Abstract

Additive manufacturing, commonly known as 3D printing, is a time-efficient and cost-effective alternative to machining and milling in the manufacturing process. It offers promising capabilities to fabricate custom-designed parts for complex applications. Fused Deposition Modeling (FDM) is one of the most commonly used printing techniques for creating prototype pieces. This technique is relatively inexpensive, easy to use, and has been estimated to reduce the creation time of manufacturing tools by up to 85%. However, non-optimal environmental conditions can cause significant issues in the 3D printing process, including jammed nozzles and defective parts such as stringy prints, parts with bubbly or uneven surface textures, and soft or brittle parts. For example, higher relative humidity levels in the printer vicinity significantly decrease the ultimate strength and Young’s modulus of 3D printed parts. An enclosure is an effective tool and an efficient way to control and set the airflow patterns, temperature distribution, or humidity level in the vicinity of the printer for a more optimum printing process. A well-designed enclosure to encapsulate the printer can play a vital role in reducing waste and generating quality parts by controlling the printing environmental conditions. Accurate and efficient CFD modeling can be instrumental in designing and optimizing these enclosures, which is a major focus of the current research project. The present effort reports on the development of a CFD-based simulation model for providing detailed and accurate information on the conditions inside the printer enclosure. It also details the validation of the model using experimentally measured airflow, temperature, and humidity data obtained from inside the enclosure. The simulation results obtained from this new CFD model were shown to compare well with the physical model's measured data. The comparison of temperature measured at four different locations showed a difference of less than 0.5% for values obtained from computational and physical models. The time variation of predicted humidity level also compared favorably with measured data from the physical model both in trend and magnitude. Finally, the fan models used in the computational study were validated using measured data and flow rate values provided by the manufacturer’s fan curve. The next phase of this work involves utilizing this CFD model along with optimization software for the design and optimization of 3D printer enclosures with external fans and humidity controls for the ability to set optimal printing conditions.