scholarly journals Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5764
Author(s):  
Karol Frydrych ◽  
Kamran Karimi ◽  
Michal Pecelerowicz ◽  
Rene Alvarez ◽  
Francesco Javier Dominguez-Gutiérrez ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Multiscale Modeling ◽  
Data Science ◽  
Large Data ◽  
Mechanical Deformation ◽  
Materials Informatics ◽  
Fast Growing ◽  
Regression Methods ◽  
Classification And Regression ◽  
Materials Behavior

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials’ discovery or develop new understanding of materials’ behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.

scholarly journals Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges

2021 ◽  
Author(s):  
Karol Frydrych ◽  
Kamran Karimi ◽  
Michal Pecelerowicz ◽  
Rene Alvarez ◽  
Francesco Javier Dominguez-Gutiérrez ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Multiscale Modeling ◽  
Data Science ◽  
Large Data ◽  
Mechanical Deformation ◽  
Metals And Alloys ◽  
Materials Informatics ◽  
Fast Growing ◽  
Regression Methods ◽  
Classification And Regression

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials discovery or develop new understanding of materials’ behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.

Supervised Learning in Game Data Science

2021 ◽  
pp. 179-218
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Data Science ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Regression Methods ◽  
Vector Machines ◽  
Pros And Cons ◽  
Classification And Regression

This chapter discusses several classification and regression methods that can be used with game data. Specifically, we will discuss regression methods, including Linear Regression, and classification methods, including K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Linear Discriminant Analysis, Support Vector Machines, Decisions Trees, and Random Forests. We will discuss how you can setup the data to apply these algorithms, as well as how you can interpret the results and the pros and cons for each of the methods discussed. We will conclude the chapter with some remarks on the process of application of these methods to games and the expected outcomes. The chapter also includes practical labs to walk you through the process of applying these methods to real game data.

PHYSICAL AND MECHANICAL PROPERTIES OF GLUED-LAMINATED LUMBER FROM FAST-GROWING TREE SPECIES USING MAHOGANY TANNIN ADHESIVE

2019 ◽  
Vol 51 (2) ◽  
pp. 132-143
Author(s):  
Andi Sri Rahayu Diza Lestari ◽  
Yusuf Sudo Hadi ◽  
Dede Hermawan ◽  
Adi Santoso ◽  
Antonio Pizzi
Keyword(s):  
Mechanical Properties ◽  
Tree Species ◽  
Physical And Mechanical Properties ◽  
Tannin Adhesive ◽  
Fast Growing

Determination of mechanical properties of two-phase and hybrid nanocomposites: experimental determination and multiscale modeling

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mahmoud Haghighi ◽  
Hossein Golestanian ◽  
Farshid Aghadavoudi
Keyword(s):  
Mechanical Properties ◽  
Ultimate Strength ◽  
Multiscale Modeling ◽  
Filler Content ◽  
Hybrid Nanocomposites ◽  
Dynamics Simulation ◽  
Strength Increase ◽  
Two Phase ◽  
Macro Scale ◽  
Elongation To Failure

Abstract In this paper, the effects of filler content and the use of hybrid nanofillers on agglomeration and nanocomposite mechanical properties such as elastic moduli, ultimate strength and elongation to failure are investigated experimentally. In addition, thermoset epoxy-based two-phase and hybrid nanocomposites are simulated using multiscale modeling techniques. First, molecular dynamics simulation is carried out at nanoscale considering the interphase. Next, finite element method and micromechanical modeling are used for micro and macro scale modeling of nanocomposites. Nanocomposite samples containing carbon nanotubes, graphene nanoplatelets, and hybrid nanofillers with different filler contents are prepared and are tested. Also, field emission scanning electron microscopy is used to take micrographs from samples’ fracture surfaces. The results indicate that in two-phase nanocomposites, elastic modulus and ultimate strength increase while nanocomposite elongation to failure decreases with reinforcement weight fraction. In addition, nanofiller agglomeration occurred at high nanofiller contents especially higher than 0.75 wt% in the two-phase nanocomposites. Nanofiller agglomeration was observed to be much lower in the hybrid nanocomposite samples. Therefore, using hybrid nanofillers delays/prevents agglomeration and improves mechanical properties of nanocomposite at the same total filler content.

scholarly journals Fast Preparation of High-Performance Wood Materials Assisted by Ultrasonic and Vacuum Impregnation

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 567
Author(s):  
Hong Yang ◽  
Mingyu Gao ◽  
Jinxin Wang ◽  
Hongbo Mu ◽  
Dawei Qi
Keyword(s):  
Mechanical Properties ◽  
High Performance ◽  
Mechanical Tests ◽  
Vacuum Impregnation ◽  
Natural Forests ◽  
Ultrasonic Pretreatment ◽  
Fast Growing ◽  
Before And After ◽  
New Material ◽  
Ft Ir

In the absence of high-quality hardwood timber resources, we have gradually turned our attention from natural forests to planted fast-growing forests. However, fast-growing tree timber in general has defects such as low wood density, loose texture, and poor mechanical properties. Therefore, improving the performance of wood through efficient and rapid technological processes and increasing the utilization of inferior wood is a good way to extend the use of wood. Densification of wood increases the strength of low-density wood and extends the range of applications for wood and wood-derived products. In this paper, the effects of ultrasonic and vacuum pretreatment on the properties of high-performance wood were explored by combining sonication, vacuum impregnation, chemical softening, and thermomechanical treatments to densify the wood; then, the changes in the chemical composition, microstructure, and mechanical properties of poplar wood before and after treatment were analyzed comparatively by FT-IR, XRD, SEM, and mechanical tests. The results showed that with ultrasonic pretreatment and vacuum impregnation, the compression ratio of high-performance wood reached its highest level and the MOR and MOE reached their maximums. With the help of this method, fast-growing softwoods can be easily prepared into dense wood materials, and it is hoped that this new material can be applied in the fields of construction, aviation, and automobile manufacturing.

Anatomical, Chemical and Mechanical Properties of Fast-Growing Populus × Euramericana cv. ‘74/76’

IAWA Journal ◽  
2014 ◽  
Vol 35 (2) ◽  
pp. 158-169 ◽  
Author(s):  
Zhao Rongjun ◽  
Yao Chunli ◽  
Cheng Xianbao ◽  
Lu Jianxiong ◽  
Fei Benhua ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Sandy Loam ◽  
Physical And Mechanical Properties ◽  
Wood Properties ◽  
Planting Density ◽  
Dry Density ◽  
Growth Rings ◽  
Fast Growing ◽  
Breast Height ◽  
Large Growth

The anatomical characteristics, chemical composition, and physical and mechanical properties of fast-growing Populus × euramericana cv. ‘74/76’ juvenile wood were investigated. Four- to five-year-old clonal plantation trees were harvested from two different experimental sites in the suburbs of Beijing. The Shunyi site had black alkali soil with a planting density of 4 × 6 m and the Miyun site had sandy loam soil with a planting density of 3 × 5 m. The test results showed that the poplar trees from the two sites were both fast growing, with poplar at Shunyi growing faster than at Miyun. There were no significant differences in wood properties between trees grown at the two sites. Fiber length at breast height varied from 872 to 1300 μm between growth rings, average fiber width varied from 21.0 to 25.5 μm and double wall thickness varied from 5.0 to 6.6 μm. Average cellulose, lignin and hemicellulose contents in the samples were 48.9%, 25.4%, and 18.8%, respectively. MFA was higher in the first two growth rings (20–25°), and then decreased rapidly to 12° close to the bark. The average air-dry density at breast height was 401 kg/m3 while the average MOE at breast height was 9.3 GPa. The trees showed large growth rates in both height and stem diameter during the growing season. However, wood properties of the juvenile poplar appeared to be similar to those of poplars with a slower growth rate.

Research on Chemical Modification of Wood of Fast-Growing Poplar

2013 ◽  
Vol 749 ◽  
pp. 461-465
Author(s):  
De Jun Shen ◽  
Chang Hai Yu ◽  
Zhen Xing
Keyword(s):  
Mechanical Properties ◽  
Molecular Weight ◽  
Structural Material ◽  
Chemical Modification ◽  
Orthogonal Test ◽  
Low Molecular Weight ◽  
Wood Structure ◽  
Optimum Conditions ◽  
Fast Growing ◽  
Performance Requirements

This topic is considered to modify the fast-growing Poplar to improve the properties, in order to fully meet the performance requirements for the structural material. This study aims to improve the dimensional stability and some other mechanical properties through impregnated with the low-molecular-weight PF resin. Through design orthogonal test in different mole ratio of Formaldehyde and Phenol, different amount of NaOH and PVA, we make PF resin to impregnate Poplar and pressing into laminated timber to measure bonding strength, MOR, MOE. The study indicated that: the optimum conditions of the low molecular weight PF resin for modify Poplar are: mole ratio of Formaldehyde and Phenol is 2.4, mole ratio of NaOH and phenol is 0.05, amount of PVA is 3% of the phenol. Under this condition Poplar specimen got the biggest increase in various properties and it can satisfy the requirements of the outdoor wood structure.

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

EFFECTS OF STOICHIOMETRY ON PROPERTIES OF DGEBF/DETDA EPOXY USING MOLECULAR DYNAMICS

2021 ◽  
Author(s):  
SAGAR PATIL ◽  
MICHAEL OLAYA ◽  
PRATHAMESH DESHPANDE ◽  
MARIANNA MAIARÙ ◽  
GREGORY ODEGARD
Keyword(s):  
Mechanical Properties ◽  
Molecular Dynamics ◽  
Molecular Modeling ◽  
Bulk Modulus ◽  
Mass Density ◽  
Strong Dependence ◽  
Crosslinking Density ◽  
Mechanical Deformation ◽  
Epoxy System ◽  
Reactive Interface

This article details the molecular modeling of full and off-stoichiometry models of the DGEBF/DETDA epoxy system using Molecular Dynamics to predict the mechanical properties as a function of the crosslinking density. The Reactive Interface Force Field (IFF-R) is implemented in this work to simulate mechanical deformation. The “fix bond/react” command in LAMMPS is used to simulate crosslinking between epoxy monomers. The results show that the predicted mass density, volumetric shrinkage, and bulk modulus have a strong dependence on the stoichiometry of the epoxy.

Recovery, Recrystallization, and Grain-Growth

2019 ◽  
Vol 41 (1) ◽  
pp. 1-1
Author(s):  
Iqra Zubair Awan Iqra Zubair Awan
Keyword(s):  
Mechanical Properties ◽  
State Physics ◽  
Heat Treatment ◽  
Grain Growth ◽  
Structural Damage ◽  
Physical And Mechanical Properties ◽  
Mechanical Deformation ◽  
Solid State Physics ◽  
Science And Engineering ◽  
Thermally Activated

This is a brief review of the important phenomena of recovery, recrystallization as well as grain-growth. The three mentioned phenomena are the mechanisms by which metals and alloys fix the structural damage introduced by the mechanical deformation and, as a consequence, in the physical and mechanical properties. These rehabilitation mechanisms are thermally activated. For this process, the materials have to be heated and any such heat-treatment is meant to reduce deformation-induced break is termed annealing. Other or different heat-treatments lead to recovery and recrystallization. It is rather strange that, though these phenomena are extremely important in metallurgical science and engineering, not so much work has been done as that in corrosion and shape memory technologies. An attempt has been made here to summarize all important aspects of these phenomena for the benefits of students of metallurgy, chemistry and solid state physics.

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