Project Abstract

Metals are widely used in many applications due to their durability and strength, but they suffer wear and tear over time. Nowadays, metallic deposition by wire arc additive manufacturing is being used for the fabrication/modification/repair of metallic components. The wire arc deposition process is widely used in the industry and accounts for more than 50% of all the deposits.

Fundamentally, it is an arc process that uses a continuous solid wire electrode heated and fed into the molten pool from a gun. The two base materials are melted together, forming a deposit or joint. The gun feeds a shielding gas alongside the electrode, helping protect the molten pool from airborne contaminants. However, there are still several issues with the quality of the metal deposits, such as (1) the inconsistency of deposited metal’s mechanical and chemical properties, (2) hot cracking and dilution of hardfacing/overlay of deposit material on a substrate, (3) uncontrolled heat affected zone and defects that lead to high residual stresses and prone to crack and (4) lack of machine learning to assist with process optimisaton to save time, cost and obtain the desired properties.

Therefore, to solve the issues that occur in wire arc deposited metals, the study will focus on (1) the development of deposition strategies, (2) understanding the changes in microstructure, chemical composition, and mechanical properties through a series of parametric studies and (3) development of machine learning algorithm integrated with metaheuristic optimisation techniques to accurately predict and optimise the quality of wire arc deposits. These issues need to be better understood and solved to create new opportunities for the industries.