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2021, ELSEVIER
Advancements in Intelligent Gas Metal Arc Welding Systems: Fundamentals and Applications presents the latest on gas metal arc welding which plays a significant role in modern manufacturing industries and accounts for about 70% of welding processes. The importance of advancements in GMAW cannot be underestimated as they can lead to more efficient production strategies, resource savings and quality improvements. This book provides an overview of various aspects associated with GMAW, starting from the theoretical basis and ending with characteristics of industrial applications and control methods. Additional sections cover processes associated with welding and welding control, such as fuzzy logic, artificial neural networks, and others.
1990 •
This paper discusses work on a model-based intelligent process controller for gas metal arc welding. Four sensors input to a neural network, which communicates to a reference model-based adaptive controller that controls process parameters. Reference model derivation and validation are discussed. The state of an arch weld is determined by the composition of the weld and base metal and the weld's thermomechanical history. The composition of the deposited weld metal depends primarily on the amount of filler metal dilution; heat input to the weld, comprising pre-heat and process heat, is the controlling factor in the thermal cycle. Thus, control of the arc welding process should focus on rational specification and in-process control of the heat and mass input to the weld. A control model has been developed in which the governing equations are solved for the process parameters as functions of the desired heat input (in terms of heat input unit weld length) and mass input (in terms ...
The International Journal of Advanced Manufacturing Technology
Weld bead geometry real-time control in gas metal arc welding processes using intelligent systems2022 •
The International Journal of Advanced Manufacturing Technology
Artificial Neural Network Controlled GMAW System: Penetration and Quality Assurance in a Multi-Pass Butt Weld Application2019 •
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
A novel approach for monitoring and improving the quality of welded joint in gas metal arc welding process using adaptive neuro-fuzzy systems2016 •
In gas metal arc welding, like other welding techniques, the quality of welded joint may be described in terms of weld bead geometry and the presence of welding defects. In turn, the characteristics of welding signals, such as voltage, current and sound, may be used to predict and improve the quality of welded joint. In this work, two sets of adaptive neuro-fuzzy inference system have been used to predict and improve the weld quality characteristics. The required data for modeling were obtained from 57 experiments based on D-optimal design of experiments. The first set is developed to predict the possible welding defects (discontinuity, lack of fusion and overlap) and shape factor of the weld bead. These “predicting adaptive neuro-fuzzy inference system models” have been developed using 13 statistical parameters of the sound, voltage and current signals. The objective of the second set of models, called “improving adaptive neuro-fuzzy inference system models,” is to adjust the input...
Journal of Materials Processing Technology
Development of digital gas metal arc welding system2007 •
This paper presents a relatively new method for achieving better welding results of gas metal arc welding (GMAW) system by using a decentralized control method. The GMAW system is an arc welding process which incorporates the power source of GMAW (PS-GMAW) with the wire feed unit (WFU). In this paper, the GMAW system is considered as two separated subsystems, those are PS-GMAW and the WFU. The mathematical modeling of PS-GMAW and WFU are presented. Based on two subsystems, a sliding mode controller is designed and applied to control the WFU meanwhile a proportional controller is designed and applied to control the PS-GMAW. Furthermore, two decentralized controllers have to be designed to control the welding arc of GMAW, be stable and control the output welding current and welding voltage for tracking the constant setting values accurately during the welding process, respectively. The simulation and experimental results are presented to show the effectiveness of the proposed controllers.
IEEE Transactions on Industry Applications
Application of fuzzy logic to spatial thermal control in fusion welding2000 •
Editura Științifică, București, 1974
Strabon, Geografia, vol. II [tr. Felicia Vanț-Ștef] [1974]Studiu introductiv, traducere, notițe introductive, note și indice de Felicia Vanț-Ștef
Environmental Research Communication (IOP Publishing Ltd))
Deep Learning-Based Water Quality Index Classification Using Stacked Ensemble Variational Mode Decomposition2024 •
Water is crucial to human survival in general, and determining the WQI (water quality index) is one of the primary aspects. The existing water quality classification models are facing various challenges and gaps that are impeding their effectiveness. These challenges include limited data availability, the intricate nature of water systems, spatial and temporal variability, non-linear relationships, sensor noise, and error, interpretability, and explainability. It is imperative to address these challenges to improve the accuracy and efficacy of the models and to ensure that they continue to serve as reliable tools for monitoring and safeguarding water quality. To solve the issues, this paper proposes a Stacked Ensemble efficient long short-term memory (StackEL) model for an efficient water quality index classification. At first, the raw input data is pre-processed to rescale the input data using data normalization and one-hot encoding. After that, the process known as variational mode decomposition (VMD) is applied to get at the intrinsic mode functions (IMFs). Consequently, feature selection is performed using an extended coati optimization (EX-CoA) algorithm to select the most significant attributes from the feature selection. Here, publicly available datasets, namely the water quality dataset from Kaggle, are used for classification and performed using are used to perform the Stacked Ensemble efficient long short-term memory (StackEL) classification process effectively. To further perfect the proposed prediction model, the Dwarf Mongoose optimization (DMO) method is implemented. Several measures of effectiveness are examined. When compared to other existing models, the suggested model can achieve a high accuracy of 98.85% of the water quality dataset.
A major mining region, Roman Hispania was the main exporter of lead to the Mediterranean between the 2nd century BC and the 1st century AD. The many ingots found, in particular, in underwater wrecks, bear a rich epigraphy that covers both the production and marketing of the metal. For the first time, this documentation has been brought together in one book, with extensive commentary, combining literary, epigraphic, archaeological and archaeometric sources.
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Las Órdenes Militares de origen hispánico. La guerra juramentada contra el infiel
Las Órdenes Militares de origen hispánico2021 •
International journal of metalcasting
Study of the Wear Resistance Plasma Nitrided GGG60 by Optimization of Surface Treatment Conditions Using Response Surface Methodology2024 •
European journal of obstetrics, gynecology, and reproductive biology
Number of colony forming units in urine at 35-37 weeks' gestation as predictor of the vaginal load of Group B Streptococci at birth2018 •
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Pesquisa Brasileira em Ciência da Informação e Biblioteconomia
A dinâmica da produção científica a partir da perspectiva das práticas informacionais2019 •