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dc.contributor.authorVishvesha, Archisman-
dc.date.accessioned2025-07-06T12:51:29Z-
dc.date.available2025-07-06T12:51:29Z-
dc.date.issued2015-05-
dc.identifier.urihttp://localhost:8081/jspui/handle/123456789/17826-
dc.description.abstractWhile predicting the welding distortion for a complex welded structure like the guide blade carrier assembly of power generation turbine, the traditional thermo-mechanical models commonly utilized for geometrically simplistic butt and fillet joints are not adequate. For large welded structure, the thermo-mechanical elasto-plastic analysis is computationally prohibitive. Furthermore, the aforementioned distortion prediction technique is unable to provide reliable results when applied to large complex 3-D welded structures. Hence, inherent strain method is proposed here to predict distortion in a welded GBC (Guide Blade Carrier) of a steam turbine. Such welded GBC are large welded structures having many welded joints of different weld geometries at several planes. In the present study, inherent strain based technique was utilized for the above mentioned welded structure in elastic finite element analysis for the accurate prediction of 3-D distortion patterns with reduced computational time. The predicted and measured values of 3-D distortions of GBC were observed to be in good agreement with the ones provided by BFIEL Haridwar, indicating the adequacy of inherent strain based method for the prediction of welding distortion in large and complex welded structures. As part of this investigation, a suitable welding restraint was designed and verified through inherent stain method to aid in minimizing the distortion of welded GBC.en_US
dc.description.sponsorshipINDIAN INSTITUTE OF TECHNOLOGY ROORKEEen_US
dc.language.isoenen_US
dc.publisherIIT ROORKEEen_US
dc.subjectWelding Distortionen_US
dc.subjectWelded Structureen_US
dc.subjectThermo-Mechanical Modelsen_US
dc.subject3-D Distortionen_US
dc.titleDISTORTION PREDICTION OF AN ARC WELDED STRUCTURE BY INHERENT STRAIN METHODen_US
dc.typeOtheren_US
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