Search research articles

Related Concept Videos

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

A real-time detection framework for road cracks in noisy and morphologically complex environments.

Scientific reports·2026

Same author

Assessing surgical trauma in robot-assisted pelvic fracture fixation: the role of the systemic immune-inflammatory index.

Frontiers in surgery·2025

Same author

Comparative study of the efficacy and safety of remimazolam and midazolam for general anesthesia in elderly patients: a randomized controlled trial.

Perioperative medicine (London, England)·2025

Same author

Impact of Image Preprocessing and Crack Type Distribution on YOLOv8-Based Road Crack Detection.

Sensors (Basel, Switzerland)·2025

Same author

Challenges and solutions of deep learning-based automated liver segmentation: A systematic review.

Computers in biology and medicine·2024

Same author

A hybrid Transformer-LSTM model apply to glucose prediction.

PloS one·2024

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

Search research articles

Related Experiment Video

Updated: Sep 9, 2025

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

Adaptive force-position-speed collaborative process planning and roughness prediction for robotic polishing.

Ma Haohao^1,2, Azizan As'arry², Niu Jing¹

¹School of Mechatronics and Automotive Engineering, Tianshui Normal University, Tianshui, China.

|September 3, 2025

Summary

This study introduces an adaptive robot polishing framework for enhanced stability and precision. The new method significantly reduces surface roughness, improving efficiency in robotic material removal processes.

More Related Videos

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

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

Related Experiment Videos

Last Updated: Sep 9, 2025

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

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

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

Area of Science:

Robotics
Manufacturing Engineering
Materials Science

Background:

Robot polishing processes often lack stability and precision, particularly for complex geometries.
Optimizing process parameters like pressure, speed, and tool type is crucial for effective material removal.
Existing control strategies struggle to maintain consistent contact forces and accuracy during robotic polishing.

Purpose of the Study:

To develop an adaptive force-position-speed collaborative process planning framework for robot polishing.
To enhance the stability and accuracy of robot polishing operations.
To improve surface roughness and overall efficiency in robotic polishing applications.

Main Methods:

Developed a material removal model based on Preston's theory, incorporating polishing pressure, tool speed, feed speed, and sandpaper type.
Utilized an improved Dung Beetle Optimization algorithm, Back Propagation Neural Network, Finite Element Analysis, and Response Surface Methodology for parameter selection.
Implemented a curvature adaptive interpolation method for trajectory generation on curved workpieces and an adaptive impedance control strategy with PD iteration and RBF neural networks for force control.

Main Results:

Achieved a root mean square error (RMSE) accuracy of 0.0001 µm in the roughness prediction model.
Demonstrated enhanced stability in the proposed force control method.
Reduced average surface roughness by 20.79% compared to baseline methods.

Conclusions:

The proposed adaptive framework significantly improves the stability and precision of robot polishing.
The integration of advanced algorithms and control strategies leads to superior surface finish and efficiency.
This research validates the effectiveness of the developed framework for high-precision, high-efficiency robot polishing applications.