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

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.

[1]  Zesheng Lu.  PREDICTIVE MODELING OF SURFACE ROUGHNESS AND CUTTING PARAMETERS OPTIMIZATION IN ULTRA-PRECISION TURNING BASED ON GENETIC ALGORITHM , 2005 .

[2]  Alok K. Verma,et al.  Parametric Study of Surface Finish in End Milling Using Robust Design Techniques , 2003 .

[3]  Seong-Jun Kim,et al.  A Review on the Taguchi Method and Its Alternatives for Dynamic Robust Design , 2013 .

[4]  Yang Liu,et al.  Analysis of milling surface roughness prediction for thin-walled parts with curved surface , 2017 .

[5]  Amit Banerjee,et al.  Surface roughness prediction as a classification problem using support vector machine , 2017 .

[6]  Jenq-Shyong Chen,et al.  A study of the surface scallop generating mechanism in the ball-end milling process , 2005 .

[7]  Chih-Cherng Chen,et al.  Experimental investigation of tool vibration and surface roughness in the precision end-milling process using the singular spectrum analysis , 2012 .

[8]  Jacob Chen,et al.  A Fuzzy-Net-Based Multilevel In-Process Surface Roughness Recognition System in Milling Operations , 2001 .

[9]  Xiaomei Xu,et al.  Multi-objective optimal design of sandwich panels using a genetic algorithm , 2017 .

[10]  S. Kalidass,et al.  Prediction of Surface Roughness for AISI 304 Steel with Solid Carbide Tools in End Milling Process Using Regression and ANN Models , 2014 .

[11]  C. N. Chu,et al.  Texture prediction of milled surfaces using texture superposition method , 1999, Comput. Aided Des..

[12]  W. Ding,et al.  Prediction of grinding temperature of PTMCs based on the varied coefficients of friction in conventional-speed and high-speed surface grinding , 2017 .

[13]  Lieh-Dai Yang,et al.  Fuzzy-nets-based in-process surface roughness adaptive control system in end-milling operations , 2006 .

[14]  S. G. Deshmukh,et al.  A genetic algorithmic approach for optimization of surface roughness prediction model , 2002 .