Results 251 to 260 of about 1,099,320 (331)

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

open access: yesAdvanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani   +2 more
wiley   +1 more source

A Case‐Based Reasoning Approach to Model Manufacturing Constraints for Impact Extrusion

open access: yesAdvanced Engineering Materials, EarlyView.
A hybrid modeling approach is presented that combines constraint‐based process modeling and case‐based reasoning. The model formalizes manufacturing constraints and integrates simulation data to model complex manufacturing processes. The approach supports manufacturability analysis during product design through an adaptive modeling environment.
Kevin Herrmann   +5 more
wiley   +1 more source

Non‐Destructive and Mechanical Characterization of the Bond Quality of Co‐Extruded Titanium‐Aluminum Profiles

open access: yesAdvanced Engineering Materials, EarlyView.
This study investigates the bond quality of co‐extruded aluminum–titanium hybrid profiles, focusing on the lateral angular co‐extrusion (LACE) process. It examines how heat treatments (HT) affect intermetallic phase formation, bond strength, and material properties.
Norman Mohnfeld   +9 more
wiley   +1 more source

Tobacco product litter as a form of postconsumption marketing: an observational study in India. [PDF]

open access: yesTob Control
Grilo G   +6 more
europepmc   +1 more source

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

open access: yesAdvanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi   +4 more
wiley   +1 more source

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