Metal Additive Manufacturing (AM) promises an era of highly flexible part production, replete with unprecedented levels of design freedom and inherently short supply chains. But as AM transitions from a technology primarily used for prototyping to a viable manufacturing method, many challenges must first be met before these dreams can become reality. In order for machine users to continue pushing the design envelope, process space must be expanded beyond the limits currently recommended by the machine manufacturers. Furthermore, as usable process space expands and demands for reduced operator burden and mission-critical parts increase, in-situ monitoring of the processes will become a greater necessity. Processing space includes both the parameters (e.g. laser beam power and travel velocity) and the feedstock used to build a part. The correlation between process parameters and process outcomes such as melt pool geometry, melt pool variability, and defects should be understood by machine users to allow for increased design freedom and ensure part quality. In this work, an investigation of the AlSi10Mg alloy in a Laser Powder Bed Fusion (L-PBF) process is used as a case study to address this challenge. Increasing the range (processing space) of available feedstocks beyond those vetted by the machine manufacturers has the potential to reduce costs and reassure industries sensitive to volatile global supply chains. In this work, four non-standard metal powders are successfully used to build parts in an L-PBF process. The build quality is compared to that of a standard powder (supplied by the machine manufacturer), and correlations are found between the mean powder particle diameters and as-built part quality. As user-custom parameters and feedstocks proliferate, an increased degree of process outcome variability can be expected, further increasing the need for non-destructive quality assurance and the implementation of closed-loop control schema. This work presents two Machine Learning-based Computer Vision algorithms capable of autonomously detecting and classifying anomalies during the powder spreading stage of L-PBF processes. While initially developed to serve as the monitoring component in a feedback control system, the final algorithm is also a powerful data analytics tool – enabling the study of build failures and the effects of fusion processing parameters on powder spreading. Importantly, many troubling defects (such as porosity) in AM parts are too small to be detected by monitoring the entire powder bed; for this reason, an autonomous method for detecting changes in melt pool morphology via a high speed camera is presented. Finally, Machine Learning techniques are applied to the in-situ melt pool morphology data to enable the study of melt pool behavior during fusion of non-bulk part geometries.