Metastable austenitic stainless steels are used in many applications, from shavers and kitchen sinks to various applications in the food industry. The diversity in applications of this type of steels is possible due to the many positive properties of the steel. It is not only esthetically pleasing, it also has a good corrosive and wear resistance, it is easy to clean and it does not support biofilm growth as well as other steels. Besides the benefits of using austenitic stainless steels in products, also some benefits can be found during the production of the products: these types of steel are easily deformable, but also have a high strength. These contradicting properties can both be found in the steel because of a phase change occurring during deformation. The austenitic phase, which is soft and easily deformable, can transform into the martensite phase, which is harder and less deformable compared to the austenite. Accompanying the transformation is a transformation strain, witch improves the deformability of the steel even further. A downside of the steel is the complex material behavior and the complicated modeling of this behavior. Models of production processes are often used to determine the optimal process conditions to obtain the desired dimensions, mechanical properties and the lowest cost price of a product. The accuracy of these models depends greatly on the accuracy of the material model describing the deformation process of the steel. The development of an accurate model describing the deformation of a metastable austenitic stainless steel is not easily done. While several models exist which can describe various, relatively straight forward proportional experiments performed on austenitic steels, none can describe the correct behavior of the steel at more complex strain paths, which commonly occur during the production of a product. Two examples of areas in which the current models need to be improved are the relation between the transformation behavior of the steel, preferred orientations of the austenite grains –texture– and the strain direction as well as the influence of a changing strain path –non-proportional strain– on the transformation. These effects cannot be observed during standard experiments used to determine the parameters for the currently existing material models, but do occur during the deformation process of a product. In this research these effects on transformation of austenitic stainless steels were investigated. The results from this research can be used to develop new, more accurate material models. The material behavior during the deformation in various directions of two metastable austenitic stainless steels, one with and one without a crystallographic texture, were investigated. Both steels show transformation during deformation, but while transformation in the textured material dependeds on the deformation direction, in the untextured steel it does not. Investigating the austenitic texture after deformation and transformation shows that the orientation of an austenite grain with respect to the stress has a strong influence on the transformation properties of the grain. Several models are presented which can predict this behavior. The influence of a non-monotonic strain path on the transformation is studied by applying various subsequent strain paths on a steel specimen. In this research, most attention has been paid on a strain path containing a strain reversal. It is shown that, besides the classical Bauschinger effect –the decrease in flow stress after a load reversal–, also the transformation behavior, and thus the material behavior, changes significantly after the strain reversal. The similar effect has been observed during non-proportional strain paths. This research shows that the current material models describing the material behavior of metastable austenitic stainless steels during deformation, can be improved. Based on the knowledge obtained during this research, it is possible to develop new models capable of describing the material behavior during 3- dimensional deformation processes more accurately.
|Award date||30 Aug 2013|
|Place of Publication||Enschede|
|Publication status||Published - 30 Aug 2013|