Welding in the World最新文献_第2页

High-quality forming of laser DED components based on a multi-source sensing system 基于多源传感系统的激光 DED 组件的高质量成型

IF 2.4 4区材料科学

Welding in the World Pub Date : 2025-02-16 DOI: 10.1007/s40194-025-01937-3

Xu Li, Jiehao Shen, Kanghong Zhu, Huabin Chen

{"title":"High-quality forming of laser DED components based on a multi-source sensing system","authors":"Xu Li, Jiehao Shen, Kanghong Zhu, Huabin Chen","doi":"10.1007/s40194-025-01937-3","DOIUrl":"10.1007/s40194-025-01937-3","url":null,"abstract":"<div><p>Laser directed energy deposition (LDED) is an additive manufacturing technology that uses a laser as the energy source to create a liquid melt pool in the deposition area, which rapidly moves it, melting powder and depositing layers sequentially. Given that the LDED process involves intense energy exchange and complex physicochemical changes, the quality control of the formed parts and the repeatability of the process are common technical challenges for its large-scale application. This paper establishes an integrated in situ monitoring system for LDED, which can monitor the geometric characteristics of the liquid melt pool, temperature, and high-temperature strain on the side walls of the formed parts, through a temperature sensing unit, a visual sensing unit, and a strain unit based on digital image correlation algorithm. Based on the information obtained from multi-source sensing of the cladding process, we compared the stability and forming quality of the cladding process under different process parameter paths and identified a process path that yields more stable melt pool temperatures, reduced fluctuations in melt pool dimensions, and lower peak strains on the build sidewalls; the maximum strain eyy and exx were 23% and 20% lower; and the strain fluctuation range of eyy and exx was found to be 45.65% and 26.49% lower compared to components built before process optimization, thereby achieving high-quality construction manufacturing when building cladding components of the same size.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"69 5","pages":"1207 - 1218"},"PeriodicalIF":2.4,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Surrogate model and machine learning approaches for thermal field reconstruction from weld pool contour: application to GTA welding 从焊池轮廓重建热场的代用模型和机器学习方法：应用于 GTA 焊接

IF 2.4 4区材料科学

Welding in the World Pub Date : 2025-02-15 DOI: 10.1007/s40194-025-01969-9

Zaid Boutaleb, Issam Bendaoud, Sébastien Rouquette, Fabien Soulié

{"title":"Surrogate model and machine learning approaches for thermal field reconstruction from weld pool contour: application to GTA welding","authors":"Zaid Boutaleb, Issam Bendaoud, Sébastien Rouquette, Fabien Soulié","doi":"10.1007/s40194-025-01969-9","DOIUrl":"10.1007/s40194-025-01969-9","url":null,"abstract":"<div><p>Thermal cycles in arc welding are crucial as they determine the metallurgy, residual stresses, and distortions of welded parts. Experimentally measuring the temperature everywhere in the welded parts is not possible. This can be achieved with a thermal simulation but finite element analysis requires long computational times, especially for large parts. This study aimed to predict the thermal field using a data-driven approach using numerical and experimental data. First, thermal modeling is defined and arc heating is described with an equivalent heat source. The numerical design of experiments was conducted by varying the heat source parameters. The weld pool contour is extracted from each simulation for building a numerical dataset. The numerical dataset is used for training a surrogate model. The surrogate model is used for estimating the heat source parameters from the weld pool contour using an optimization technique. Then, a K-nearest neighbors algorithm is used to predict the thermal field from the estimated heat source parameters. A significant reduction in computational time is obtained for predicting the thermal field from experimental weld pool contour. Numerical analysis showed that the predicted thermal field is fairly good in the solid than in the weld pool.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"69 5","pages":"1291 - 1307"},"PeriodicalIF":2.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monitoring process parameters and predicting rail steel welded joint microstructure and mechanical property of three-wire fusion nozzle electroslag welding

IF 2.4 4区材料科学

Welding in the World Pub Date : 2025-02-14 DOI: 10.1007/s40194-025-01962-2

Shengfu Yu, Yang Wang, Zhongyi Zhang

{"title":"Monitoring process parameters and predicting rail steel welded joint microstructure and mechanical property of three-wire fusion nozzle electroslag welding","authors":"Shengfu Yu, Yang Wang, Zhongyi Zhang","doi":"10.1007/s40194-025-01962-2","DOIUrl":"10.1007/s40194-025-01962-2","url":null,"abstract":"<div><p>This study explores the impact of an innovative three-wire fusion nozzle electroslag welding (FNESW) technique on the microstructural evolution and tensile properties of U75V pearlitic steel rail weld joints. An intelligent monitoring system was developed to systematically capture critical welding parameters, including current, voltage, cooling rate, and magnetic field intensity. Furthermore, a Back Propagation (BP) neural network model was designed and trained to predict the microstructural features and mechanical properties of the welded joints. The model exhibited robust predictive performance, effectively establishing the quantitative relationship between welding parameters and joint performance. Experimental validation corroborated the model’s reliability, with relative errors of key predictive indicators maintained below 15%. The findings provide a scientific basis for optimizing welding parameters and designing high-performance steel rail weld joints through the integration of machine learning techniques, offering new insights into the intelligent control of welding processes.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"69 5","pages":"1229 - 1240"},"PeriodicalIF":2.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent monitoring and fuzzy control of MIG welding seam tracking based on passive visual sensing

IF 2.4 4区材料科学

Welding in the World Pub Date : 2025-02-13 DOI: 10.1007/s40194-025-01938-2

Ming Zhu, Qingsong Ma, Runji Lei, Jun Weng, Yu Shi

{"title":"Intelligent monitoring and fuzzy control of MIG welding seam tracking based on passive visual sensing","authors":"Ming Zhu, Qingsong Ma, Runji Lei, Jun Weng, Yu Shi","doi":"10.1007/s40194-025-01938-2","DOIUrl":"10.1007/s40194-025-01938-2","url":null,"abstract":"<div><p>To reduce the risk of personnel operation and further improve welding efficiency, weld seam tracking in MIG welding process arc has to be developed for automatic manufacturing. Weld seam tracking system mainly contains intelligent monitoring and fuzzy control. For monitoring part, an optical testing platform and a passive visual detecting device are established to analyze groove and arc position. Also, preprocessing workflow and adaptive enhancement algorithm are built to increase image gray values. Deep learning program is used to select and locate interest area to improve the accuracy of detection. The arc position calculation model is also proposed to extract geographic location. For control part, based on welder’s operation skills, fuzzy logic rules are programmed to control the arc position at the middle of gap. Also, control experiments are carried out and compared with manual adjustment. Results show that: (1) with preprocessing workflow and adaptive enhancement algorithm, the average gray value of the groove area and the arc area increased by 114% and 100%; (2) by using deep learning, the interest area contains information of groove shape and oscillating arc position and could be selected accurately, and the mAP index is as high as 99.27%; and (3) based on the preset deviation test, the pixel error of the alignment deviation detection is within 8 pixels. And with the alignment deviation, distance can be controlled between ± 0.5 mm.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"69 5","pages":"1437 - 1445"},"PeriodicalIF":2.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vision-driven adaptive welding solutions for the top three challenges in welding fabrication

IF 2.4 4区材料科学

Welding in the World Pub Date : 2025-02-12 DOI: 10.1007/s40194-025-01968-w

Mahyar Asadi, Ahmad Ashoori, Mehrnoosh Afshar, Ali Sheikhshab, Todd Scheerer, Austin Kaspardlov, Soroush Bagheri, Sina Firouz, Soroush Karimzadeh

{"title":"Vision-driven adaptive welding solutions for the top three challenges in welding fabrication","authors":"Mahyar Asadi, Ahmad Ashoori, Mehrnoosh Afshar, Ali Sheikhshab, Todd Scheerer, Austin Kaspardlov, Soroush Bagheri, Sina Firouz, Soroush Karimzadeh","doi":"10.1007/s40194-025-01968-w","DOIUrl":"10.1007/s40194-025-01968-w","url":null,"abstract":"<div><p>With experience in more than over 100 robotic deployments in pipe prefabrication and a decade-long dedication to welding automation, we have pinpointed the key challenges, notably fit-up variation, tack adaptation, and live seam tracking. We engineered an innovative adaptive welding solution that integrates the perceptual and cognitive abilities of welders into articulated robots. This system dynamically responds to real-time welding scenarios, effectively tackling associated challenges. Unlike existing methods reliant on pre-scanning or laser readings before welding, our vision-based adaptive welding technology operates instantaneously, replicating the expertise of proficient human welders. The outcome is a consistent delivery of high-quality welds. Given the widespread advancement of AI, the heart of the adaptive welding system must skillfully manage diverse welding conditions, covering different joint preparations, types, positions, thicknesses, materials, and beyond. Addressing the necessity of training the AI core requires navigating through diverse practical challenges in deployments. Leveraging our expertise in deploying various methodologies, we ultimately provide an efficient solution for training the welding AI, primed for widespread deployment across high-mix low-volume applications. This solution incorporates a data tracing and monitoring platform across deployments, enhancing ERP (Enterprise Resource Planning) functionality, and providing insights into welding operations, historical performance analytics, and problem tracking with proactive improvements.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"69 5","pages":"1277 - 1289"},"PeriodicalIF":2.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of heat treatment parameters on SLM-fabricated GNPs/IN718 composites: microstructural evolution and mechanical properties

IF 2.4 4区材料科学

Welding in the World Pub Date : 2025-02-10 DOI: 10.1007/s40194-025-01958-y

Yang Chu, Haichuan Shi, Peilei Zhang, Boyu Wang, Zhishui Yu, Hua Yan, Qinghua Lu, Kaichang Yu, Zhaolong Li, Yu Lei

{"title":"Effect of heat treatment parameters on SLM-fabricated GNPs/IN718 composites: microstructural evolution and mechanical properties","authors":"Yang Chu, Haichuan Shi, Peilei Zhang, Boyu Wang, Zhishui Yu, Hua Yan, Qinghua Lu, Kaichang Yu, Zhaolong Li, Yu Lei","doi":"10.1007/s40194-025-01958-y","DOIUrl":"10.1007/s40194-025-01958-y","url":null,"abstract":"<div><p>This paper systematically investigated the effects of solution and solution aging treatments on the microstructure evolution and mechanical properties of selective laser melting (SLM) graphene nanoplatelets (GNPs)–reinforced Inconel 718 (IN718) composites. The selective orientation of the grains in the heat-treated SLMed GNPs/IN718 composites gradually disappeared, and the changes in the morphology and precipitation phases of the grains were systematically investigated. Results show that the solution treatment eliminated the dendritic and cellular crystal structure within the composite, while also producing a large number of white carbide particles. The mechanical properties of the composites decreased with increasing temperature after the solution treatment. Solution aging treatments eliminate elemental segregation, precipitate a large number of γ″-strengthened phases, and improve the materials’ tensile strength and wear resistance. Notable changes were observed compared with the untreated specimens. The hardness and tensile strength exhibited respective increases of 26.4% and 1.5%. Conversely, the elongation was reduced by 14%. Moreover, the average coefficient of friction and weight loss dropped by 6.89% and 18.78%, respectively. In the friction test, GNPs act as a lubricating phase, resulting in a significant increase in the friction wear performance of the composite. The heat treatment process releases residual stresses within the composite and improves the internal anisotropy of the material. This work is expected to provide a potential pathway to obtaining attractive mechanical properties for nickel-based superalloy components.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"69 4","pages":"1103 - 1121"},"PeriodicalIF":2.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40194-025-01958-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143581183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving out-of-distribution generalization for online weld expulsion inspection using physics-informed neural networks

IF 2.4 4区材料科学

Welding in the World Pub Date : 2025-02-09 DOI: 10.1007/s40194-025-01950-6

Yu-Jun Xia, Qiang Song, BenGang Yi, TianLe Lyu, ZhiQiang Sun, YongBing Li

{"title":"Improving out-of-distribution generalization for online weld expulsion inspection using physics-informed neural networks","authors":"Yu-Jun Xia, Qiang Song, BenGang Yi, TianLe Lyu, ZhiQiang Sun, YongBing Li","doi":"10.1007/s40194-025-01950-6","DOIUrl":"10.1007/s40194-025-01950-6","url":null,"abstract":"<div><p>Weld expulsion is one of the most common welding defects during the resistance spot welding (RSW) process. It is desired that the expulsion intensity to be inspected online via in-process sensing signals and machine learning methods so as to control and eventually eliminate weld expulsion in production. However, conventional machine learning methods struggle with out-of-distribution (OOD) data. Their performance would significantly deteriorate when there is a deviation between the distribution of test data and training data. In this study, by incorporating a specially designed autoencoder and physical constraints, a new approach using physics-informed neural networks (PINN) successfully integrates domain knowledge from welding physics to enhance the generalization performance. The results showed that the new method exhibits improved generalization capability to OOD data, allowing accurate prediction of weld expulsion intensity even under abnormal welding conditions such as electrode wear. Compared to traditional methods, the new approach achieves a 60% increase in accuracy, making it suitable for addressing the issue of lacking labeled data and uncertainty disturbances of welding conditions in mass production. This study provides new ideas for the application of PINN in monitoring and control of the welding process.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"69 5","pages":"1309 - 1322"},"PeriodicalIF":2.4,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applicable and generalizable machine learning for intelligent welding in automotive manufacturing

IF 2.4 4区材料科学

Welding in the World Pub Date : 2025-02-07 DOI: 10.1007/s40194-025-01951-5

Peng Edward Wang, Hassan Ghassemi-Armaki, Masoud Pour, Xijia Zhao, Junjie Ma, Kianoosh Sattari, Blair Carlson

{"title":"Applicable and generalizable machine learning for intelligent welding in automotive manufacturing","authors":"Peng Edward Wang, Hassan Ghassemi-Armaki, Masoud Pour, Xijia Zhao, Junjie Ma, Kianoosh Sattari, Blair Carlson","doi":"10.1007/s40194-025-01951-5","DOIUrl":"10.1007/s40194-025-01951-5","url":null,"abstract":"<div><p>This review paper examines the application and challenges of machine learning (ML) in intelligent welding processes within the automotive industry, focusing on resistance spot welding (RSW) and laser welding. RSW is predominant in body-in-white assembly, while laser welding is critical for electric vehicle battery packs due to its precision and compatibility with dissimilar materials. The paper categorizes ML applications into three key areas: sensing, in-process decision-making, and post-process optimization. It reviews supervised learning models for defect detection and weld quality prediction, unsupervised learning for feature extraction and data clustering, and emerging generalizable ML approaches like transfer learning and federated learning that enhance adaptability across different manufacturing conditions. Additionally, the paper highlights the limitations of current ML models, particularly regarding generalizability when moving from lab environments to real-world production, and discusses the importance of adaptive learning techniques to address dynamically changing conditions. Case studies like virtual sensing, defect detection in RSW, and optimization in laser welding illustrate practical applications. The paper concludes by identifying future research directions to improve ML adaptability and robustness in high-variability manufacturing environments, aiming to bridge the gap between experimental ML models and real-world implementation in automotive welding.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"69 5","pages":"1349 - 1384"},"PeriodicalIF":2.4,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40194-025-01951-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Influence of process parameters on residual stress in laser metal deposition of nickel-based superalloy

IF 2.4 4区材料科学

Welding in the World Pub Date : 2025-02-05 DOI: 10.1007/s40194-025-01940-8

Maohong Yang, Guiyi Wu, Xiangwei Li, Ruiyao Zhang, Shuyan Zhang, Honghong Wang, Illiashenko Yevhenii

{"title":"Influence of process parameters on residual stress in laser metal deposition of nickel-based superalloy","authors":"Maohong Yang, Guiyi Wu, Xiangwei Li, Ruiyao Zhang, Shuyan Zhang, Honghong Wang, Illiashenko Yevhenii","doi":"10.1007/s40194-025-01940-8","DOIUrl":"10.1007/s40194-025-01940-8","url":null,"abstract":"<div><p>This paper studied the impac t of process parameters in laser metal deposition on distribution and magnitude of residual stresses. A finite element method was used to create a residual stress model with parameters as variables. The study used a Taguchi L32 experimental design, with four levels for each parameter, and analyzed residual stresses along critical paths using quadratic functions. The results reveal that the impact of process parameters varies across different regions of the component. Melt pool size primarily affects the location of maximum residual stresses at the interface between the substrate and the deposited layer. Deposition length affects the concentration of residual stresses at the center of the interface between the substrate and the deposited layer. Substrate thickness has a substantial impact on residual stresses. Scanning strategy influences residual stresses at the edges of the upper surface of the deposited layer. Boundary conditions and yield strength affect residual stresses in various regions of the deposited layer. The coefficient of thermal expansion influences residual stresses at the interface between the base material and the deposited layer.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"69 4","pages":"1057 - 1072"},"PeriodicalIF":2.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40194-025-01940-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143581108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Control of DE-GMAW through human–robot collaboration

IF 2.4 4区材料科学

Welding in the World Pub Date : 2025-02-05 DOI: 10.1007/s40194-025-01954-2

Yue Cao, YuMing Zhang

{"title":"Control of DE-GMAW through human–robot collaboration","authors":"Yue Cao, YuMing Zhang","doi":"10.1007/s40194-025-01954-2","DOIUrl":"10.1007/s40194-025-01954-2","url":null,"abstract":"<div><p>Double-electrode gas metal arc welding (DE-GMAW) modifies conventional gas metal arc welding (GMAW) by adding a second electrode, allowing part of the current to flow directly from the wire back to the power supply. This configuration reduces the current flowing to the workpiece compared to that at the wire, and this reduction is freely controllable. This unique ability to separately control mass and heat input is particularly advantageous for applications requiring flexible heat management, such as additive manufacturing. In this innovative process, the positioning of the bypass electrode relative to the wire tip is critical for maintaining a stable arc and optimal metal transfer; however, designing an effective positioning rule can be tedious and challenging. A general solution is human–robot collaboration (HRC), which enables humans to directly operate robots and serves as real-time optimizers that can quickly develop effective rules through a few trials. Additionally, HRC allows for learning from human operation data to fully automate these rules. In this work, we designed a dual-robot HRC system that enables operators to make stable, real-time adjustments to electrode positions with ease. The HRC system incorporates a virtual reality (VR) environment, providing immersive, real-time process visualization to assist operators in accurately and safely perceiving the welding state. Efficient teleoperation of DE-GMAW is achieved by integrating high-quality camera visuals and precise robotic execution into a VR environment, eliminating hazards associated with on-site manual welding, such as welding fumes, arc radiation, and electric shock, while enhancing observation and operational accuracy. Experiments were conducted to evaluate the system’s capability to support fast and precise human adjustments, demonstrating the effectiveness of the proposed system in implementing DE-GMAW. Furthermore, full automation provides a pathway for transitioning DE-GMAW into manufacturing applications.</p></div>","PeriodicalId":809,"journal":{"name":"Welding in the World","volume":"69 5","pages":"1459 - 1468"},"PeriodicalIF":2.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0