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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

123
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
123
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

110
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
110
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

262
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
262

You might also read

Related Articles

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

Sort by
Same author

Design and Synthesis of P(AAm-co-NaAMPS)-Alginate-Xanthan Hydrogels and the Study of Their Mechanical and Rheological Properties in Artificial Vascular Graft Applications.

Gels (Basel, Switzerland)·2024
Same author

A Novel Design Method of an Evolutionary Mold Cooling Channel Using Biomimetic Engineering.

Polymers·2023
Same author

Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding.

Polymers·2022
Same author

Causes of the Gloss Transition Defect on High-Gloss Injection-Molded Surfaces.

Polymers·2020

Related Experiment Video

Updated: Oct 17, 2025

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars
05:32

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars

Published on: August 4, 2018

12.8K

Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine

Jinsu Gim1, Byungohk Rhee2

  • 1Department of Chemical Engineering, Hanyang University, 55 Hanyangdeahak-ro, Ansan 15588, Korea.

Polymers
|October 13, 2021
PubMed
Summary

This study introduces a new method to analyze how cavity pressure profiles affect plastic part quality. It uses neural networks to link pressure data to part weight, aiding injection molding process optimization.

Keywords:
cavity pressureinjection moldinginterpretable machine learning

More Related Videos

Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding
10:43

Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding

Published on: June 27, 2014

20.1K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.3K

Related Experiment Videos

Last Updated: Oct 17, 2025

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars
05:32

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars

Published on: August 4, 2018

12.8K
Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding
10:43

Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding

Published on: June 27, 2014

20.1K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.3K

Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Data Science

Background:

  • Part quality in injection molding is critically dependent on the cavity pressure profile.
  • Understanding this relationship requires expertise in both the injection molding process and polymer behavior.

Purpose of the Study:

  • To propose a novel methodology for analyzing the influence of cavity pressure profiles on part quality.
  • To establish a framework for interpreting neural network models as metamodels for this analysis.

Main Methods:

  • Utilized a neural network as a metamodel to represent the relationship between cavity pressure profiles and part weight.
  • Extracted Process State Points (PSPs) from cavity pressure data as input features for the neural network.
  • Analyzed the overall impact and specific contributions of these features on part weight.

Main Results:

  • The methodology successfully clarified the influence of the cavity pressure profile on part weight.
  • The impact of process parameters on part weight and PSPs validated the proposed analysis method.
  • Identified influential features and their impacts within the cavity pressure profile.

Conclusions:

  • The developed methodology provides a clear understanding of how cavity pressure profiles affect part quality.
  • Insights gained can be used to define monitoring windows and optimize injection molding processes.
  • The contribution analysis of features enables targeted process improvements for enhanced part quality.