Temperature has been shown to be a critical factor impacting additive manufacturing (AM). During selective laser melting (SLM), the heat transfer and fluid flow affect grain growth and the microstructure of the printed material. Previous efforts have mostly relied on tuning parameters such as laser power and scan rate, but a more detailed understanding of temperature effects in AM is still lacking. In this Seed, we will probe and understand how dynamic and localized heating and cooling affect the microstructure of additive manufactured (AM) materials by operando temperature mapping and machine learning.