Theoretical modelling of surface roughness in robotic abrasive disc grinding

Robotic abrasive disc grinding is widely used for surface finishing of structural components in industries such as aerospace and automotive manufacturing, where controlling surface roughness is critical for ensuring product quality and performance. Surface roughness theoretical models play a foundational role in deepening the understanding of surface profile formation and optimizing machining processes. Despite studies in wheel and belt grinding, abrasive disc grinding-specific models are still comparatively rare. This paper addresses this gap by proposing a novel theoretical model that integrates disc machining theory with key process parameters—rotation speed, grinding force, and feed rate—to establish a quantitative relationship with surface roughness. By analysing the material removal process and surface formation mechanism, the model estimates surface roughness from both kinematic and geometric perspectives. The proposed model is validated through robotic abrasive disc grinding experiments conducted under various conditions, achieving an average absolute relative error of 7.3%, with most deviations below 10%. This theoretical model offers physical insights into the grinding process. In the future, the model could be combined with data-driven approaches to enhance generalization and accuracy, leveraging its interpretability to improve predictive performance.

Authors

Institutions

Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science
Published
2025-11-05
DOI
https://doi.org/10.1177/09544062251384617
Primary Topic
Advanced Surface Polishing Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Theoretical modelling of surface roughness in robotic abrasive disc grinding

Hongcai Chen, Kanjian Zhang, Haikun Wei, Zewen Hu
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science
Advanced Surface Polishing Techniques
article

Theoretical modelling of surface roughness in robotic abrasive disc grinding

Hongcai Chen, Kanjian Zhang, Haikun Wei, Zewen Hu
article en

Abstract

Robotic abrasive disc grinding is widely used for surface finishing of structural components in industries such as aerospace and automotive manufacturing, where controlling surface roughness is critical for ensuring product quality and performance. Surface roughness theoretical models play a foundational role in deepening the understanding of surface profile formation and optimizing machining processes. Despite studies in wheel and belt grinding, abrasive disc grinding-specific models are still comparatively rare. This paper addresses this gap by proposing a novel theoretical model that integrates disc machining theory with key process parameters—rotation speed, grinding force, and feed rate—to establish a quantitative relationship with surface roughness. By analysing the material removal process and surface formation mechanism, the model estimates surface roughness from both kinematic and geometric perspectives. The proposed model is validated through robotic abrasive disc grinding experiments conducted under various conditions, achieving an average absolute relative error of 7.3%, with most deviations below 10%. This theoretical model offers physical insights into the grinding process. In the future, the model could be combined with data-driven approaches to enhance generalization and accuracy, leveraging its interpretability to improve predictive performance.

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering ScienceVol. 240(2)
Southeast University (CN)
Openalex Percentile: Top 68%
Advanced Surface Polishing Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.