João Miguel Nóbrega

Thermoplastic extrusion, a widely used method for processing thermoplastic materials, requires precise temperature control to ensure product quality. However, existing computer-aided engineering tools often oversimplify the temperature distribution calculations, leading to additional discrepancies between simulations and the actual processes. This study introduces a novel multi-region modeling approach to address this issue. By employing realistic temperature control conditions, the methodology overcomes the limitations of current numerical modeling tools. The key contributions include the development of a transient, incompressible, non-isothermal solver integrated into the OpenFOAM computational library and the implementation of a specialized boundary condition that emulates Proportional-Integral-Derivative (PID) control using real-time thermocouple measurements. The findings highlight temperature deviations at the flow channel walls and total pressure drop while demonstrating a smaller impact on velocity and flow uniformity at the outlet under steady-state conditions. This research substantially advances the understanding of thermal dynamics in extrusion processes, offering crucial insights for enhancing temperature control and laying the groundwork for more effective and precise operational strategies.

Selective laser sintering (SLS) is one of the most well-regarded additive manufacturing (AM) sub-processes, whose popularity has been increasing among numerous critical and demanding industries due to its capabilities, mainly manufacturing parts with highly complex geometries and desirable mechanical properties, with potential to replace other, more expensive, conventional processes. However, due to its various underlying multi-physics phenomena, the intrinsic complexity of the SLS process often hampers its industrial implementation. Such limitation has motivated academic interest in obtaining better insights into the process to optimize it and attain the required standards. In that regard, the usual experimental optimization methods are time-consuming and expensive and can fail to provide the optimal configurations, leading researchers to resort to computational modeling to better understand the process. The main objective of the present work is to develop a computational model capable of simulating the SLS process for polymeric applications, within an open-source framework, at a particle-length scale to assess the main process parameters’ impact. Following previous developments, virgin and used polymer granules with different viscosities are implemented to better represent the actual process feedstock. The results obtained agree with the available experimental data, leading to a powerful tool to study, in greater detail, the SLS process and its physical parameters and material properties, contributing to its optimization.