2021:09 Mechanisms important for material modelling in weld residual stress analysis

SSM perspective

Background

The prediction of weld residual stresses (WRS) is very complex as many mechanisms and phenomena infuence the development of WRS during welding. For accurate prediction, the constitutive material models used are essential and must be able to describe the material response of the weldment constituents. For example, selection of a material model should only depend on material behaviour and not on other parameters, mechanisms or modelling techniques. Thus, before constitutive models for welding simulations can be further developed, the interaction between all essential mechanisms and phenomena infuencing WRS, and their respective impact on WRS, must be understood.
The present study aims to investigate mechanisms and phenomena related to welding and WRS. The purpose is to understand which mechanisms and phenomena that need to be considered for reliable predictions and how this knowledge should be taken into account in the material modelling.

Results

Mechanisms and phenomena are explained and their development of WRS during welding are studied. Consequently, some mechanisms and phenomena are pointed out as more important than other for how WRS develop. To improve knowledge and accuracy in WRS predictions some mechanisms and phenomena need to be further investigated.

Relevance

The work has increased the understanding for how mechanisms and phenomena infuence the development of WRS. Moreover, the increased knowledge can lead to improved material models and accordingly more reliable predictions of WRS

Need for further research

High temperature mechanisms as recovery, recrystallization and creep are modelled by use of an annealing function. Today, a binary function where full annealing occurs when the temperature exceeds a predefned temperature is used. However, experimental data suggests that annealing should occur within a specifc temperature span. An improved annealing function is further investigated in research project SSM2018-5270.