Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

2.2K
Crystallization is a phase transformation process in which crystals are precipitated from a supersaturated solution or formed from other sources. During crystallization, atoms or molecules arrange themselves into a well-defined, rigid crystal lattice to minimize energy.
Initiating crystallization involves manipulating the concentration of the solute and the temperature of the solution. Since crystal growth occurs when the ratio of concentration and solubility of the solute in the solvent...
2.2K
Recrystallization: Solid–Solution Equilibria01:10

Recrystallization: Solid–Solution Equilibria

1.1K
Recrystallization is a purification technique used to separate impurities from solid compounds. In this technique, no chemical reactions occur. Instead, it exploits physical properties only, specifically, the solubility differences between the desired compound and impurities, either at a single temperature or at different temperatures, and under other selected conditions. The solid-solution equilibrium (solubility equilibrium) of each component in the solution represents a binary phase...
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ecocomposite Filaments from Spent Coffee Grounds for FFF 3D Printing: Material Properties and Printability.

Polymers·2026
Same author

Artificial intelligence application in the prediction of spontaneous preterm birth by cervical length in the first trimester of pregnancy: Comparison of three measurement methods.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics·2026
Same author

A novel technique with cool-tip radiofrequency ablation for selective fetal reduction in complicated monochorionic twin.

Taiwanese journal of obstetrics & gynecology·2025
Same author

Optimally Miscible Polymer Bulk-Heterojunction-Particles for Nonsurfactant Photocatalytic Hydrogen Evolution.

Journal of the American Chemical Society·2024
Same author

Deep learning model for diagnosis of venous thrombosis from lower extremity peripheral ultrasound imaging.

iScience·2024
Same author

An investigation into the shifting landscape preferences of rural residents in Taiwan and their relationship with ecological indicators.

Scientific reports·2024

Related Experiment Video

Updated: Jul 30, 2025

A Microfluidic Approach for the Study of Ice and Clathrate Hydrate Crystallization
08:01

A Microfluidic Approach for the Study of Ice and Clathrate Hydrate Crystallization

Published on: August 18, 2022

3.1K

Deep Learning Model to Predict Ice Crystal Growth.

Bor-Yann Tseng1, Chen-Wei Conan Guo1, Yu-Chen Chien1

  • 1Department of Engineering Science, National Cheng Kung University, No. 1, University Rd., Tainan, 701, Taiwan.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|May 17, 2023
PubMed
Summary

This study introduces artificial intelligence for controlling casting solidification. A deep learning model predicts dendritic crystal growth, while reinforcement learning controls the process for superior material properties.

Keywords:
castingdendritic structuregenerative model materialreinforcement learningsolidification

More Related Videos

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity
08:46

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity

Published on: January 15, 2014

9.2K
Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering
09:15

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

Published on: August 14, 2018

10.6K

Related Experiment Videos

Last Updated: Jul 30, 2025

A Microfluidic Approach for the Study of Ice and Clathrate Hydrate Crystallization
08:01

A Microfluidic Approach for the Study of Ice and Clathrate Hydrate Crystallization

Published on: August 18, 2022

3.1K
Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity
08:46

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity

Published on: January 15, 2014

9.2K
Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering
09:15

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

Published on: August 14, 2018

10.6K

Area of Science:

  • Materials Science
  • Computational Materials Science
  • Artificial Intelligence in Manufacturing

Background:

  • Controllable manufacturing processes are essential for developing advanced materials with specific properties.
  • Casting is an economical and versatile manufacturing method, but controlling its solidification stage is key to product quality.
  • Traditional solidification modeling is computationally intensive and mathematically complex.

Purpose of the Study:

  • To develop a controllable solidification process for casting using artificial intelligence.
  • To predict dendritic crystal growth morphology and control solidification using deep learning and reinforcement learning.
  • To optimize casting processes for advanced materials and enhance material design properties.

Main Methods:

  • A deep learning model was developed to predict dendritic crystal growth morphology.
  • Reinforcement learning was employed to control the solidification process.
  • The deep learning model was trained using data generated from the phase field method.

Main Results:

  • The deep learning model successfully predicted the solidification process.
  • Crystal growth structures were modified by adjusting the supercooling degree within the deep learning model.
  • Reinforcement learning effectively controlled the dendritic structures.

Conclusions:

  • Artificial intelligence offers a novel approach to optimizing casting processes.
  • This AI-driven method enables precise control over solidification for improved material properties.
  • The research paves the way for AI applications in advanced material processing and design.