An exploration and analysis of the microstructure, dimensional changes and scattering effects of stereolithography additive manufactured alumina
Creators: name: Oliver, Jon Lawrence
Publisher: name: No Publisher Supplied
Year: 2025

description: This dissertation examines the microstructure and quantitative analytical assessment of porosity, scattering, and lateral dimensional changes in stereolithography (SL), additive manufactured (AM), alumina (Al2O3). The accompanying research is divided into two primary areas of focus: an in-depth analysis of the microstructure of alumina, printed under multiple light intensities, layer sizes and ceramic powder particle sizes including an investigation of quantifying lateral dimensional spread and scattering and its impact on the microstructure. In the analysis of the microstructure, advanced techniques including backscattered and secondary electron imaging, provide for sample imaging of the layers of the AM pieces. Each sample is investigated within each layer as well as the adhesion between layers. This microstructure visible by analysis on a scanning electron microscope demonstrates the correlation between printing parameters and the challenges introduced by scattering and lateral dimensional changes. The result of this investigation and the computational analysis of the properties assists in determining the best parameters for optimal printing of the ceramic materials. The phenomena studied here and the factors that have been quantified provide the opportunity for consideration of potential strategies for optimizing the SLA process including resin composition, adjusting the parameters of the light source, and developing post-processing techniques. The multi-faceted approach discussed in this study is one pathway to conduct experiments and create characterization techniques to address the challenges of printing and post-processing to ensure the optimization of the printing process based on the unique needs of each resin as well as the design of each finished part.This study conducts a series of experiments comparing the microstructure, scattering and lateral spread under various experimental conditions. The microstructure of each of these sample conditions are then introduced into a computational model to ensure that predictable and reproducible results are achieved. Additionally, the potential of machine learning algorithms for predicting and compensating for dimensional changes pre-sintering is explored, highlighting the role of real-time adjustments in printing parameters for enhanced accuracy and consistency.The findings presented here contribute to the field of additive manufacturing, offering insights into optimizing SLA for ceramic resins and paving the way for its application in industries requiring high precision and specific material properties, such as strength, toughness, and corrosion resistance. Additionally, the research presented provides for the use of Artificial Intelligence (AI) in the form of Machine Learning (ML) to assist in quantifying and understanding the changes and differences in each part based on printing and curing parameters. The research also provides opportunities for future investigations into the SLA manufacturing processes and the expansion of this technology and quantitative technique to other ceramic powders.