Accelerating materials discovery using machine learning
Y Juan, Y Dai, Y Yang, J Zhang - Journal of Materials Science & …, 2021 - Elsevier
The discovery of new materials is one of the driving forces to promote the development of
modern society and technology innovation, the traditional materials research mainly …
modern society and technology innovation, the traditional materials research mainly …
Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective
Carbon capture, utilization, and storage (CCUS) is a promising pathway to decarbonize
fossil-based power and industrial sectors and is a bridging technology for a sustainable …
fossil-based power and industrial sectors and is a bridging technology for a sustainable …
[PDF][PDF] Customer churn prediction in telecommunication industry using deep learning
Without proper analysis and forecasting, industries will find themselves repeatedly churning
customers, which the telecom industry in particular cannot afford. A predictable model for …
customers, which the telecom industry in particular cannot afford. A predictable model for …
Optimum design of a seat bracket using artificial neural networks and dandelion optimization algorithm
Nature-inspired metaheuristic algorithms are gaining popularity with their easy applicability
and ability to avoid local optimum points, and they are spreading to wide application areas …
and ability to avoid local optimum points, and they are spreading to wide application areas …
A novel integrated BPNN/SNN artificial neural network for predicting the mechanical performance of green fibers for better composite manufacturing
Since the mechanical properties of green cellulosic fibers are only determined
experimentally with high diversity, introducing prediction methods for such intrinsic …
experimentally with high diversity, introducing prediction methods for such intrinsic …
The role of artificial neural networks in prediction of mechanical and tribological properties of composites—a comprehensive review
The artificial neural network (ANN) approach motivated by the biological nervous system is
an inspiring mathematical tool that simulates many complicated engineering applications …
an inspiring mathematical tool that simulates many complicated engineering applications …
Mechanical properties prediction of composite laminate with FEA and machine learning coupled method
C Zhang, Y Li, B Jiang, R Wang, Y Liu, L Jia - Composite Structures, 2022 - Elsevier
In order to predict mechanical properties of composite laminate, a method coupling finite
element analysis (FEA) and machine learning is established to analyze three examples of …
element analysis (FEA) and machine learning is established to analyze three examples of …
[HTML][HTML] Application of machine learning methods on dynamic strength analysis for additive manufactured polypropylene-based composites
This study aimed at applying machine learning (ML) methods to analyze dynamic strength of
3D-printed polypropylene (PP)-based composites. The dynamic strength of additive …
3D-printed polypropylene (PP)-based composites. The dynamic strength of additive …
[HTML][HTML] Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems
Reinforced composite is a preferred choice of material for the design of industrial lightweight
structures. As of late, composite materials analysis and development utilizing machine …
structures. As of late, composite materials analysis and development utilizing machine …
Data-driven modeling to predict the load vs. displacement curves of targeted composite materials for industry 4.0 and smart manufacturing
This work presents an approach for smart manufacturing focusing on Industry 4.0 to predict
the load vs. displacement curve of targeted cotton fiber/Polypropylene (PP) composite …
the load vs. displacement curve of targeted cotton fiber/Polypropylene (PP) composite …