Thermal and Residual Stress Distributions in Inconel 625 Butt-Welded Plates: Simulation and Experimental Validation

Thermal and residual stress distributions induced by the gas tungsten arc welding (GTAW) process on Inconel 625 were studied using numerical simulation and experiments. A multi-pass welding model was developed that uses a volumetric heat source. Thermomechanical analysis is carried out to assess the Thermal and residual stress distributions. Experiments were carried out with 5 mm thick Inconel 625 plates. X-ray diffraction techniques were used to measure residual stresses, and IR thermometry was employed to capture the temperature values on the welded joints. Simulations were performed with ANSYS numerical code, and a close agreement was found between the predicted and experimentally measured residual stress. Thermal measurements were collected pass by pass from the analysis, and the agreement was 9.08%. The agreement between the measured and analysed residual stress was 11%

Simulação numérica de deformações e tensões em soldadura

Welding is one of the best known methods in the industry for joining a wide variety of materials. This process inevitably creates stresses and strains in the components due to the high energy intensity released by the heat source. Nowadays it is almost mandatory to quantify these changes in the parts that go through the welding process. This is the only way to comply with strict quality parameters ensuring that the part fulfils the assigned function. It is very common to use experimental methods to do this analysis. However, the use of computational methods in welding process simulation was being increasing significantly. Numerical simulation, based on the Finite Element Method, appears to make it easier for engineers to predict and analyse complex phenomena. In this work two numerical simulation models of the welding process by laser were developed on Dual-Phase 600 steel plates. Two types of joints were tested: butt and in T. The deformations and stresses caused were quantified using the Simufact software.A soldadura é dos métodos mais conhecidos na indústria para unir uma grande variedade de materiais. Este processo cria inevitavelmente tensões e deformações nos componentes devido à alta intensidade de energia libertada pela fonte de calor. Nos dias que correm torna-se quase obrigatório quantificar estas alterações nas peças que passam pelo processo de soldadura. Só assim é possível cumprir rigorosos parâmetros de qualidade, garantindo que a peça cumpre a função atribuída. É muito comum recorrer a métodos experimentais para fazer esta análise. No entanto, o uso de métodos computacionais em simulação de processos de soldadura tem crescido significativamente. A simulação numérica, baseada no Método de Elementos Finitos, surge para facilitar aos engenheiros a prevenção e análise de fenómenos complexos. No presente trabalho foram desenvolvidos dois modelos de simulação numérica do processo de soldadura através de laser em chapas de Dual-Phase 600. Foram testados 2 tipos de juntas: topo a topo e em T. As deformações e tensões causadas pelo processo foram quantificadas com recurso ao software Simufact.Mestrado em Engenharia Mecânic

Welding Processes

Despite the wide availability of literature on welding processes, a need exists to regularly update the engineering community on advancements in joining techniques of similar and dissimilar materials, in their numerical modeling, as well as in their sensing and control. In response to InTech's request to provide undergraduate and graduate students, welding engineers, and researchers with updates on recent achievements in welding, a group of 34 authors and co-authors from 14 countries representing five continents have joined to co-author this book on welding processes, free of charge to the reader. This book is divided into four sections: Laser Welding; Numerical Modeling of Welding Processes; Sensing of Welding Processes; and General Topics in Welding

Parametric Studies Based Mechanical and Thermal Modelling of Spot Welded Joints

This work has focused on formulating a experimental/numerical framework for the investigation of spot weld properties and performance. An Inverse temperature measurement approach has been established to predict the thermal history of a spot welded joints using remote thermocouples. This method incorporated the experimental data into an Artificial Neural Network (AAN) to predict cooling curves of the HAZ. Advanced modelling programs have been developed to simulate spot welded joints and thermocouples. Using the programs to investigate the effects of the key dimensional or material parameters on the mechanical or thermal response of spot welded joints of steels and different thermocouple joints relevant to their applications. Graphical User Interface Abaqus plug-ins of spot welded joints have developed using Python scripting and are used to investigate the effect of nugget size and sheet thickness on the stress and deformation of spot welded joints of steel. These works are important to establish an integrated approach to study the electrical, mechanical and thermal process of the spot welding process

Impulse-Based Manufacturing Technologies

In impulse-based manufacturing technologies, the energy required to form, join or cut components acts on the workpiece in a very short time and suddenly accelerates workpiece areas to very high velocities. The correspondingly high strain rates, together with inertia effects, affect the behavior of many materials, resulting in technological benefits such as improved formability, reduced localizing and springback, extended possibilities to produce high-quality multi material joints and burr-free cutting. This Special Issue of JMMP presents the current research findings, which focus on exploiting the full potential of these processes by providing a deeper understanding of the technology and the material behavior and detailed knowledge about the sophisticated process and equipment design. The range of processes that are considered covers electromagnetic forming, electrohydraulic forming, adiabatic cutting, forming by vaporizing foil actuators and other impulse-based manufacturing technologies. Papers show significant improvements in the aforementioned processes with regard to: Processes analysis; Measurement technique; Technology development; Materials and modelling; Tools and equipment; Industrial implementation

Nuclear Fusion Programme: Annual Report of the Association Karlsruhe Institute of Technology (KIT)/EURATOM ; January 2010 - December 2010 (KIT Scientific Reports ; 7592)

The Karlsruhe Institute of Technology (KIT) is working in the framework of the European Fusion Programme on key technologies in the areas of superconducting magnets, microwave heating systems (Electron-Cyclotron-Resonance-Heating, ECRH), the deuterium-tritium fuel cycle, He-cooled breeding blankets, a He-cooled divertor and structural materials, as well as refractory metals for high heat flux applications including a major participation in the preparation of the international IFMIF project

1992 NASA/ASEE Summer Faculty Fellowship Program

For the 28th consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama and MSFC during the period June 1, 1992 through August 7, 1992. Operated under the auspices of the American Society for Engineering Education, the MSFC program, was well as those at other centers, was sponsored by the Office of Educational Affairs, NASA Headquarters, Washington, DC. The basic objectives of the programs, which are the 29th year of operation nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings