Designing Printable Alloys for Additive Manufacturing

本博士研究的重点是了解关键的热物理和热力学性质对熔合增材制造合金中气孔和裂纹等缺陷形成的影响。所获得的知识将用于指导新合金的设计，这些合金可打印并适合增材制造。该设计将由神经网络和高斯过程等机器学习算法辅助。

This PhD study focuses on understanding the effects of key thermo-physical and thermodynamics properties on the formation of defects such as porosity and cracks in alloys fabricated by fusion-based additive manufacturing. The obtained knowledge will be used to guide the design of new alloys that are printable and suitable for additive manufacturing. The design will be assisted by machine learning algorithms such as neural networks and Gaussian processes.