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Surface Properties

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Optimization of running-in surface morphology parameters based on the AutoML model.

PloS one
Running-in is an important and relatively complicated process. The surface morphology prior to running-in affects the surface morphology following the running-in process, which in turn influences the friction and wear characteristics of the workpiece...

Machine Learning Approach to Analyze the Surface Properties of Biological Materials.

ACS biomaterials science & engineering
Similar to how CRISPR has revolutionized the field of molecular biology, machine learning may drastically boost research in the area of materials science. Machine learning is a fast-evolving method that allows for analyzing big data and unveiling cor...

Generation of Interconnected Neural Clusters in Multiscale Scaffolds from Human-Induced Pluripotent Stem Cells.

ACS applied materials & interfaces
The development of in vitro neural networks depends to a large extent on the scaffold properties, including the scaffold stiffness, porosity, and dimensionality. Herein, we developed a method to generate interconnected neural clusters in a multiscale...

A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells.

Scientific reports
Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. On a cellular level, the development of fibrosis is associated with the activa...

Identifying specular highlights: Insights from deep learning.

Journal of vision
Specular highlights are the most important image feature for surface gloss perception. Yet, recognizing whether a bright patch in an image is due to specular reflection or some other cause (e.g., texture marking) is challenging, and it remains unclea...

Prediction of abrasive wears behavior of dental composites using an artificial neural network.

Computer methods in biomechanics and biomedical engineering
Resin composites are widely used as dental restorative materials since dental parts are subjected to prolonged wear and ultimately need to be replaced. The objective of this study is to analyze the potential of the feed-forward back propagation artif...

Prediction of the Lotus Effect on Solid Surfaces by Machine Learning.

Small (Weinheim an der Bergstrasse, Germany)
Superhydrophobic surfaces with the "lotus effect" have wide applications in daily life and industry, such as self-cleaning, anti-freezing, and anti-corrosion. However, it is difficult to reliably predict whether a designed superhydrophobic surface ha...

Using artificial intelligence to predict the final color of leucite-reinforced ceramic restorations.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
OBJECTIVES: The aim of this study was to evaluate the accuracy of machine learning regression models in predicting the final color of leucite-reinforced glass CAD/CAM ceramic veneer restorations based on substrate shade, ceramic shade, thickness and ...

Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales.

International journal of pharmaceutics
The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and da...

Development of predictive algorithms for the wear resistance of denture teeth materials.

Journal of the mechanical behavior of biomedical materials
OBJECTIVES: To investigate the wear resistance of conventional, CAD-milled and 3D-printed denture teeth in vitro with simulated aging. To use the collected data to train single time series sample model LSTM and provide proof of concept.