Research Progress in Modelling of Surface Topography and Roughness Based on Grinding Mechanism

doi:10.3901/JME.2025.13.327

Abstract

Abstract: The surface topography and roughness of workpieces are critical metrics in grinding processes, with accurate prediction considered essential for advancing intelligent manufacturing. The generation of workpiece surfaces during grinding is recognized as a complex, stochastic process, and the accuracy of existing physics-based predictive models is deemed insufficient. A comprehensive review of predictive models and methodologies for workpiece surface topography is presented, with emphasis placed on geometric and kinematic aspects of grinding. Six geometric modeling approaches for abrasive grains, including the random plane method, are summarized, and the influence of abrasive grain parameters on model fidelity is examined. Mathematical models for the random distribution of abrasive grain positions and orientations on grinding wheel surfaces are reviewed, and the effects of model parameters on features such as protrusion height are analyzed. Methods for the fabrication and conditioning of grinding wheels with controlled abrasive grain arrangements are also discussed. Kinematic models of abrasive grains for various grinding processes, including plane and ultrasonic-assisted grinding, are analyzed. The interaction mechanisms between abrasive grains and the workpiece surface under different conditions are explored, and predictive models for surface roughness are generalized based on dynamic abrasive grain models. Finally, prediction errors of existing roughness models are statistically analyzed, with error ranges identified from 4.47% to 37.65%, and an average error of 11.59% determined. New perspectives for improving the prediction of grinding surface topography and roughness are proposed, offering references for the development of intelligent predictive methods integrating grinding mechanisms with data analysis.

Key words: grinding, wheel modelling, motion trajectory, contact model, workpiece morphology, surface roughness prediction

CLC Number:

TG580

PENG Fei, ZHANG Yanbin, CUI Xin, LIU Mingzheng, LIANG Xiaoliang, XU Peiming, ZHOU Zongming, LI Changhe. Research Progress in Modelling of Surface Topography and Roughness Based on Grinding Mechanism[J]. Journal of Mechanical Engineering, 2025, 61(13): 327-359.

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