Analytical grinding force prediction with random abrasive grains of grinding wheels

Wu，Zhonghuai1,2; Zhang，Liangchi3,4,5

2023-07-15

10.1016/j.ijmecsci.2023.108310

INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES   影响因子和分区

0020-7403

1879-2162

A reliable prediction of grinding forces and surface morphology is critically important to the design of a grinding process. However, due to the complex microstructure of a grinding wheel which contains randomly-sized and randomly-distributed abrasive grains, a practical prediction model has been unavailable. This paper aims to develop a novel stochastic model to take into account the random nature of the abrasive grain size and their distribution in a grinding wheel, and hence to predict grinding forces more accurately. In addition, the evolution of the ground surface morphology, the real-time undeformed chip thickness, the interactions between the abrasive grains and the grain-workpiece contact mechanics are integrated into the modelling. Nanoindentation and tribology experiments were conducted to determine the micromechanical properties of the workpiece and abrasive grain. Grinding experiments were then performed to validate the predictions of the model. It was found, both theoretically and experimentally, that the stochastic model thus established can reliably predict grinding forces. It was also demonstrated that the proposed model can be effectively used to reveal the underlying mechanisms of grinding processes.

Force prediction Grinding Kinematic analysis Statistical model

SCI ; EI

英语

通讯

National Natural Science Foundation of China[52293401] ; Shenzhen Key Laboratory of Cross-scale Manufacturing Mechanics[ZDSYS20200810171201007]

Engineering ; Mechanics

Engineering, Mechanical ; Mechanics

WOS:000955924100001

PERGAMON-ELSEVIER SCIENCE LTD

20231213743911

Forecasting ; Grinding wheels ; Morphology ; Stochastic models ; Stochastic systems ; Surface morphology ; Tribology

Machining Operations:604.2 ; Control Systems:731.1 ; Probability Theory:922.1 ; Classical Physics; Quantum Theory; Relativity:931 ; Physical Properties of Gases, Liquids and Solids:931.2 ; Materials Science:951 ; Systems Science:961

ENGINEERING

2-s2.0-85150059964

Scopus

1.Beijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology,School of Optics and Photonics,Beijing Institute of Technology,Beijing,100081,China
2.MIIT Key Laboratory of Complex-Field Intelligent Exploration,Beijing Institute of Technology,Beijing,100081,China
3.Shenzhen Key Laboratory of Cross-scale Manufacturing Mechanics,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
4.SUSTech Institute for Manufacturing Innovation,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
5.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China

Wu，Zhonghuai,Zhang，Liangchi. Analytical grinding force prediction with random abrasive grains of grinding wheels[J]. INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES,2023,250.

Wu，Zhonghuai,&Zhang，Liangchi.(2023).Analytical grinding force prediction with random abrasive grains of grinding wheels.INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES,250.

Wu，Zhonghuai,et al."Analytical grinding force prediction with random abrasive grains of grinding wheels".INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 250(2023).