Supervisors: Prof. Raj Das (RMIT) and Associate Prof. Andrey Molotnikov (RMIT) and Prof. Daniel Casellas (EUT) and Full Prof. Antonio Mateo (UPC)
Project 1: Understanding fatigue resistance of AM parts from processing parameters and material properties
Additive manufacturing (AM) is rapidly penetrating the industry to produce high performance parts. Fatigue resistance is one of the key application properties in many engineering components. It is greatly influenced by the pre-existent defects, mainly their size and distribution. A proper understanding of the influence of defects produced during manufacturing is crucial to understand the fatigue resistance and to optimize manufacturing parameters to obtain high performance parts. The PhD focusses on the experimental evaluation of the interaction between microstructure, defects, and materials properties to understand fatigue resistance in AM parts.
Project 2: Fatigue assessment of structural parts produced by additive manufacturing
Additive manufacturing (AM) is rapidly penetrating the industry to produce high performance parts. Fatigue resistance is one of the key application properties in many engineering components. Fatigue tests are typically time consuming and require a large amount of material to be tested. For AM parts this is especially relevant because the inherent anisotropy of the process requires also to test specimen in different directions. Such situation hampers the rapid development of AM parts. Rapid fatigue testing methodologies are available for bulk materials. The PhD explores the applicability of such rapid resting techniques to assess the fatigue performance of AM specimens.
Project 3: High fatigue performance AM parts by topological optimization
Additive manufacturing (AM) is rapidly penetrating the industry to produce high performance parts. Fatigue resistance is one of the key application properties in many engineering components. AM allows extreme lightweight designs by locally changing material type and part geometry. This is known as topological optimization. The PhD will focusses on the development of high fatigue performance parts designed through topological optimization.