Improving densification in Al2024 injection moulding parts by trace additions and its ThermoCalc simulations
Year: 2025
Type: article
Abstract: The main objective of this work is the design of a sustainable aluminium-based feedstock in the form of pellets optimised to obtain aluminium alloy parts with the highest possible densification, for use in an industrial application by manufacturing processes such as Powder Injection Moulding (PIM) and the 3D printing technique Composite Extrusion Modelling (CEM). For the production of aluminium alloy parts from a sustainable feedstock, the PIM process was used, through which the feedstock design was carried out and the optimal debinding and sintering conditions were established. This sustainable feedstock was manufactured by combining 2024 alloy powders with a water-soluble polymer, polyethylene glycol (PEG), and a low-CO 2 emitting polymer, cellulose acetate butyrate (CAB). Using ThermoCalc software, the influence of the quantity of the trace element additions in the liquid phase of the 2024 aluminium alloy was analysed. Thanks to the results of these simulations, the optimal trace element percentage was selected to achieve the highest possible densification of the sintered parts. A microstructural characterisation of the tin-added parts was also done to check the densification of the parts, as well as a mechanical characterisation obtaining superior properties to injected parts from aluminium powders with smaller particle size. • Designed a sustainable aluminium-based feedstock by Powder Injection Moulding. • Used ecological PEG and CAB binder system, reducing carbon footprint in dense parts. • Enhanced 2024 alloy sintering by adding tin, promoting diffusion and densification. • ThermoCalc simulations to optimize tin trace quantity for highest densification. • Mechanical and microstructural characterisation of optimised aluminium final parts.
Source: Journal of Alloys and Compounds
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,660
Awards: G5741233064, G590391771
OpenAlex ID: W4409196455
