Strategies for the Characterization and Virtual Testing of SLM 316L Stainless Steel

Abstract

The selective laser melting (SLM) process allows for the control of unique part form and function characteristics not achievable with conventional manufacturing methods and has thus gained interest in several industries such as the aerospace and biomedical fields. The fabrication processing parameters selected to manufacture a given part influence the created material microstructure and the final mechanical performance of the part. Understanding the process-structure and structure-performance relationships is very important for the design and quality assurance of SLM parts. Image based analysis methods are commonly used to characterize material microstructures, but are very time consuming, traditionally requiring manual segmentation of imaged features. Two Python-based image analysis tools are developed here to automate the instance segmentation of manufacturing defects and subgranular cell features commonly found in SLM 316L stainless steel (SS) for quantitative analysis. A custom trained mask region-based convolution neural network (Mask R-CNN) model is used to segment cell features from scanning electron microscopy (SEM) images with an instance segmentation accuracy nearly identical to that of a human researcher, but about four orders of magnitude faster. The defect segmentation tool uses techniques from the OpenCV Python library to identify and segment defect instances from optical images. A melt pool structure generation tool is also developed to create custom melt-pool geometries based on a few user inputs with the ability to create functionally graded structures for use in a virtual testing framework. This tool allows for the study of complex melt-pool geometries and graded structures commonly seen in SLM parts and is applied to three finite element analyses to investigate the effects of different melt-pool geometries on part stress concentrations.