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IoT devices and sensor networks present new opportunities for measuring,monitoring, and guiding scientific experiments. Sensors, cameras, andinstruments can be combined to provide previously unachievable insights intothe state of ongoing experiments. However, IoT devices can vary greatly in thetype, volume, and velocity of data they generate, making it challenging tofully realize this potential. Indeed, synergizing diverse IoT data streams innear-real time can require the use of machine learning (ML). In addition, newtools and technologies are required to facilitate the collection, aggregation,and manipulation of sensor data in order to simplify the application of MLmodels and in turn, fully realize the utility of IoT devices in laboratories.Here we will demonstrate how the use of the Argonne-developed ManufacturingData and Machine Learning (MDML) platform can analyze and use IoT devices in amanufacturing experiment. MDML is designed to standardize the research andoperational environment for advanced data analytics and AI-enabled automatedprocess optimization by providing the infrastructure to integrate AI incyber-physical systems for in situ analysis. We will show that MDML is capableof processing diverse IoT data streams, using multiple computing resources, andintegrating ML models to guide an experiment.

The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT