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

267
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...
267

You might also read

Related Articles

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

Sort by
Same author

Geopolymer Materials for Additive Manufacturing: Chemical Stability, Leaching Behaviour, and Radiological Safety.

Materials (Basel, Switzerland)·2025
Same author

Influence of Anode Immersion Speed on Current and Power in Plasma Electrolytic Polishing.

Micromachines·2024
Same author

Plasma Electrolytic Polishing of Nitinol: Investigation of Functional Properties.

Materials (Basel, Switzerland)·2021
Same author

Investigation of Post-Processing of Additively Manufactured Nitinol Smart Springs with Plasma-Electrolytic Polishing.

Materials (Basel, Switzerland)·2021

Related Experiment Video

Updated: Jan 10, 2026

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

10.6K

A Hybrid Fuzzy-PSO Framework for Multi-Objective Optimization of Stereolithography Process Parameters.

Mohanned M H Al-Khafaji1, Abdulkader Ali Abdulkader Kadauw2,3, Mustafa Mohammed Abdulrazaq1

  • 1Collage of Production Engineering and Metallurgy, University of Technology-Iraq, Baghdad 10066, Iraq.

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

This study introduces a hybrid intelligent framework to optimize Stereolithography (SLA) 3D printing parameters for Acrylonitrile Butadiene Styrene (ABS) parts. The novel approach enhances part quality by accurately modeling and optimizing key performance characteristics.

Keywords:
Particle Swarm Optimization (PSO)Taguchi methodfuzzy logicmulti-objective optimizationstereolithography (SLA) 3D printer

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.4K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.0K

Related Experiment Videos

Last Updated: Jan 10, 2026

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

10.6K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.4K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.0K

Area of Science:

  • Materials Science and Engineering
  • Additive Manufacturing
  • Computational Intelligence

Background:

  • Additive manufacturing, particularly Stereolithography (SLA), is increasingly used for final part production.
  • Optimizing SLA process parameters is crucial for achieving desired material properties and surface finish.
  • Acrylonitrile Butadiene Styrene (ABS) photopolymers are common materials in SLA, requiring precise parameter control.

Purpose of the Study:

  • To develop a hybrid intelligent framework for modeling and optimizing SLA 3D printing parameters.
  • To investigate the complex, nonlinear relationships between SLA process parameters and performance characteristics.
  • To achieve multi-objective optimization for enhanced mechanical properties and surface finish of ABS parts.

Main Methods:

  • Employed a Taguchi design of experiments with an L18 orthogonal array for efficient experimental design.
  • Developed a novel hybrid fuzzy logic-Particle Swarm Optimization (PSO) algorithm, ARGOS, for automated fuzzy inference system (FIS) generation and tuning.
  • Utilized Modified Learn From Example (MLFE) for initial FIS creation, followed by PSO for predictive accuracy enhancement.

Main Results:

  • The ARGOS models demonstrated exceptional predictive accuracy, with correlation coefficients (R²) exceeding 0.9999 for all five output responses.
  • A multi-objective optimization using the weighted sum method identified optimal parameter settings for balancing key part qualities.
  • The proposed hybrid approach proved robust and highly accurate for modeling and optimizing the SLA 3D printing process.

Conclusions:

  • The hybrid intelligent framework effectively models and optimizes SLA 3D printing parameters for ABS parts.
  • The ARGOS algorithm offers a powerful tool for generating accurate Mamdani-type FISs from experimental data.
  • This research provides a valuable methodology for achieving high-quality 3D printed parts in real-world manufacturing applications.