Prediction of Surface Roughness and Optimization of Cutting Parameters of Stainless Steel Turning Based on RSM

Abstract

The turning test of stainless steel was carried out by using the central composite surface design of response surface method (RSM) and Taguchi design method of central combination design. The influence of cutting parameters (cutting speed, feed rate, and cutting depth) on the surface roughness was analyzed. The surface roughness prediction model was established based on the second-order RSM. According to the test results, the regression coefficient was estimated by the least square method, and the regression equation was curve fitted. Meanwhile, the significance analysis was conducted to test the fitting degree and response surface design and analysis, in addition to establishing a response surface map and three-dimensional surface map. The life of the machining tool was analyzed based on the optimized parameters. The results show that the influence of feed rate on the surface roughness is very significant. Cutting depth is the second, and the influence of cutting speed is the least. Therefore, the cutting parameters are optimized and tool life is analyzed to realize the efficient and economical cutting of difficult-to-process materials under the premise of ensuring the processing quality.