Electrochemical Characteristics of Intermetallic Phases in Aluminum Alloys

This paper presents a survey of corrosion potentials, pitting potentials, and electrochemical characteristics for intermetallic particles commonly present in high-strength aluminum-based alloys. Results from relevant pure metals and solid solutions are also presented. It is seen that corrosion potentials and pitting potentials vary over a wide range for various intermetallics. Elaboration of the results reveals that the electrochemical behavior of intermetallics is more detailed than the simple noble or active classification based upon corrosion potential or estimated from the intermetallic composition. Intermetallics capable of sustaining the largest cathodic current densities are not necessarily those with the most noble Ecorr, similarly those with the least noble Ecorr will not necessarily sustain the largest anodic currents. The data herein was collected via the use of a microcapillary electrochemical cell facilitating electrode investigations upon intermetallic particles in the micrometer-squared range. This survey may be used as a tool for clarification of localized corrosion phenomena in Al alloys

Investigation and Discussion of Characteristics for Intermetallic Phases Common to Aluminum Alloys as a Function of Solution pH

This paper presents results for corrosion potentials, pitting potentials, and electrochemical characteristics for intermetallic particles commonly present in high strength aluminum-based alloys, for tests conducted in a 0.1 M NaCl solution of varying pH via the use of a microcapillary electrochemical cell. The intermetallics investigated were Mg_2Si, MgZn_2, Al_7Cu_2Fe, Al_2Cu, Al_2CuMg, and Al_3Fe. Elaboration of the results reveals that the electrochemical behavior of such compounds varies markedly with pH, with attendant ramifications for localized corrosion and protection in Al alloys. Examples of this are shown for AA7075-T651, where it is shown that the localized corrosion morphology is drastically different upon the bulk alloy depending on the pH of the test environment. A stochastic pitting is observed at an acid pH, near-neutral conditions result in a deterministic-type pitting, and a general corrosion is observed at an alkaline pH

Microstructure and corrosion evolution of additively manufactured aluminium alloy AA7075 as a function of ageing

Additively manufactured high strength aluminium alloy AA7075 was prepared using selective laser melting. High strength aluminium alloys prepared by selective laser melting have not been widely studied to date. The evolution of microstructure and hardness, with the attendant corrosion, were investigated. Additively manufactured AA7075 was investigated both in the as-produced condition and as a function of artificial ageing. The microstructure of specimens prepared was studied using electron microscopy. Production of AA7075 by selective laser melting generated a unique microstructure, which was altered by solutionising and further altered by artificial ageing - resulting in microstructures distinctive to that of wrought AA7075-T6. The electrochemical response of additively manufactured AA7075 was dependent on processing history, and unique to wrought AA7075-T6, whereby dissolution rates were generally lower for additively manufactured AA7075. Furthermore, immersion exposure testing followed by microscopy, indicated different corrosion morphology for additively manufactured AA7075, whereby resultant pit size was notably smaller, in contrast to wrought AA7075-T6.Comment: 37 pages, includes 4 Tables and 11 Figure

Spontaneous externalization of peritoneal catheter through the abdominal wall in a patient with hydrocephalus: a case report

Since 1905, the abdominal cavity has been used for absorption of cerebrospinal fluid in patients with hydrocephalus. We report a case of a 33-year-old female, in which a spontaneous extrusion of the peritoneal catheter of a ventriculo-peritoneal shunt through the intact abdominal wall occurred. We suggest that the rather hard peritoneal catheter eroded the abdominal wall, caused local inflammation, and then extruded through the skin. Additionally, the intestinal peristaltic movements, the omental activity and the intraabdominal pressure could play an adjuvant part, pressing direct the foreign body from the peritoneal cavity toward the skin

Revolutionising inverse design of magnesium alloys through generative adversarial networks

The utility of machine learning (ML) techniques in materials science has accelerated materials design and discovery. However, the accuracy of ML models - particularly deep neural networks - heavily relies on the quality and quantity of the training data. Data collection methods often have limitations arising from cost, difficulty, and resource-intensive human efforts. Thus, limited high-quality data, especially for novel materials, poses a significant challenge in developing reliable ML models. Generative adversarial networks (GANs) offer one solution to augment datasets through synthetic sample generation. The present work explores the application of GANs in magnesium (Mg) alloy design, by training two deep neural networks within the structure of a Wasserstein GAN to generate new (novel) alloys with desired mechanical properties. This data augmentation-based strategy contributes to model robustness, particularly in cases where traditional data collection is impractical. The approach presented may expedite Mg alloy development, through a GAN assisted inverse design approach.Comment: 23 pages, 4 figures, 2 tables, 1 Github repositor

Electrochemical Response of AA7075-T651 Following Immersion in NaCl Solution

The electrochemical behavior of AA7075-T651 following immersion in quiescent 0.1M NaCl is presented. Electrochemical impedance at various polarization intervals was determined using Fourier transformation of potentiostatically induced current transients. This allowed for rapid determination of the impedance response at fixed intervals revealing a more detailed insight into the kinetic response of the alloy when assessed with complementary analysis tools such as potentiodynamic testing. This led to a discussion regarding aspects of dissolution phenomena prior to alloy breakdown and at short immersion times

A primitive machine learning tool for the mechanical property prediction of multiple principal element alloys

Multi-principal element alloys (MPEAs) are produced by combining metallic elements in what is a diverse range of proportions. MPEAs reported to date have revealed promising performance due to their exceptional mechanical properties. Training a machine learning (ML) model on known performance data is a reasonable method to rationalise the complexity of composition dependent mechanical properties of MPEAs. This study utilises data from a specifically curated dataset, that contains information regarding six mechanical properties of MPEAs. A parser tool was introduced to convert chemical composition of alloys into the input format of the ML models, and a number of ML models were applied. Finally, Gradio was used to visualise the ML model predictions and to create a user-interactive interface. The ML model presented is an initial primitive model (as it does not factor in aspects such as MPEA production and processing route), however serves as a an initial user tool, whilst also providing a workflow for other researchers