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

Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

4.3K
Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
4.3K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.7K
Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
2.7K

You might also read

Related Articles

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

Sort by
Same author

A coma pattern-based autofocusing method resolves bacterial cold shock response at single-cell level.

eLife·2026
Same author

Examining the Association Between Internet Addiction and Nonsuicidal Self-Injury Among Chinese Middle School Students: Prospective Cohort Study.

Journal of medical Internet research·2026
Same author

Development and validation of a machine learning model to predict prognostic outcomes in infantile epileptic spasms syndrome.

Frontiers in pediatrics·2026
Same author

Corrigendum to "The neural pathways and genetic substrates of non-suicidal self-injury as a 'sensation of pain' addiction in drug-naïve depressed adolescents" [Prog Neuropsychopharmacol Biol Psychiatry. 2026 Jan 2;144: 111597].

Progress in neuro-psychopharmacology & biological psychiatry·2026
Same author

Morphotropic Phase Boundary in Graft Poly(Vinylidene Fluoride-co-Tetrafluoroethylene) With High Curie Temperature.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Large piezoelectricity in crosslinked ferroelectric polymers.

Nature communications·2026

Related Experiment Video

Updated: Jan 10, 2026

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

13.0K

Dynamic Monitoring Method of Polymer Injection Molding Product Quality Based on Operating Condition Drift Detection

Guancheng Shen1,2, Sihong Li2, Yun Zhang2

  • 1Xi'an Modern Chemistry Research Institute, Xi'an 710065, China.

Polymers
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a drift-aware framework for polymer injection molding quality prediction. It enhances accuracy by detecting and adapting to changing operating conditions, improving process stability.

Keywords:
drift detectionincremental learninginjection moldingproduct quality

More Related Videos

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
06:59

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

Published on: March 1, 2019

8.3K
Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

406

Related Experiment Videos

Last Updated: Jan 10, 2026

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

13.0K
Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
06:59

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

Published on: March 1, 2019

8.3K
Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

406

Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence
  • Materials Science

Background:

  • Polymer injection molding quality prediction models degrade with changing operating conditions.
  • Variations in melting temperature, cooling, and machine parameters cause model drift.

Purpose of the Study:

  • To develop a drift-aware dynamic quality-monitoring framework for robust online prediction.
  • To address the challenge of prediction model degradation in evolving industrial environments.

Main Methods:

  • Integrated hybrid-feature autoencoder (HFAE) for drift detection.
  • Utilized sliding-window reconstruction error analysis.
  • Employed a mixed-feature artificial neural network (ANN) for online quality prediction.
  • Implemented an adaptive scheme with drift-event response and model updates.

Main Results:

  • Achieved a 35.4% increase in overall accuracy compared to static models.
  • Reduced root-mean-squared error by 42.3% after incremental updates.
  • Significantly decreased anomaly detection rate from 0.86 to 0.09.
  • Effectively narrowed the distribution gap between training and testing sets.

Conclusions:

  • The proposed framework maintains high-fidelity quality predictions under dynamic conditions.
  • Enables reduced rework, improved process stability, and lower sampling frequency in industrial production.
  • Demonstrates practical relevance for large-scale polymer injection molding.