Supervisors: A/Prof Andrew Ang (Swinburne), Professor Guoxing Lu (Swinburne), Dr Samuel Pinches (Swinburne)

Thesis Title: Application of Machine Learning in Cold Spray

Thesis Abstract: Cold Spray (CS) is a coating process in which powder particles are accelerated to supersonic speeds without melting them before they impinge on a surface. It can be used to spray thin coatings onto surfaces or to create 3D objects. Process parameters and materials affect the characteristics of manufactured parts and therefore must be chosen with care. Data-driven models based on Machine Learning (ML) techniques have been applied in the field of Additive manufacturing to support this process. Machine Learning models can learn patterns and develop general rules from vast quantities of inter-related data and outputs from an identified additive manufacturing process, which can be applied subsequently to make predictions. The outcome of this work is to apply ML algorithms in CS to predict process results.

Biography: Martin Eberle joined SEAM in 2020 as a PhD Candidate with SEAM, at the Swinburne University node. He graduated from Technical University Kaiserslautern, Germany and was awarded a Master of Engineering. In his Bachelor and Master studies he worked on projects related to material science and additive manufacturing. Martin has also worked as a Project Manager in the pharmaceutical and in the logistics industry. After completing his PhD in 2024, Martin joined the Melbourne-based cold spray company Titomic as an engineer.

LinkedIn: https://www.linkedin.com/in/martin-eberle-1b9361172/