期刊
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
卷 119, 期 5-6, 页码 3407-3425出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00170-021-08133-9
关键词
Multi-axis grinding; Grinding wheel wear; Instantaneous engagement model; Grinding ratio
资金
- National Natural Science Foundations of China [51775445]
- Xi'an science and technology project [201805042YD20CG26-(9)]
- Natural Science Basic Research Plan in Shaanxi Province of China
- Fundamental Research Funds for the Central Universities [31020190503008]
This paper presents a grinding wheel wear prediction method for multi-axis grinding, which calculates the simplified engagements between the grinding wheel and the workpiece on virtual planes and obtains the spatial instantaneous engagements. It accurately calculates the abrasion loss of the grinding wheel and provides a basis for predicting the tool's time of failure.
Grinding is a precision machining method widely used in the precision manufacturing. Wears of the grinding wheel are common during the grinding process that would lead to the decrease of manufacturing precision. To improve grinding precision, a grinding wheel wear prediction method for multi-axis grinding is presented in this paper. Due to the complex shape of grinding wheel and the surface-to-grind, they are represented on a group of virtual planes. In terms of the kinematics of five-axis machine tool, the simplified engagements between the grinding wheel and the workpiece are calculated on these planes. By composing these scattered engagements, the spatial instantaneous engagements are obtained. Next, the material volume removed by the infinitesimal on the profile of the grinding wheel is calculated accurately. Inversely, the abrasion loss of each infinitesimal can be obtained based on the grinding ratio. The abrasion loss distribution is determined by composing all the infinitesimals on the wheel's profile. Finally, a free-form surface is ground by a cylindrical wheel to verify the proposed method. It shows that the wear prediction of the grinding wheel can provide a basis for predicting the tool's time of failure.
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