Additive Manufacturing (AM), commonly known as 3D printing, represents a groundbreaking technology often referred to as the third industrial revolution. This project is specifically focused on Direct Energy Deposition (DED), an advanced technique that facilitates the fabrication of intricate geometries without the conventional need for tooling, die casting or mould creation. This capability significantly streamlines the manufacturing process, reducing costs and time. In DED metal additive manufacturing, a focused energy source like a laser or electron beam deposits and fuses metal feedstock material, which can be either in wire or powder form. The energy source moves along a predefined path, melting and bonding the feedstock material layer by layer. The primary objective of this research is to optimise the DED process parameters to ensure superior quality in 3D printed parts. A critical aspect of this is identifying and refining key processing parameters that profoundly affect the accuracy and reliability of printed parts.
Implementing this optimisation will directly enhance the quality and performance of DED technology, impacting its greater adoption by providing more design freedom, reducing production lead times and increasing productivity in industrial applications. Furthermore, our ultimate aim involves developing a digital twin for the additive manufacturing process to facilitate real-time monitoring and optimisation. By employing a set of sensors and monitoring systems integrated into the DED setup, we can capture a wide range of data, including temperature variations, deposition rates and melt pool characteristics. This data is fed into the digital twin, a precise virtual model of the manufacturing process, which allows for real-time process oversight and adaptation. This capability enables immediate adjustments and process optimisations in real-time during the printing process.
The collaboration with MELTIO, a renowned leader in Metal Additive Manufacturing (MAM), is pivotal as they are experts in building MAM machines and printing parts. This collaboration is crucial for integrating the intelligent optimiser developed from artificial intelligence into the machines, facilitating real-time process monitoring and adjustments, such as altering the laser power to achieve a better print. Finally, the project will conclude with a comprehensive characterisation of printed parts using these optimal parameters to evaluate how adjustments in processing parameters, for example, power variations, influence mechanical performance. These investigations will take place at the RMIT Centre of Additive Manufacturing, leveraging its advanced facilities to ensure thorough analysis.
