IIW White Paper

9.1.3 Hot topics Reducing negative impact on the environment e.g. by introducing gas conditioning plants. Efficient utilisation of refurbished existing assets and life extension of existing plant including appropriate welding repair specifications, technologies and inspection. Building new plant with increased efficiency e.g. supercritical or ultra supercritical steam generating plant. Changing to cleaner burning fuels, with accompanying asset modification. Refurbishment rather than replacement of worn components. Recycling of discarded materials and consumables. Utilisation of procedures and techniques which put a lower burden on the environment e.g. uptake of solid state welding processes for fabrication of power plant components. Improved plant condition monitoring e.g. creep testing. Retentionof skills, anddevelopment of personnel to support technologies andweldingmanagement. 9.1.4 Power – Nuclear The challenges in the development of robust welding technology for nuclear reactor materials hold the key for sustainable development of economic, safe and environmentally friendly nuclear energy. The development of new as well as improved variants of nuclear reactor materials needs adaptation of the latest advances in welding technology to ensure cost-competitiveness of nuclear power. The present maturity in understanding of the science of welding has to be utilised for developing suitable welding consumables for the advanced/ improved materials as also for evolving appropriate repair welding technologies for existing nuclear power plants. The innovative welding technologies, like activated TIG welding, friction stir welding, hybrid welding, have to be harnessed to enable their industrial application for fabrication of nuclear reactor components. Tools like artificial neural networks would find increasing application in ensuring high quality welds. Computational, experimental and theoretical are the three pillars to meet the demands of desired performance and regulatory requirements. On the long-term, development of fusion technology will be crucially dependent on the development of appropriate welding technologies for materials like low/reduced activation ferritic-martensitic steels and oxide dispersion strengthened alloys. Multi-disciplinary intelligent welding approaches are being increasingly adopted for realising high performance welding technology through synergistic use of sensors, evaluation, modelling and automation. An intelligent welding system, equipped with sensors, artificial intelligence and actuators to sense and control welding operations in real time would reduce the occurrence of defects in welds and would also guide the welder to eliminate errors while providing the adaptability needed to accommodate the variability found in the welding process. Online monitoring of welding processes is feasible through synergistic use of sensors and modelling for evaluation of narrow gap welds and resistance welds in high performance components. The application of artificial intelligence methods in the field of welding is exemplified through the development of quantitative models using artificial neural networks for predicting ferrite content, solidification mode and weld-bead geometry in austenitic stainless steel welds. The models for ferrite content and solidification mode can be effectively used for designing the welding consumables having chemical compositions that minimise hot-cracking susceptibility in austenitic stainless steel welds, while the models for weld-bead geometry can be used for optimising welding process parameters for obtaining high quality welds. Research efforts are continuing worldwide for increasingly applying artificial intelligence methods in automated welding.

Nuclear power is expected to play a key role in the global energy scenario in the coming years, especially in the context of the rapid depletion of fossil fuel resources and increasing concerns about climatic changes

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Through Optimum Use and Innovation of Welding and Joining Technologies

Improving Global Quality of Life