AIMC Topic: Materials Testing

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A novel machine learning based computational framework for homogenization of heterogeneous soft materials: application to liver tissue.

Biomechanics and modeling in mechanobiology
Real-time simulation of organs increases comfort and safety for patients during the surgery. Proper generalized decomposition (PGD) is an efficient numerical method with coordinate errors below 1 mm and response time below 0.1 s that can be used for ...

Predicting degradation rate of genipin cross-linked gelatin scaffolds with machine learning.

Materials science & engineering. C, Materials for biological applications
Genipin can improve weak mechanical properties and control high degradation rate of gelatin, as a cross-linker of gelatin which is widely used in tissue engineering. In this study, genipin cross-linked gelatin biodegradable porous scaffolds with diff...

Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques.

PloS one
Single-point incremental forming (SPIF) is a technology that allows incremental manufacturing of complex parts from a flat sheet using simple tools; further, this technology is flexible and economical. Measuring the forming force using this technolog...

Magnetically Actuated Heterogeneous Microcapsule-Robot for the Construction of 3D Bioartificial Architectures.

ACS applied materials & interfaces
Core-shell microcapsules as one type of the most attractive carriers and reactors have been widely applied in the fields of drug screening and tissue engineering owing to their excellent biocompatibility and semi-permeability. Yet, the spatial organi...

Machine learning for the prediction of sunscreen sun protection factor and protection grade of UVA.

Experimental dermatology
We report a prediction model for sunscreen sun protection factor (SPF) and protection grade of ultraviolet (UV) A (PA) based on machine learning. We illustrate with real clinical test results of UV protection ability of sunscreen for SPF and PA. With...

Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning.

PloS one
High performance materials, from natural bone over ancient damascene steel to modern superalloys, typically possess a complex structure at the microscale. Their properties exceed those of the individual components and their knowledge-based improvemen...

An In Vitro Study to Evaluate and Compare the Hemocompatibility of Titanium and Zirconia Implant Materials after Sandblasted and Acid-etched Surface Treatment.

The journal of contemporary dental practice
AIM: This study was aimed to investigate the hemocompatibility of zirconia and titanium implant materials after surface treatment with sandblasting and acid etching (SLA).

Use of Artificial Neural Network in Determination of Shade, Light Curing Unit, and Composite Parameters' Effect on Bottom/Top Vickers Hardness Ratio of Composites.

BioMed research international
OBJECTIVE: To assess the influence of light emitting diode (LED) and quartz tungsten halogen (QTH) light curing unit (LCU) on the bottom/top (B/T) Vickers Hardness Number (VHN) ratio of different composites with different shades and determination of ...

Robotic hip joint testing: Development and experimental protocols.

Medical engineering & physics
The use of robotic systems combined with force sensing is emerging as the gold standard for in vitro biomechanical joint testing, due to the advantage of controlling all six degrees of freedom independently of one another. This paper describes a nove...

Vibrational Properties of Metastable Polymorph Structures by Machine Learning.

Journal of chemical information and modeling
Despite vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity and other transport properties of solids, their inclusion in ab initio materials repositories has been hindered...