Results 181 to 190 of about 494,722 (259)

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch   +3 more
wiley   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

open access: yesAdvanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang   +2 more
wiley   +1 more source

Microstructure Evolution of a VMnFeCoNi High‐Entropy Alloy After Synthesis, Swaging, and Annealing

open access: yesAdvanced Engineering Materials, EarlyView.
The synthesis and processing (rotary swaging and annealing) of the novel VMnFeCoNi alloy is investigated, alongside the estimation of the grain size effect on hardness. Analysis of a wide grain size range of recrystallized microstructures (12–210 µm) reveals a low annealing twin density.
Aditya Srinivasan Tirunilai   +6 more
wiley   +1 more source

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann   +8 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

open access: yesAdvanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer   +4 more
wiley   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

open access: yesAdvanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle   +5 more
wiley   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

open access: yesAdvanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare   +5 more
wiley   +1 more source

Extrusion‐Based Additive Manufacturing of Advanced Ceramics: Strengthening Mechanisms and Process Optimization Review

open access: yesAdvanced Engineering Materials, EarlyView.
This review comprehensively evaluates extrusion‐based additive manufacturing for advanced ceramics, detailing feedstock options and key process parameters. By critically addressing defect mechanisms like porosity and cracking, the work highlights optimization strategies through machine learning and advanced postprocessing.
Meisam Bakhtiari   +4 more
wiley   +1 more source

The Influence of Field‐Assisted Sintering Technology (FAST) Temperature and Cooling Rate on the Microstructural Evolution in Gamma‐Titanium Aluminide Alloy GE4822

open access: yesAdvanced Engineering Materials, EarlyView.
Controlling the Field Assisted Sintering Technology (FAST) parameters, dwell temperature and cooling rate, significantly influences the microstructural evolution in titanium aluminide GE4822. Significant γ‐lamellar colonies develop only upon cooling through the α‐transus.
Jack Krohn, James Pepper, Martin Jackson
wiley   +1 more source

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