Investigation of novel thermal field simulation methods for selective laser melting
Creators: name: McMillan, Matthew
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 401408
subject: Manufacturing processes and technologies (excl. textiles)

description: Selective laser melting (SLM) is a form of Powder Bed Fusion (PBF) based additive manufacturing, which enables the production of complex geometries such as lattice structures that exceed the manufacturing capability of traditional processes. The SLM process is highly complex, including transient, thermo-mechanical loading, spatially and temporally transient boundary conditions, and multi-scale effects. Furthermore, SLM is subject to significant experimental and statistical uncertainties. Consequently, the SLM process is still poorly understood, and build-process simulations are computationally demanding. This limits the capability of Design for Additive manufacturing (DFAM) tools and generally necessitates part-by-part experimental validation for commercial SLM applications.<br> <br>This research investigates methods to address these limitations for the SLM process by developing novel reduced order simulation methods. A literature review identified that existing simulations are primarily methods of investigating the effects of process parameters and the interaction of complex physics present. These methods are not computationally efficient and therefore cannot be applied to full scale geometry. This means there is a lack of simulation methods that can be used as DFAM tools. To address this limitation the slice data simulation (SDS) and the Lattice Additive Manufacturing Beam element simulation (LAMB) tools are developed.<br><br>Both SDS and LAMB are custom developed to predict the transient temperature field of the geometry outside of the highly transient zone surrounding the melt pool, the bulk thermal zone, in a computationally efficient manner. The SDS method reduces data generation by reusing SLM layer information to generate simulation results and the LAMB method simplifies lattice structures into beam elements with unique properties developed based on the 3D geometry. Both SDS and LAMB achieve qualitative design insights that align with SLM manufactured case studies. Due to the simplifications, to enable quantitative results, in-situ validation data from process monitoring is required.<br><br>In-situ process monitoring enables examination of the transient temperature field for simulation validation. However, existing literature primarily implements narrow-field observations that provide little insight into the bulk thermal zone, that is predicted with the SDS and LAMB methods. Therefore, novel process monitoring methods are developed to address this gap in literature. The first method uses a wide field of view thermal camera to determine the cooling duration after exposure. The cooling duration is a measurement of temperature change in the bulk thermal zone at the surface of the current build layer which is achieved through custom processing and a calibrated thermal camera. The second method develops a novel process to perform in-situ, sub-surface, process monitoring using thermocouples. The thermocouple tip is placed into geometry via a notch a set distance below the current build layer which is achieve by modifying a SLM machine to allow thermocouple ingress. The information gathered using this method provides direct measurement of temperature for the bulk thermal zone. Both methods provide novel data that enables fundamental insights into the SLM process and act as a reference data set to enable the validation of reduced order simulations including the SDS and LAMB methods.<br> <br>Due to the simplifications present in the SDS and LAMB methods they can be rapidly applied during the design phase, as a DFAM tool. Furthermore, the implemented process monitoring methods demonstrate new techniques to enable the validation of DFAM simulations including SDS and LAMB. This research provides a roadmap to enable future development of novel DFAM simulations and their associated process monitoring methods.