Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Response Surface Methodology

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

You might also read

Related Articles

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

Sort by

Same author

The sporting life: Athletic activities during early adolescence.

Journal of youth and adolescence·2013

Same author

Commentary: impact of disease activity at conception on disease activity during pregnancy in patients with inflammatory bowel disease--authors' reply.

Alimentary pharmacology & therapeutics·2013

Same author

Inflammatory lipid sphingosine-1-phosphate upregulates C-reactive protein via C/EBPβ and potentiates breast cancer progression.

Oncogene·2013

Same author

Meta-analysis: the impact of disease activity at conception on disease activity during pregnancy in patients with inflammatory bowel disease.

Alimentary pharmacology & therapeutics·2013

Same author

Surface roughness enhances the osseointegration of titanium headposts in non-human primates.

Journal of neuroscience methods·2012

Same author

miR-34a functions as a tumor suppressor modulating EGFR in glioblastoma multiforme.

Oncogene·2012

Same journal

Retracted: Diagnostic Efficacy of CT Radiomic Features in Pulmonary Invasive Mucinous Adenocarcinoma.

Scanning·2023

Same journal

Retracted: 3D Convolutional Neural Network Framework with Deep Learning for Nuclear Medicine.

Scanning·2023

Same journal

Retracted: Observation on the Effect of MRI Image Scanning on Knee Pain in Football Injury.

Scanning·2023

Same journal

Retracted: Optimal Cellular Microscopic Pattern Recognizer- (OCMPR-) Based Wireless Detection Network for Efficiently Leveraging the Parallel Distributed Processing Capabilities.

Scanning·2023

Same journal

Retracted: Changes of Volume Parameters in the Treatment of Graves Ophthalmopathy by Endoscopic Transethmoidal Decompression of the Orbital Inner Wall Combined with Fat Decompression.

Scanning·2023

Same journal

Retracted: Diagnostic Value of Specialist Systems in Sports Knee Injuries.

Scanning·2023

See all related articles

Search research articles

Related Experiment Video

Updated: May 13, 2026

Impact of Fabrication Techniques and Polishing Procedures on Surface Roughness of Denture Base Resins

Impact of Fabrication Techniques and Polishing Procedures on Surface Roughness of Denture Base Resins

Published on: January 17, 2025

Roughness parameter selection for novel manufacturing processes.

M Ham¹, B M Powers

¹Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario, Canada.

|March 6, 2013

Summary

This summary is machine-generated.

This study introduces a roughness parameter (RP) selection method for new manufacturing processes. It successfully reduced 18 RPs to 8 by grouping correlated parameters in single point incremental forming.

Keywords:

SPIF correlation forming roughness

More Related Videos

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

Convergent Polishing: A Simple, Rapid, Full Aperture Polishing Process of High Quality Optical Flats & Spheres

Convergent Polishing: A Simple, Rapid, Full Aperture Polishing Process of High Quality Optical Flats & Spheres

Published on: December 1, 2014

Related Experiment Videos

Last Updated: May 13, 2026

Impact of Fabrication Techniques and Polishing Procedures on Surface Roughness of Denture Base Resins

Impact of Fabrication Techniques and Polishing Procedures on Surface Roughness of Denture Base Resins

Published on: January 17, 2025

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

Convergent Polishing: A Simple, Rapid, Full Aperture Polishing Process of High Quality Optical Flats & Spheres

Convergent Polishing: A Simple, Rapid, Full Aperture Polishing Process of High Quality Optical Flats & Spheres

Published on: December 1, 2014

Area of Science:

Manufacturing Engineering
Materials Science
Process Optimization

Background:

Selecting critical roughness parameters (RPs) is challenging for novel manufacturing processes.
Existing knowledge gaps hinder efficient process characterization and optimization.

Purpose of the Study:

To develop a systematic method for roughness parameter selection in data-scarce manufacturing environments.
To identify a representative RP for highly correlated groups, simplifying analysis.

Main Methods:

A novel methodology for roughness parameter (RP) selection was proposed.
The method identifies and consolidates highly correlated RPs into a single representative parameter.
Single Point Incremental Forming (SPIF) was utilized as a case study for validation.

Main Results:

The proposed method successfully reduced the number of investigated RPs from 18 to 8.
This demonstrates the efficacy of the RP selection technique in simplifying complex parameter spaces.
The methodology proved effective for the single point incremental forming process.

Conclusions:

The developed method provides an efficient approach to roughness parameter selection for novel manufacturing processes.
Consolidating correlated RPs significantly reduces experimental workload and enhances process understanding.
This technique is valuable for optimizing processes with limited prior knowledge, like SPIF.