Designing Printable Alloys for Additive Manufacturing

这项博士研究的重点是了解关键的热物理和热力学特性对通过基于熔融的增材制造的合金中的孔隙和裂纹等缺陷形成的影响。所获得的知识将用于指导可打印和适用于增材制造的新合金的设计。该设计将由机器学习算法（如神经网络和高斯过程）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.