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In-Situ Data Collection, Process Monitoring and Quality Improvement for Melt Extrusion Additive Manufacturing

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“Personal” melt extrusion additive manufacturing such as Rep-Rap is the most common method of 3D printing; found in libraries, schools of all grade levels and maker spaces the world over. Unfortunately, it is an unpredictable, unreliable and hugely error-prone and wasteful process. Waste includes huge amounts of wasted plastic, most of which cannot be recycled, along with significant additional direct and indirect costs, such as time to set up and wait for failed prints and energy for heating the plastic in failed prints. We sought to address this problem by instrumenting popular personal printers such as Prusa® and Ender® with numerous real-time sensors and imaging devices to gather real-time in-situ process data from actual 3D printing operations and developing methods of adaptive control that use the in-situ process data to adjust 3D printing process parameters “on-the-fly”. Complex, sensor-derived data such as stepper motor current, filament diameter and much more are collected and sent to a Low Investment Manufacturing System (LIMS®) Edge device (LECS Energy) that manages the data in the cloud and enables disturbed processing, AI and machine learning. To this end, we present a system and methods to capture in-situ printing process parameters, correlate these parameters with actual live printing processes and execute real-time process corrections that improve ultimate print quality and consistency and reduce waste. 

Learning Objectives:

  • Implement real-time, in-situ data collection methodologies to help improve quality and reduce waste in personal melt extrusion 3D printing.
  • Automate in-situ data collection to improve operational efficiency and lower cost for multiple personal melt extrusion 3D printers, such as in a model shop, lab, school or service bureau.
  • Develop custom algorithms to improve quality and consistency and to reduce cost and waste from sensor-derived, real-time, in-situ data and share algorithms with others in a larger community.