摘要
This paper describes the application of orthogonal test design coupled with non-linear regression analysis to optimize abrasive suspension jet （AS J） cutting process and construct its cutting model. Orthogonal test design is applied to cutting stainless steel. Through range analysis on experiment results, the optimal process conditions for the cutting depth and the kerr ratio of the bottom width to the top width can be determined. In addition, the analysis of ranges and variances are all employed to identify various factors： traverse rate, working pressure, nozzle diameter, standoff distance which denote the importance order of the cutting parameters affecting cutting depth and the kerf ratio of the bottom width to the top width. ~rthermore, non-linear regression analysis is used to establish the mathematical models of the cutting parameters based on the cutting depth and the kerr ratio. Finally, the verification experiments of cutting parameters＇ effect on cutting performance, which show that optimized cutting parameters and cutting model can significantly improve the prediction of the cutting ability and quality of ASJ.
This paper describes the application of orthogonal test design coupled with non-linear regression analysis to optimize abrasive suspension jet （AS J） cutting process and construct its cutting model. Orthogonal test design is applied to cutting stainless steel. Through range analysis on experiment results, the optimal process conditions for the cutting depth and the kerr ratio of the bottom width to the top width can be determined. In addition, the analysis of ranges and variances are all employed to identify various factors： traverse rate, working pressure, nozzle diameter, standoff distance which denote the importance order of the cutting parameters affecting cutting depth and the kerf ratio of the bottom width to the top width. ~rthermore, non-linear regression analysis is used to establish the mathematical models of the cutting parameters based on the cutting depth and the kerr ratio. Finally, the verification experiments of cutting parameters＇ effect on cutting performance, which show that optimized cutting parameters and cutting model can significantly improve the prediction of the cutting ability and quality of ASJ.
基金
supported by the Science and Technology Development Foundation of Shanghai Municipal Science and Technology Commission (Grant No.037252022)
