arXiv subjects

Thomas Dorin

Publications and source records attributed to Thomas Dorin.

3 recordsLinked to original sources

arXiv2023

Improved iron-tolerance in recycled aluminum alloys via direct strip casting process

Recycled aluminum alloys are pivotal for sustainable manufacturing, offering strength, durability, and environmental advantages. However, the presence of iron (Fe) impurities poses a major challenge, undermining their properties and recyclability. Conventional manufacturing processes result in coarse Fe-rich intermetallic compounds that limit the tolerance of Fe content and negatively influence performance of advanced aluminum alloys. To address this, rapid solidification techniques like direct strip casting have been explored. In this work, a detailed study of the strip cast microstructure was conducted by scanning electron microscopy, electron backscattered diffraction and atom probe tomography. Our results reveal that alloys produced by DSC exhibit significantly refined microstructures and are free from coarse Fe-rich intermetallics, thereby retaining the majority of Fe in solid solution. These findings indicate that strip casting significantly enhances Fe-tolerance in aluminum alloys, making it an attractive process for future aluminum recycling, with implications for sustainable high-performance applications.

cond-mat.mtrl-sci↗

arXiv2020

Accelerated Bayesian Optimization throughWeight-Prior Tuning

Bayesian optimization (BO) is a widely-used method for optimizing expensive (to evaluate) problems. At the core of most BO methods is the modeling of the objective function using a Gaussian Process (GP) whose covariance is selected from a set of standard covariance functions. From a weight-space view, this models the objective as a linear function in a feature space implied by the given covariance K, with an arbitrary Gaussian weight prior ${\bf w} \sim \mathcal{N} ({\bf 0}, {\bf I})$. In many practical applications there is data available that has a similar (covariance) structure to the objective, but which, having different form, cannot be used directly in standard transfer learning. In this paper we show how such auxiliary data may be used to construct a GP covariance corresponding to a more appropriate weight prior for the objective function. Building on this, we show that we may accelerate BO by modeling the objective function using this (learned) weight prior, which we demonstrate on both test functions and a practical application to short-polymer fibre manufacture.

stat.ML↗

arXiv2018

Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation

The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present. The method presented borrows techniques from reproducing kernel Banach space theory (specifically m-kernels) and leverages them to convert (or re-weight) existing covariance functions into new, problem-specific covariance functions. The key advantage of this approach is that rather than relying on the user to manually select (with some hyperparameter tuning and experimentation) an appropriate covariance function it constructs the covariance function to specifically match the problem at hand. The technique is demonstrated on two real-world problems - specifically alloy design and short-polymer fibre manufacturing - as well as a selected test function.

stat.ML↗