Fused filament fabrication (FFF) is one of the most prolific additive manufacturing techniques. Creating FFF is often credited to Scott Crump in the late 1980s, later commercialized by Stratasys in 1991. As in most additive manufacturing techniques, the FFF process involves layering thermoplastic material to create 3 dimensional objects. In the last decade, critical patent expiration allowed for the proliferation of low-cost adaptations of the FFF process, poising FFF to become one of the most widespread additive manufacturing techniques. Currently, FFF is extensively used for both industrial and consumer markets, highlighting its versatility and high impact on prototyping and production. The FFF technology is predominantly used for prototyping, although its adoption in small-scale and custom production is steadily increasing. FFF is additionally experiencing a surge in applications of bound metal sintering, expanding the material portfolio of the FFF process beyond low performance polymers into metals.
However, critical process reliability and variability exist. These errors exist due to the difficulty of implementing closed loop feedback control of material flow as well as the lack of availability of accurate process models of the nonlinear behavior of extrusion. We remedy these issues by building a meteorology grade FFF 3D printing platform with a high degree of sensorization and high spatial and temporal signal acquisition resolution. This platform allows us to collect high resolution datasets to build and verify data-driven and analytical models of the extrusion process. These models are then used as a building block of feedback controllers that improve the reliability and performance of the system, thereby producing higher quality 3D prints with lower build errors.
Temperature plays a critical role in the success of 3D printing, influencing the flow of material through the extruder and the adhesion between layers. To ensure optimal print quality, it is essential to accurately capture and understand the thermal behavior throughout the process.
Our approach integrates real-time data from thermal cameras with simulations, allowing for precise calibration of key thermal properties, such as heat capacity, convection, and conduction coefficients, along with temperature evolution and cooling rates. This bead-level thermal analysis provides deep insights into how heat impacts the printing process, helping us fine-tune parameters in real time to achieve consistent, high-quality results.
Weighted average carbon footprint of steel is 1.85 tons CO2 to 1 ton steel produced. Our innovative approach redefines this process, enabling direct fabrication from iron ore to finished parts using advanced paste deposition technology. Starting with just a CAD model and an iron ore-based paste, we deposit material layer-by-layer to build 3D geometries. The resulting "green part" is then sintered, transforming it into a fully functional metal component. With only three streamlined steps, we eliminate the need for costly machining, reduce material waste, and dramatically accelerate production. Our process not only simplifies manufacturing but also offers a more sustainable and efficient solution.