomarkers for various disease conditions after welding fume exposure. This needs further investigation.

Assessing the influence of parameters on tissue welding in small bowel end-to-end anastomosis in vitro and in vivo.

BACKGROUND: The use of high-frequency electric welding technology for intestinal end-to-end anastomosis holds significant promise. Past studies have focused on in vitro, and the safety and efficacy of this technology is uncertain, severely limiting the clinical application of this technology. This study investigates the impact of compression pressure, energy dosage, and duration on anastomotic quality using a homemade anastomosis device in both in vitro and in vivo settings. METHODS: Two hundred eighty intestines and 5 experimental pigs were used for in vitro and in vivo experiments, respectively. The in vitro experiments were conducted to study the effects of initial pressure (50-400 kpa), voltage (40-60 V), and time (10-20 s) on burst pressure, breaking strength, thermal damage, and histopathological microstructure of the anastomosis. Optimal parameters were then inlaid into a homemade anastomosis and used for in vivo experiments to study the postoperative porcine survival rate and the pathological structure of the tissues at the anastomosis and the characteristics of the collagen fibers. RESULTS: The anastomotic strength was highest when the compression pressure was 250 kPa, the voltage was 60 V, and the time was 15 s. The degree of thermal damage to the surrounding tissues was the lowest. The experimental pigs had no adverse reactions after the operation, and the survival rate was 100%. 30 days after the operation, the surgical site healed well, and the tissues at the anastomosis changed from immediate adhesions to permanent connections. CONCLUSION: High-frequency electric welding technology has a certain degree of safety and effectiveness. It has the potential to replace the stapler anastomosis in future and become the next generation of new anastomosis device.

Enhancement of keratinocyte survival and migration elicited by interleukin 24 upregulation in dermal microvascular endothelium upon welding-fume exposure.

Occupational exposure to welding fumes constitutes a serious health concern. Although the effects of fumes on the respiratory tract have been investigated, few apparent reports were published on their effects on the skin. The purpose of this study was to investigate the effects of exposure to welding fumes on skin cells, focusing on interleukin-24 (IL-24), a cytokine involved in the pathophysiology of skin conditions, such as atopic dermatitis and psoriasis. Treatment with welding fumes increased IL-24 expression and production levels in human dermal microvascular endothelial cells (HDMEC) which were higher than that in normal human epidermal keratinocytes. IL-24 levels in Trolox and deferoxamine markedly suppressed welding fume-induced IL-24 expression in HDMEC, indicating that oxidative stress may be involved in this cytokine expression. IL-24 released from HDMEC protected keratinocytes from welding fume-induced damage and enhanced keratinocyte migration. Serum IL-24 was higher in welding workers than in general subjects and was positively correlated with elevated serum levels of 8-hydroxy-2'-deoxyguanosine, an oxidative stress marker. In summary, welding fumes enhanced IL-24 expression in HDMEC, stimulating keratinocyte survival and migration. IL-24 expression in endothelial cells may act as an adaptive response to welding-fume exposure in the skin.

Factors influencing metal concentrations in hair and nails during longitudinal follow-up of apprentice welders.

The aim of this study was to determine factors influencing observed increased metal biomarkers of exposure levels in a group of 116 Quebec apprentice welders during a longitudinal follow-up of exposure. Analysis of 14 metals was carried out in hair, fingernail, and toenail samples taken from participants over the course of their welding curriculum at 6 different times. Personal and socio-demographic characteristics, lifestyle habits, and other potential confounding factors were documented by questionnaire. Multivariate linear mixed-effect models were used to assess main predictors of metal concentrations in each biological matrix including increasing time of exposure throughout the curriculum (defined as the repeated measure "time" variable"). Significant associations between repeated measure "time" variable and metal levels in hair, fingernails, and toenails were found for chromium, iron, manganese and nickel. Significant associations with "time" were also noted for arsenic levels in hair and fingernails, and for barium, cobalt and vanadium levels in fingernails and toenails. The repeated measure "time" variable, hence increasing time of exposure throughout the curriculum, was the predominant predictor of elevated biological metal levels. Reduced spaces and simultaneous activities such as oxyfuel-cutting and welding in the same welding room were suspected to contribute to higher metal levels. Age, ethnicity, and annual household income exerted an effect on metal levels and considered as confounders in the models. Variations observed in metal levels between hair and nails of apprentice welders also emphasized the relevance and importance of performing multi-matrix and multi-element biomonitoring to assess temporal variations in biological metal concentrations during welding curriculum.

Impact of welding occupation on serum aluminium level and its association with physical health, cognitive function, and quality of life: a cross-sectional study.

OBJECTIVE: There is an occupational health concern about welders' inhalation of toxic aluminium fumes. We investigated whether serum aluminium level (SAL) and demographic variables can significantly predict physical health parameters, cognition, and quality of life (QoL) among welders. METHODS: The cross-sectional study involved 100 age- and location-matched men (50 welders and 50 non-welders). SAL obtained using a graphite furnace atomic absorption spectrometer, and data collected using blood pressure and body mass index (BMI) apparatuses, biodata form, pain rating scale, General Practitioner Assessment of Cognition, WHOQoL-BREF, and Nordic musculoskeletal symptoms (MSS) questionnaire were analysed using independent samples t test, chi-square, Pearson's correlation, and hierarchical linear regression. RESULTS: Welders had significantly higher SAL (mean difference [MD] = 1.77 µg/L, p < 0.001), lower QoL (MD = 3.92, p = 0.039), and higher prevalence of MSS on the neck (χ2 = 10.187, p = 0.001), shoulder (χ2 = 9.007, p = 0.003), upper back (χ2 = 6.832, p = 0.009), and knee (χ2 = 12.190, p < 0.001) than non-welders. There was a significant bivariate association between SAL, systolic blood pressure (ß = 0.313, p = 0.002), and BMI (ß = 0.279, p = 0.005), but not pain intensity, cognition, or QoL. SAL remained a significant predictor of systolic blood pressure after adjustment for physical health and QoL parameters (ß = 0.191, p = 0.044). The association between SAL and social QoL became significant after adjustment for physical health and other QoL domains (ß = - 0.210, p = 0.032) and demographic variables (ß = - 0.233, p = 0.046). CONCLUSION: Welders had significantly higher SAL, musculoskeletal symptoms, blood pressure, and lower QoL than non-welders. SAL was associated with adverse physical health parameters and social-related QoL, not cognition. We recommend routine aluminium bioavailability and physical health checks among welders.

Welding fume exposure and prevalence of chronic respiratory symptoms among welders in micro- and small-scale enterprise in Akaki Kality sub-city, Addis Ababa, Ethiopia: a comparative cross-sectional study.

BACKGROUND: Exposure to welding fumes can lead to different respiratory health disorders, including lung cancer, due to long-term exposures. In Ethiopia, large numbers of people are engaged in the welding sector. Often, these workers are exposed to welding fumes at their workplaces, however, the level of exposure and its health effects have never been studied. OBJECTIVE: To measure the level of personal welding fume exposure and assess chronic respiratory symptoms and associated factors, among micro and small-scale enterprise metal workshop workers, in Akaki Kality Sub city, Ethiopia. METHODS: A comparative cross-sectional study involving 226 welders and 217 controls. Chronic respiratory symptoms were assessed using a standardized questionnaire adopted from the American Thoracic Society (ATS). Welding fumes were collected from the welder's breathing zone using 37 mm close-faced plastic cassettes fitted with Polyvinyl Chloride (PVC) filters connected to Casella pumps at an airflow rate of 2 L/min. RESULT: The overall prevalence of chronic respiratory symptoms among welders and controls were 54 (23.9%) and 20 (9.2%) respectively. The geometric mean and geometric standard deviation (GSD) of personal welding fume exposure, among welders was 5.98 mg/m3 (± GSD = 1.54). In this study, 53.3% of the samples exceeded the Occupational Exposure Limit defined by the American Conference of Governmental Industrial Hygiene. Chronic respiratory symptoms were significantly associated with educational status (Adjusted Odds Ratio (AOR): 5.11, 95% CI: 1.35, 19.33), respiratory protective equipment use (AOR: 3.33, 95% CI: 1.52, 7.31), safety training (AOR: 2.41, 95% CI: 1.10, 5.28), smoking (AOR:3.57, 95% CI: 1.54, 8.23), welding machine maintenance (AOR: 1.87, 95% CI: 1.01, 3.59) and welding site (i.e. indoors vs. outdoor) (AOR: 6.85. 95% CI: 2.36, 19.89). CONCLUSIONS: The prevalence of chronic respiratory symptoms among welding workers was significantly higher than controls. More than half of the samples exceeded the Occupational Exposure Limit. Educational status, implementation of safety training, and welding sites were significantly associated with chronic respiratory symptoms. The results suggested a need to reduce welding fume exposure to improve the respiratory health of the workers.

Neurological and neurobehavioral effects of welders in Egypt exposed to manganese containing welding fumes.

PURPOSE: Welders are more likely to develop neurobehavioral disorders because of their exposure to neurotoxic metals such as manganese. This study aimed to measure the neurobehavioral performance of welders occupationally exposed to manganese at welding enterprises and its relationship with the workplace environment. METHODS: It is a comparative cross-sectional study carried out on 130 welders working at 50 welding enterprises in Menoufia governorate, Egypt, compared to 130 non-occupationally exposed controls. RESULTS: It was found that the environments of the studied welding enterprises had levels of respirable dust, manganese, and total welding fumes that exceeded internationally permissible limits. In addition, the mean blood manganese levels were significantly higher among welders (4.16 ± 0.61) than the controls (1.72 ± 0.41). Welders had a significantly higher prevalence of neurological manifestations and lower performance of neurobehavioral tests. Lower neurobehavioral performance among welders was significantly correlated with increased work duration and blood levels in some tests. CONCLUSION: To lessen the fumes in the breathing zone of workers, it is therefore strongly recommended to regularly wear high-quality personal protective equipment, especially masks, and to ensure proper ventilation.

Longitudinal changes in cardiovascular disease-related proteins in welders.

OBJECTIVE: Occupational exposure to welding fumes is linked to a higher risk of cardiovascular disease; however, the threshold exposure level is unknown. Here, we aimed to identify changes in proteins associated with cardiovascular disease in relation to exposure to welding fumes. METHODS: Data were obtained from two timepoints six years apart for 338 non-smoking men (171 welders, 167 controls); of these, 174 (78 welders, 96 controls) had measurements available at both timepoints. Exposure was measured as personal respirable dust (adjusted for personal protective equipment), welding years, and cumulative exposure. Proximity extension assays were used to measure a panel of 92 proteins involved in cardiovascular processes in serum samples. Linear mixed models were used for longitudinal analysis. The biological functions and diseases related to the identified proteins were explored using the Ingenuity Pathway Analysis software. RESULTS: At both timepoints, the median respirable dust exposure was 0.7 mg/m3 for the welders. Seven proteins were differentially abundant between the welders and controls and increased incrementally with respirable dust: FGF23, CEACAM8, CD40L, PGF, CXCL1, CD84, and HO1. CD84 was significant after adjusting for multiple comparisons. These proteins have been linked to disorders of blood pressure, damage related to clogged blood vessels, and chronic inflammatory disorders. CONCLUSION: Exposure to mild steel welding fumes below current occupational exposure limits for respirable particles and welding fumes in Europe and the US (1-5 mg/m3) was associated with changes in the abundance of proteins related to cardiovascular disease. Further research should evaluate the utility of these proteins as prospective biomarkers of occupational cardiovascular disease.

Effects of occupational exposure to metal fume PM2.5 on lung function and biomarkers among shipyard workers: a 3-year prospective cohort study.

OBJECTIVE: This study investigates the associations of α1-antitrypsin, inter-α-trypsin inhibitor heavy chain (ITIH4), and 8-isoprostane with lung function in shipyard workers exposed to occupational metal fume fine particulate matter (PM2.5), which is known to be associated with adverse respiratory outcomes. METHODS: A 3-year follow-up study was conducted on 180 shipyard workers with 262 measurements. Personal exposure to welding fume PM2.5 was collected for an 8-h working day. Pre-exposure, post-exposure, and delta (∆) levels of α1-antitrypsin, ITIH4, and 8-isoprostane were determined in urine using enzyme-linked immunosorbent assays. Post-exposure urinary metals were sampled at the beginning of the next working day and analyzed by inductively coupled plasma-mass spectrometry. Lung function measurements were also conducted the next working day for post-exposure. RESULTS: An IQR increase in PM2.5 was associated with decreases of 2.157% in FEV1, 2.806% in PEF, 4.328% in FEF25%, 5.047% in FEF50%, and 7.205% in FEF75%. An IQR increase in PM2.5 led to increases of 42.155 µg/g in ∆α1-antitrypsin and 16.273 µg/g in ∆ITIH4. Notably, IQR increases in various urinary metals were associated with increases in specific biomarkers, such as post-urinary α1-antitrypsin and ITIH4. Moreover, increases in ∆ α1-antitrypsin and ∆ITIH4 were associated with decreases in FEV1/FVC by 0.008% and 0.020%, respectively, and an increase in ∆8-isoprostane resulted in a 1.538% decline in FVC. CONCLUSION: Our study suggests that urinary α1-antitrypsin and ITIH4 could indicate early lung function decline in shipyard workers exposed to metal fume PM2.5, underscoring the need for better safety and health monitoring to reduce respiratory risks.

Risk of bladder, kidney and prostate cancer from occupational exposure to welding fumes: a systematic review and meta-analysis.

BACKGROUND: Our aimed to conduct a meta-analysis of cohort studies on risk of genitourinary (GU) cancers in workers exposed to welding fumes (WF). METHODS: We performed a systematic review of studies published on Pubmed, Scopus and Embase following PRISMA criteria. Two researchers selected cohort studies on WF exposure. From 2582 articles, 7 non-overlapping studies were included. Quality of studies was scored according to CASP. We run a random effects meta-analysis to calculate the relative risk (RR) and 95% confidence intervals (CI) of GU cancer, overall and stratified by cancer, country, and quality score. RESULTS: We included seven studies reporting results on GU cancers, including prostate, bladder and kidney cancer (PC, BC, and KC). The RR was 1.19 (95% CI = 1.07-1.32, 16 risk estimates) for GU cancer; 1.13 (95% CI = 0.90-1.42, 4 risk estimates) for PC; 1.26 (95% CI = 0.98-1.60, 7 risk estimates) for BC and 1.28 (95% CI = 1.12-1.47, 5 risk estimates) for KC. Heterogeneity was present in all meta-analyses (p < 0.001). The increased risk was more pronounced in North American than in European studies (respectively, OR = 1.35, 95% CI = 1.18-1.55; OR = 1.13, 95% CI = 1.01-1.27 p heterogeneity = 0.03). There was no heterogeneity according to quality score (p = 0.4). Data were insufficient to investigate associations by industry or welding type. Publication bias for each cancer was excluded. CONCLUSION: This meta-analysis suggests increased risk of KC and BC, but not of PC, in workers exposed to WF. Confounding by other occupational and non-occupational risk factors could not be excluded. Data were not adequate to address the risk of specific exposure circumstances.

Digital Twin Virtual Welding Approach of Robotic Friction Stir Welding Based on Co-Simulation of FEA Model and Robotic Model.

Robotic friction stir welding has become an important research direction in friction stir welding technology. However, the low stiffness of serial industrial robots leads to substantial, difficult-to-measure end-effector deviations under the welding forces during the friction stir welding process, impacting the welding quality. To more effectively measure the deviations in the end-effector, this study introduces a digital twin model based on the five-dimensional digital twin theory. The model obtains the current data of the robot and six-axis force sensor and calculates the real-time end deviations using the robot model. Based on this, a virtual welding model was realized by integrating the FEA model with the digital twin model using a co-simulation approach. This model achieves pre-process simulation by iteratively cycling through the simulated force from the FEA model and the end displacement from the robot model. The virtual welding model effectively predicts the welding outcomes with a mere 6.9% error in lateral deviation compared to actual welding, demonstrating its potential in optimizing welding parameters and enhancing accuracy and quality. Employing digital twin models to monitor, simulate, and optimize the welding process can reduce risks, save costs, and improve efficiency, providing new perspectives for optimizing robotic friction stir welding processes.

Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates.

Maintaining high-quality welded connections is crucial in many industries. One of the challenges is assessing the mechanical properties of a joint during its production phase. Currently, in industrial practice, this occurs through NDT (non-destructive testing) conducted after the production process. This article proposes the use of a virtual sensor, which, based on temperature distributions observed on the joint surface during the welding process, allows for the determination of hardness distribution across the cross-section of a joint. Welding trials were conducted with temperature recording, hardness measurements were taken, and then, neural networks with different hyperparameters were tested and evaluated. As a basis for developing a virtual sensor, LSTM networks were utilized, which can be applied to time series prediction, as in the analyzed case of hardness value sequences across the cross-section of a welded joint. Through the analysis of the obtained results, it was determined that the developed virtual sensor can be applied to predict global temperature changes in the weld area, in terms of both its value and geometry changes, with the mean average error being less than 20 HV (mean for model ~35 HV). However, in its current form, predicting local hardness disturbances resulting from process instabilities and defects is not feasible.

Welding Defect Monitoring Based on Multi-Scale Feature Fusion of Molten Pool Videos.

Real-time quality monitoring through molten pool images is a critical focus in researching high-quality, intelligent automated welding. However, challenges such as the dynamic nature of the molten pool, changes in camera perspective, and variations in pool shape make defect detection using single-frame images difficult. We propose a multi-scale fusion method for defect monitoring based on molten pool videos to address these issues. This method analyzes the temporal changes in light spots on the molten pool surface, transferring features between frames to capture dynamic behavior. Our approach employs multi-scale feature fusion using row and column convolutions along with a gated fusion module to accommodate variations in pool size and position, enabling the detection of light spot changes of different sizes and directions from coarse to fine. Additionally, incorporating mixed attention with row and column features enables the model to capture the characteristics of the molten pool more efficiently. Our method achieves an accuracy of 97.416% on a molten pool video dataset, with a processing time of 16 ms per sample. Experimental results on the UCF101-24 and JHMDB datasets also demonstrate the method's generalization capability.

Ultrasonic Weld Quality Inspection Involving Strength Prediction and Defect Detection in Data-Constrained Training Environments.

Welding is an extensively used technique in manufacturing, and as for every other process, there is the potential for defects in the weld joint that could be catastrophic to the manufactured products. Different welding processes use different parameter settings, which greatly impact the quality of the final welded products. The focus of research in weld defect detection is to develop a non-destructive testing method for weld quality assessment based on observing the weld with an RGB camera. Deep learning techniques have been widely used in the domain of weld defect detection in recent times, but the majority of them use, for example, X-ray images. An RGB image-based solution is attractive, as RGB cameras are comparatively inexpensive compared to X-ray image solutions. However, the number of publicly available RGB image datasets for weld defect detection is comparatively lower than that of X-ray image datasets. This work achieves a complete weld quality assessment involving lap shear strength prediction and visual weld defect detection from an extremely limited dataset. First, a multimodal dataset is generated by the fusion of image data features extracted using a convolutional autoencoder (CAE) designed in this experiment and input parameter settings data. The fusion of the dataset reduced lap shear strength (LSS) prediction errors by 34% compared to prediction errors using only input parameter settings data. This is a promising result, considering the extremely small dataset size. This work also achieves visual weld defect detection on the same limited dataset with the help of an ultrasonic weld defect dataset generated using offline and online data augmentation. The weld defect detection achieves an accuracy of 74%, again a promising result that meets standard requirements. The combination of lap shear strength prediction and visual defect detection leads to a complete inspection to avoid premature failure of the ultrasonic weld joints. The weld defect detection was compared against the publicly available image dataset for surface defect detection.

Passive Vision Detection of Torch Pose in Swing Arc Narrow Gap Welding.

To enhance the synchronous detection of the horizontal and vertical positions of the torch in swing arc narrow gap welding, a torch pose detection (TPD) method is proposed. This approach utilizes passive visual sensing to capture images of the arc on the groove sidewall, using advanced image processing methods to extract and fit the arc contour. The coordinates of the arc contour center point and the highest point are determined through the arc contour fitting line. The torch center position is calculated from the average horizontal coordinates of the arc contour centers in adjacent welding images, while the height position is determined from the vertical coordinate of the arc's highest point. Experimental validation in both variable and constant groove welding conditions demonstrated the TPD method's accuracy within 0.32 mm for detecting the torch center position. This method eliminates the need to construct the wire centerline, which was a requirement in previous approaches, thereby reducing the impact of wire straightness on detection accuracy. The proposed TPD method successfully achieves simultaneous detection of the torch center and height positions, laying the foundation for intelligent detection and adaptive control in swing arc narrow gap welding.

Investigating the Mechanical Properties and Temperature Compensation of a Spot-Welded Strain Sensor within an Intelligent Steel Strand Cable.

According to current regulations, welding is strictly prohibited for prestressed and tension cables. In response, this article proposes the use of a portable spot-welding machine to spot weld steel strands. This method generates a small current during spot welding, with a voltage of only 3 V to 5 V, and does not damage the internal structure of the steel strand. To effectively monitor cable tension in cable-supported structures, a novel approach utilizing a chip-based, encapsulated spot-welded strain sensor was investigated. The strain sensing capability, temperature sensitivity, stress relaxation, and static load responses were investigated on the proposed smart steel strand cables with spot-welded strain sensors. The theoretical analyses and finite element simulations revealed that the strain transfer efficiency of the spot-welded strain sensor exceeded 96%. The experimental results demonstrated that the load-strain relationship of the smart steel strand cable had a fitting degree greater than 0.999, and the tension errors obtained under different loads were within 1.26%. The tension full capacity errors measured at different temperatures were generally within 1.0%. The relaxation rate of the smart steel strand cable after 120 h was 3.78% and reduced the sensor accuracy error by 3.97%. Thus, the proposed strain sensor equipped with a smart steel strand cable is suitable for use in long-term tension monitoring.

Experimental Analyses and Predictive Modelling of Ultrasonic Welding Parameters for Enhancing Smart Textile Fabrication.

This study aims to illustrate the design, fabrication, and optimisation of an ultrasonic welding (UW) machine to join copper wires with non-woven PVC textiles as smart textiles. The study explicitly evaluates UW parameters' impact on heat generation, joint strength, and electrical properties, with a comprehensive understanding of the process dynamics and developing a predictive model applicable to smart textiles. The methodological approach involved designing and manufacturing an ultrasonic piezoelectric transducer using ABAQUS finite element analyses (FEA) software and constructing a UW machine for the current purpose. The full factorial design (FFD) approach was employed in experiments to systematically assess the influence of welding time, welding pressure, and copper wire diameter on the produced joints. Experimental data were meticulously collected, and a backpropagation neural network (BPNN) model was constructed based on the analysis of these results. The results of the experimental investigation provided valuable insights into the UW process, elucidating the intricate relationship between welding parameters and heat generation, joint strength, and post-welding electrical properties of the copper wires. This dataset served as the basis for developing a neural network model, showcasing a high level of accuracy in predicting welding outcomes compared to the FFD model. The neural network model provides a valuable tool for controlling and optimising the UW process in the realm of smart textile production.