Smart materials: rational design in biosystems via artificial intelligence

K Sagdic, I Eş, M Sitti, F Inci - Trends in biotechnology, 2022 - cell.com
Industry 4.0 encompasses a new industrial revolution in which advanced manufacturing
systems are interconnected with information technologies. These sophisticated data …

[HTML][HTML] Nickel titanium alloys as orthodontic archwires: A narrative review

I Uysal, B Yilmaz, AO Atilla, Z Evis - Engineering Science and Technology …, 2022 - Elsevier
Nickel-titanium (NiTi) archwires have been widely used in orthodontic treatments at the
aligning and leveling step which is the initial stage of the treatment. This review presents the …

Machine learning assisted design of novel refractory high entropy alloys with enhanced mechanical properties

AA Catal, E Bedir, R Yilmaz, MA Swider, C Lee… - Computational Materials …, 2024 - Elsevier
This paper details an alloy design effort by machine learning (ML) attempting to design
novel refractory high entropy alloys (RHEAs) with exceptional mechanical properties at …

[HTML][HTML] Machine learning-assisted extrusion-based 3D bioprinting for tissue regeneration applications

DV Krishna, MR Sankar - Annals of 3D Printed Medicine, 2023 - Elsevier
Extrusion-based 3D bioprinting (EBBP) prints tissues, including nerve guide conduits, bone
tissue engineering, skin tissue repair, cartilage repair, and muscle repair. The EBBP …

Design of a NiTiHf shape memory alloy with an austenite finish temperature beyond 400 C utilizing artificial intelligence

AA Catal, E Bedir, R Yilmaz, D Canadinc - Journal of Alloys and …, 2022 - Elsevier
This paper details the design process of a ternary NiTiHf shape memory alloy (SMA) with an
austenite finish temperature (A f) beyond 400° C. Specifically, available experimental data …

A comprehensive exploration of shape memory alloys: Fundamentals, structural reinforcements, nano-analysis, machine learning perspective, and emerging …

EK Kumar, SS Patel, SK Panda, BK Patle… - Mechanics of …, 2024 - Taylor & Francis
Shape memory alloys (SMAs) are widely used across various industries, including medicine,
due to their inherent properties such as the shape memory effect, pseudo-elasticity …

Early predicting tribocorrosion rate of dental implant titanium materials using random forest machine learning models

RA Ramachandran, VAR Barão, D Ozevin… - Tribology …, 2023 - Elsevier
Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may
lead to secondary revision surgery and associated risks of implantable devices. Therefore …

Ni50. 8Ti49. 2 alloy prepared by double-wire+ arc additive manufacturing with a substrate heating temperature of 600 C

J Han, X Chen, G Zhang, B Liu, Y Cai, M Chen… - Journal of Manufacturing …, 2023 - Elsevier
In this study, a Ni 50.8 Ti 49.2 single-walled component was prepared in-situ using double-
wire+ arc additive manufacturing using a substrate heated to 600° C and TA1 and ER-Ni …

Machine learning-assisted design of biomedical high entropy alloys with low elastic modulus for orthopedic implants

HC Ozdemir, E Bedir, R Yilmaz, MB Yagci… - Journal of Materials …, 2022 - Springer
This paper focuses on finding an optimum composition for the TiTaHfNbZr quinary high
entropy alloy (HEA) system with an elastic modulus close to that of bone in order to attain a …

[HTML][HTML] Machine learning guided optimal composition selection of niobium alloys for high temperature applications

T Mohanty, KS Chandran, TD Sparks - APL Machine Learning, 2023 - pubs.aip.org
Nickel-and cobalt-based superalloys are commonly used as turbine materials for high-
temperature applications. However, their maximum operating temperature is limited to about …