Minimum Surface Roughness Prediction of Grinding SKD11 Steel with the Response Surface Methodology
Received: 2 January 2025 | Revised: 23 January 2025 | Accepted: 5 February 2025 | Online: 3 April 2025
Corresponding author: Van-Long Trinh
Abstract
Surface roughness is a very important technical index of mechanical components that meets requirements for improving performance efficiency and reducing corrosion behavior in its working environment. Surface roughness is generally achieved by cutting methods using machine tools and cutting tools. The grinding process is a special finishing method using thousands of abrasives bonded together to form a grinding wheel for the finishing process with cost-effectiveness, productivity, and high quality. By controlling technological parameters, the grinding process produces products with high-quality of the roughness surfaces. However, it is difficult to compose a set of processing parameters for processing a hardened steel material such as the grinding process. This paper demonstrates a method of enhancing the quality of the surface roughness of SKD11 engineering steel material by controlling the grinding process parameters. The optimal grinding condition is conducted by using response surface methodology to analyze practical experiments designed by the Box-Behnken method to minimize the surface roughness of the final product during the grinding process. The results show that the prediction model of the surface roughness can be achieved with the minimum surface roughness of Ra of 0.591 µm and the processing parameters kit of v of 5 m/min, s of 3 mm/stroke, and t of 0.013 mm, respectively, via using the design of experimental method and the regression analysis law. The research hopes that the results will be referenced in the development of manufacturing technology for products that require high-quality index of surface roughness in the near future.
Keywords:
grinding process, machining parameters, surface roughness, optimization, finishing machiningDownloads
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Copyright (c) 2025 Van-Long Trinh, Ngoc-Tan Tran, Duy-Trinh Nguyen
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