Data-driven modeling for microstructure-property relationships of additive manufactured stainless steel parts

The high upfront cost of determining the resultant mechanical properties of metal parts built by additive manufacturing significantly hinders widespread adoption in the industry. The objective of this study is to establish data-driven models that can correlate the microstructural details to mechanical properties. Once established, such data-driven models can provide a quicker way of predicting mechanical properties based on given microstructure information and can reduce the cost of achieving desired mechanical properties by drastically reducing the number of experiments required. The major advantage of such an approach is that a data-driven model can be built based on the widely available but scattered data in the literature along with an additional small number of necessary experimental data and it can be used to predict mechanical properties for various manufacturing processes used and heat treatment conditions. The presentation shows successful case studies for building data-driven models of microstructure-property relationships for selected stainless metallic parts made by additive manufacturing processes such as Stainless 17-4 and 316L with good prediction capabilities for yield strength, ductility and ultimate tensile strength. The detailed image analysis methods and establishing the data-driven models using machine learning are also described.