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Robots, humanoid and otherwise, are being created with the underlying motivation in many cases that they will either replace or complement activities performed by humans. It has been many years since robots were starting to be designed to take over “dull, dirty, or dangerous” tasks (e.g., Singer 2009). Over time, roboticists and others within computing communities have extended their ambitions to create technology that seeks to emulate more complex ranges of human-like behavior, potentially including the ability to participate in complicated conversations. Regardless of how sophisticated its functionality is, a robot should arguably be encoded with ethical decision-making parameters, especially if it is going to interact with or could potentially endanger a human being. Yet of course determining the nature and specification of such parameters raises many longstanding and difficult philosophical questions. 

This study compares the fabrication of IN718 alloy using bi-directional raster and stochastic spot melting techniques with the open-source FreemeltOne Electron Beam Melting (EBM) system. The research aimed to produce dense parts using both scanning strategies, employing custom Python code for raster melt beam path generation and PixelMelt software for stochastic spot melting path generation. After optimizing process parameters, 10mm height builds for each scanning strategy were fabricated, and their microstructure, hardness, and density were analyzed using optical microscopy and SEM, Vickers microhardness scale, and a pycnometer. The findings reveal valuable insights into the effects of scanning strategies on the microstructure, hardness, and density of IN718 alloy components, advancing additive manufacturing knowledge. 

Ethical decision-making is difficult, certainly for robots let alone humans. If a robot's ethical decision-making process is going to be designed based on some approximation of how humans operate, then the assumption is that a good model of how humans make an ethical choice is readily available. Yet no single ethical framework seems sufficient to capture the diversity of human ethical decision making. Our work seeks to develop the computational underpinnings that will allow a robot to use multiple ethical frameworks that guide it towards doing the right thing. As a step towards this goal, we have collected data investigating how regular adults and ethics experts approach ethical decisions related to the use in a healthcare and game playing scenario. The decisions made by the former group is intended to represent an approximation of a folk morality approach to these dilemmas. On the other hand, experts were asked to judge what decision would result if a person was using one of several different types of ethical frameworks. The resulting data may reveal which features of the pill sorting and game playing scenarios contribute to similarities and differences between expert and non-expert responses. This type of approach to programming a robot may one day be able to rely on specific features of an interaction to determine which ethical framework to use in the robot's decision making.