A technical review of the physical, mechanical, and metallurgical variables that have influenced performance of Al-Li based alloys over the last 50 years is presented. First, the historic evolution of different alloys is discussed. Then, the microstructural features responsible for different mechanical properties are identified and discussed. The role of alloying additions is discussed. The shortcomings of a 2nd generation Al-Li alloys are introduced and the key alloy design principles used to overcome these are discussed. Finally, the performance parameters that play a major role in sizing several aircraft and space craft components are reviewed in a chronological perspective and compared with 3rd-generation Al–Li alloys. It is concluded that significant improvements have been made to position Al–Li alloys to enable improved performance of next generation of air and space craft.

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The Evolution of Al-Li Base Products for Aerospace and Space Applications

Magnesium (Mg) has garnered significant interest for its potential use as a biodegradable implant material. Of specific interest in this study is the effect of zinc (Zn) and strontium (Sr) additions on both the mechanical and degradation behaviors in Mg due to their established beneficial effect on strength and microstructural grain refinement while being biocompatible. Three binary Mg-x wt% Sr (x=0.5, 1.0, 1.5) alloys and three ternary Mg-x wt%Zn-0.5 wt% Sr (x=2.0, 4.0, 6.0) were studied to evaluate their mechanical and degradation behavior. Mechanical testing was performed at room temperature on solution-treated and peak aged alloys using microhardness and tensile tests. Degradation was studied using immersion tests in Hanks' solution. Results indicate a decrease in grain size and an increase in strength with increasing Sr and Zn content. When considering degradation behavior Mg-0.5 wt%Sr demonstrated the lowest degradation rate among binary alloys. At constant Sr content at 0.5 wt%, the addition of Zn increased the corrosion rate, with the highest rate for the Mg-6.0 wt%Zn-0.5 wt%Sr. The alloys which best optimized both mechanical and degradation behaviors were Mg-2.0 wt%Zn-0.5 wt%Sr and Mg-4.0 wt%Zn-0.5 wt%Sr. Finally, microstructure and property relationships were evaluated and discussed in reference to each alloy's potential use as a biodegradable implant material.

Investigation of the mechanical and degradation properties of Mg-Sr and Mg-Zn-Sr alloys for use as potential biodegradable implant materials.

The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature. Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics. A machine learning technique can identify the complex variables behind the propagation direction of small cracks in a titanium alloy. A team led by Michael Sangid at Purdue University in the U.S.A built two separate Bayesian networks using machine learning to analyse diffraction and tomography data acquired during in situ fatigue cycling of a titanium alloy. The orientation of the first principal stress axis in a specific direction and the maximum resolved shear stress were the most strongly correlated with crack propagation, and were incorporated into an analytical relationship to describe the probability of the crack propagation direction. This analytical expression reproduced experimental results and was more reliable than previous literature predictions. This sort of semi-supervised machine learning methodology may help us identify driving forces in other complex engineering problems.

/pdf/using-machine-learning-and-a-data-driven-approach-to-wgs4jkjfpr.pdf

Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials

Fatigue behavior of the aeronautical Al–Li (2198) aluminum alloy under constant amplitude loading

https://eprints.soton.ac.uk/43986/1/N.Kamp-Int_Jnl_Fatigue-paper1-together.pdf

D Li

Papers

Effect of artificial aging on the fatigue crack propagation resistance of 2000 series aluminum alloys