Project detail

Healable Al alloys Manufacturing by Advanced Automated Characterisation

Duration: 01.05.2023 — 30.04.2026

Funding resources

Technologická agentura ČR - M-ERA.NET 3 Call 2022

- whole funder (2023-07-24 - 2026-04-30)

On the project

The HAMAAC project will design and additive manufacture (AM) a lightweight healable aluminium matrix composite (hAMC) for transportation industry, combined with advanced imaged-based characterization technique towards automated quality control of the manufactured parts. Laser powder bed fusion (L-PBF or formerly called SLM) will be applied to produce the hAMC and functional parts out of it. Indeed, L-PBF provides a good control of the part quality (tolerance, roughness,..) and allows manufacturing complex 3D shapes 1,2. High strength aluminium alloys have many applications in transportation industry, where the strength-to-weight ratio should be maximized 3. However, high strength Al alloys processed by L-PBF present low ductility and fatigue resistance compared to wrought Al alloys, due to hot cracking occurring during solidification and excessive porosity 4,5. The high strength Al-Mg-Sc alloys (low Mg content, ~4 wt%) has been developed to overcome these limitations 6,7. These alloys present a yield strength of typically 500 MPa thanks to highly thermally stable Al3Sc precipitates 6,7. The development of healing strategies for lightweight materials has the potential to significantly improve the durability of parts 8,9. A structural macroscopic healable metal, i.e. healing cracks in the range of millimetres, would be particularly advantageous in applications where part replacement is difficult or impossible, for parts subjected to interrupted service or requiring high reliability. Designing self-healing metal based composites is challenging and requires a driving force such as high temperature conditions to trigger healing by diffusion and/or local melting process of a healing agent 8,9. A “proof-of-concept” step towards a healable L-PBF alloy was reached at UCLouvain (Wallonia, Belgium) in the last year using a binary Al-Mg (Mg content of ~8 wt%)10. It shows promising healing capacities probably by liquid-state assisted healing, with healing of almost all voids < 1µm and small cracks as evidenced by synchrotron µCT (X-ray Microtomography). But this alloy remains low strength with yield strength of ~150 MPa, much below the Al-Mg-Sc alloys. Understanding the healing mechanism still depends on a successful microstructural analysis performed at the exact location of a void or a crack. However, such defects and their healing are difficult to quantify using only 2D observations (where polishing might even hinders cracks), 3D non-destructive testing (NDT) is required before and after healing 11. Due to the multiscale distribution of the reinforcements and damage size, a multiresolution and multimodal imaging approach with a spatial resolution from micro- to nano- scale 12,13 combined with local chemical compositions and grain orientation will be required to evidence the healing efficiency. Thus, the HAMAAC project will: 1) develop a new generation of high strength healable aluminium matrix composite (hAMC) based on Al-Mg-Sc alloys, followed by manufacturing optimization of functional parts; 2) develop an automated characterization tool for quality control of the produced composites and functional parts. Liquid eutectic-phase assisted healing will be the targeted mechanism to heal large-scale cracks and damage within newly designed structures processed by L-PBF. The automated characterization tool will incorporate optimization of the robust and controlled multiscale analysis protocol based on three-dimensional (3D) correlative µCT followed by advanced data treatment and scaling. Machine learning (ML) approach will be applied for improved image data segmentation towards automatic defects detection at different scales and linked with the microstructural and mechanical properties using artificial intelligence algorithm.

Keywords
healable aluminium alloys, non-destructive testing, correlative tomography, machine learning, automated characterization tool

Mark

TH82020003

Default language

English

People responsible

Kaiser Jozef, prof. Ing., Ph.D. - principal person responsible

Units

Advanced instrumentation and methods for material characterization
- beneficiary (2023-03-01 - 2026-02-28)
Central European Institute of Technology BUT
- responsible department (2022-06-14 - 2022-06-15)