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. / Master of Science / Recent advancements in additive manufacturing (AM) processes like the selective laser melting (SLM) process are revolutionizing the way many products are manufactured. The geometric form and material microstructure of SLM parts can be controlled by manufacturing settings, referred to as fabrication processing parameters, in ways not previously possible via conventional manufacturing techniques such as machining and casting. The improved geometric control of SLM parts has enabled more complex part geometries as well as significant manufacturing cost savings for some parts. With improved control over the material microstructure, the mechanical performance of SLM parts can be finely tailored and optimized for a particular application. Complex functionally graded materials (FGM) can also easily be created with the SLM process by varying the fabrication processing parameters spatially within the manufactured part to improve mechanical performance for a desired application. The added control offered by the SLM process has created a need for understanding how changes in the fabrication processing parameters affect the material structure, and in turn, how the produced structure affects the mechanical properties of the part. This study presents three different tools developed for the automated characterization of SLM 316L stainless steel (SS) material structures and the generation of realistic material structures for numerical simulation of mechanical performance. A defect content tool is presented to automatically identify and create binary segmentations of defects in SLM parts, consisting of small air pockets within the volume of the parts, from digital optical images. A machine learning based instance segmentation tool is also trained on a custom data set and used to measure the size of nanoscale cell features unique to 316L (SS) and some other metal alloys processed with SLM from scanning electron microscopy (SEM) images. Both these tools automate the laborious process of segmenting individual objects of interest from hundreds or thousands of images and are shown to have an accuracy very close to that of manually produced results from a human. The results are also used to analyze three different samples produced with different fabrication processing parameters which showed similar process-structure relationships with other studies. The SLM structure generation tool is developed to create melt pool structures similar to those seen in SLM parts from the successive melting and solidification of material from the laser scanning path. This structural feature is unique to AM processes such as SLM, and the example test cases investigated in this study shows that changes in the melt pool structure geometry have a measurable effect, slightly above 10% difference, on the stress and strain response of the material when a tensile load is applied. The melt pool structure generation tool can create complex geometries capable of varying spatially to create FGMs from a few user inputs, and when applied to existing simulation methods for SLM parts, offers improved estimates for the mechanical response of SLM parts.