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Research on calculation of grinding surface roughness
Author(s) -
Van Nga Tran Thi,
Khanh Nguyen Lam,
Nguyen Van Cuong
Publication year - 2022
Publication title -
eureka, physics and engineering./eureka, physics and engineering
Language(s) - English
Resource type - Journals
eISSN - 2461-4262
pISSN - 2461-4254
DOI - 10.21303/2461-4262.2022.001832
Subject(s) - grinding , abrasive , surface roughness , materials science , machining , surface finish , diamond grinding , lubrication , grinding wheel , metallurgy , mechanical engineering , composite material , engineering
In machining processes, grinding is often chosen as the final machining method. Grinding is often chosen as the final machining method. This process has many advantages such as high precision and low surface roughness. It depends on many parameters including grinding parameters, dressing parameters and lubrication conditions. In grinding, the surface roughness of a workpiece has a significant influence on quality of the part. This paper presents a study of the grinding surface roughness predictions of workpieces. Based on the previous studies, the study built a relationship between the abrasive grain tip radius and the Standard marking systems of the grinding wheel for conventional and superabrasive grinding wheels (diamond and CBN abrasive). Based on this, the grinding surface roughness was predicted. The proposed model was verified by comparing the predicted and experimental results. Appling the research results, the surface roughness when grinding three types of steel D3, A295M and SAE 420 with Al2O3 and CBN grinding wheels were predicted. The predicted surface roughness values were close to the experimental values, the average deviation between predictive results and experimental results is 15.11 % for the use of Al2O3 grinding wheels and 24.29 % for the case of using CBN grinding wheels. The results of the comparison between the predicted model and the experiment show that the method of surface roughness presented in this study can be used to predict surface roughness in each specific case. The proposed model was verified by comparing the predicted and measured results of surface hardness. This model can be used to predict the surface hardness when surface grinding

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