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

38
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...
38

You might also read

Related Articles

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

Sort by
Same author

Study on exploring the relationships between physiological indicators in near-death experiences by drawing on in-mold electronics and node displacement concepts in brain-computer interface signal transmission.

Scientific reports·2026
Same author

Fusion of NSGA-II and Latin hypercube sampling for optimizing node displacement in thin-film IME molding.

Scientific reports·2026
Same author

Multi-Objective Optimization of IME-Based Acoustic Tweezers for Mitigating Node Displacements.

Polymers·2025
Same author

Application of non-dominated sorting genetic algorithm (NSGA-III) and radial basis function (RBF) interpolation for mitigating node displacement in smart contact lenses.

Scientific reports·2024
Same author

Barrel rifling node offset detection and subsequent optimization based on thin film in-mold decoration characteristics of the Johnson-Cook model.

Scientific reports·2024
Same author

Enhancing Brain-Computer Interfaces through Kriging-Based Fusion of Sparse Regression Partial Differential Equations to Counter Injection Molding View of Node Displacement Effects.

Polymers·2024

Related Experiment Video

Updated: May 29, 2025

Synthesis of Soft Polysiloxane-urea Elastomers for Intraocular Lens Application
11:49

Synthesis of Soft Polysiloxane-urea Elastomers for Intraocular Lens Application

Published on: March 8, 2019

12.5K

Quality optimization of liquid silicon lenses based on sequential approximation optimization and radial basis

Hanjui Chang1,2, Shuzhou Lu3,4, Yue Sun3,4

  • 1Department of Mechanical Engineering, College of Engineering, Shantou University, Shantou, 515063, China. changhj@stu.edu.cn.

Scientific Reports
|February 3, 2025
PubMed
Summary

This study presents a new optimization method for liquid silicone optical lens injection molding. It significantly reduces defects like residual stress and shrinkage, improving quality and efficiency while lowering environmental impact.

Keywords:
Destructively measureLiquid optical silicone lensesMulti-objective optimizationRadial basis functionSequential approximate optimization

More Related Videos

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
05:57

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

8.0K
Selective Area Modification of Silicon Surface Wettability by Pulsed UV Laser Irradiation in Liquid Environment
08:48

Selective Area Modification of Silicon Surface Wettability by Pulsed UV Laser Irradiation in Liquid Environment

Published on: November 9, 2015

8.2K

Related Experiment Videos

Last Updated: May 29, 2025

Synthesis of Soft Polysiloxane-urea Elastomers for Intraocular Lens Application
11:49

Synthesis of Soft Polysiloxane-urea Elastomers for Intraocular Lens Application

Published on: March 8, 2019

12.5K
Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
05:57

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

8.0K
Selective Area Modification of Silicon Surface Wettability by Pulsed UV Laser Irradiation in Liquid Environment
08:48

Selective Area Modification of Silicon Surface Wettability by Pulsed UV Laser Irradiation in Liquid Environment

Published on: November 9, 2015

8.2K

Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Optimization Theory

Background:

  • Injection molding of liquid silicone optical lenses faces challenges with residual stress and volume shrinkage, impacting product quality and manufacturing efficiency.
  • Traditional optimization methods often involve extensive trial-and-error, leading to high costs and material waste.
  • Reducing the environmental footprint of manufacturing processes is a growing concern.

Purpose of the Study:

  • To develop and validate an innovative multi-objective optimization method for the injection molding of liquid silicone optical lenses.
  • To minimize residual stress and volume shrinkage in the manufactured lenses.
  • To enhance overall product quality, manufacturing efficiency, and process sustainability.

Main Methods:

  • Utilized sequential approximation optimization (SAO) combined with radial basis function (RBF) networks to create an approximate model of the injection molding process.
  • Replaced computationally expensive finite element reanalysis with RBF networks for faster functional relationship construction.
  • Simplified multi-objective optimization into a single-objective problem and employed Pareto boundary analysis for parameter tuning.

Main Results:

  • Achieved significant reductions in residual stress and volume shrinkage in liquid silicone optical lenses.
  • Identified optimal process parameters: filling time (1.57s), melt temperature (27.18°C), mold temperature (150°C), curing time (20.02s), and curing pressure (28.79 MPa).
  • Experimental validation using nondestructive testing confirmed the numerical results and the method's effectiveness.

Conclusions:

  • The proposed SAO-RBF optimization method offers a reliable and practical solution for improving injection molding processes.
  • The approach effectively reduces trial-and-error, material waste, and carbon emissions, contributing to cost efficiency and sustainability.
  • This method demonstrates broad applicability for optimizing various complex manufacturing processes.