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

Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...

You might also read

Related Articles

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

Sort by
Same author

Cytoskeletal Prestress Regulates RIG-I-Mediated Innate Immunity.

Biophysica·2026
Same author

An Overview of Additive Manufacturing of Triply Periodic Minimal Surface (TPMS) Structures.

Polymers·2025
Same author

Investigation of Thermomechanical Properties of Hollow Glass Microballoon-Filled Composite Materials Developed by Additive Manufacturing with Machine Learning Validation.

Polymers·2025
Same author

Influence of Heat Treatment on Microstructure, Mechanical Properties, and Damping Behavior of 2024 Aluminum Matrix Composites Reinforced by Carbon Nanoparticles.

Nanomaterials (Basel, Switzerland)·2024
Same author

Deep Learning Powered Identification of Differentiated Early Mesoderm Cells from Pluripotent Stem Cells.

Cells·2024
Same author

The Effect of Microballoon Volume Fraction on the Elastic and Viscoelastic Properties of Hollow Microballoon-Filled Epoxy Composites.

Materials (Basel, Switzerland)·2023

Related Experiment Video

Updated: Jun 27, 2026

Printing Thermoresponsive Reverse Molds for the Creation of Patterned Two-component Hydrogels for 3D Cell Culture
10:49

Printing Thermoresponsive Reverse Molds for the Creation of Patterned Two-component Hydrogels for 3D Cell Culture

Published on: July 10, 2013

15.0K

Leveraging Deep Learning and Generative AI for Predicting Rheological Properties and Material Compositions of 3D

Sakib Mohammad1, Rafee Akand2, Kaden M Cook2

  • 1School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University Carbondale, Carbondale, IL 62901, USA.

Gels (Basel, Switzerland)
|October 25, 2024
PubMed
Summary

Deep learning models predict rheological properties and composition for 3D-printed polyacrylamide (PAA) hydrogels. These artificial intelligence (AI) models enable precise material design by mapping input-output relationships for 3D printing.

Keywords:
3D printingdeep learninggenerative AIpolyacrylamiderheology

More Related Videos

Agarose Fluid Gels Formed by Shear Processing During Gelation for Suspended 3D Bioprinting
07:26

Agarose Fluid Gels Formed by Shear Processing During Gelation for Suspended 3D Bioprinting

Published on: May 26, 2023

2.3K
Author Spotlight: Understanding Chronic Lung Diseases Using 3D Printed Phototunable Hydrogels
07:17

Author Spotlight: Understanding Chronic Lung Diseases Using 3D Printed Phototunable Hydrogels

Published on: June 30, 2023

1.7K

Related Experiment Videos

Last Updated: Jun 27, 2026

Printing Thermoresponsive Reverse Molds for the Creation of Patterned Two-component Hydrogels for 3D Cell Culture
10:49

Printing Thermoresponsive Reverse Molds for the Creation of Patterned Two-component Hydrogels for 3D Cell Culture

Published on: July 10, 2013

15.0K
Agarose Fluid Gels Formed by Shear Processing During Gelation for Suspended 3D Bioprinting
07:26

Agarose Fluid Gels Formed by Shear Processing During Gelation for Suspended 3D Bioprinting

Published on: May 26, 2023

2.3K
Author Spotlight: Understanding Chronic Lung Diseases Using 3D Printed Phototunable Hydrogels
07:17

Author Spotlight: Understanding Chronic Lung Diseases Using 3D Printed Phototunable Hydrogels

Published on: June 30, 2023

1.7K

Area of Science:

  • Materials Science and Engineering
  • Polymer Chemistry
  • Artificial Intelligence in Materials Science

Background:

  • Predicting rheological properties and composition of 3D-printed materials using artificial intelligence (AI) is crucial but lacks established models.
  • Polyacrylamide (PAA) hydrogels are widely used in 3D printing, requiring accurate control over their mechanical properties.

Purpose of the Study:

  • To train deep learning (DL) models for predicting rheological properties (storage and loss moduli) of 3D-printed PAA substrates.
  • To develop generative DL models for predicting material composition and 3D printing parameters for desired rheological outcomes.
  • To establish a bidirectional mapping between material composition, printing parameters, and rheological properties.

Main Methods:

  • A multilayer perceptron (MLP) was trained for multivariate regression to predict storage (G") and loss (G") moduli from seven gel constituent parameters.
  • Hyperparameter tuning for the MLP was performed using a grid-search algorithm with 10-fold cross-validation, achieving an R² value of 0.89.
  • Variational autoencoder (VAE) and conditional variational autoencoder (CVAE) models were employed to generate synthetic hydrogel compositions, validated against real data using Student's t-test and an autoencoder anomaly detector.

Main Results:

  • The MLP model successfully predicted G' and G" moduli with an R² of 0.89, demonstrating accurate input-output mapping.
  • Generative DL models produced synthetic data statistically indistinguishable from real hydrogel fabrication data.
  • No significant differences were found between the seven generated gel constituents and the real data, validating the generative approach.

Conclusions:

  • Trained deep learning models effectively predict rheological properties and constituent composition for 3D-printed PAA hydrogels.
  • The developed AI models enable precise control over material properties by mapping input-output relationships for 3D printing.
  • This work provides a foundation for designing custom 3D-printed hydrogels with specific rheological characteristics using AI.