Format

Send to

Choose Destination
Materials (Basel). 2019 Jun 13;12(12). pii: E1902. doi: 10.3390/ma12121902.

Study on ASJ Cutting of TC18, Based upon Multivariate Nonlinear Regression and SA-BP-AGA.

Author information

1
School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China. TS17130003A3@cumt.edu.cn.
2
School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China. jianniang_zhouxin@163.com.
3
School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China. cumthui@126.com.
4
Xuhai College, China University of Mining and Technology, Xuzhou 221116, China. cumthui@126.com.
5
School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China. wfc0317@163.com.
6
School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China. xqw0703@163.com.
7
School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China. cwguo@cumt.edu.cn.

Abstract

TC18 titanium alloy has been widely applied, but is considered as a difficult machining material. Taking the kerf angle as the quality criterion, this paper studied the cutting performance of TC18 by the use of an abrasive slurry jet (ASJ), based upon multivariate nonlinear regression and SA-BP-AGA. Cutting experiments were carried out according to the Taguchi orthogonal method. The experimental factors included traverse speed, standoff distance, pressure and slurry concentration, with five levels set, respectively. Meanwhile, a characterization method of the major influencing factors was proposed. A multiple nonlinear regression model and a back propagation artificial neural network (BP) prediction model, based on adaptive genetic algorithm (AGA), were established. The reliability was verified by statistics equations for the 22 groups of the fitting or training model and the three groups of experimental results. The BP-AGA and Simulated annealing algorithm (SA) were used to form a set of prediction optimization systems, called integrated SA-BP-AGA. Finally, the results showed that the main factor influencing the kerf angle is the slurry concentration. BP-AGA is easier to model, offers better robustness and is more accurate than a multivariate nonlinear regression model. The best kerf angle can be predicted by the integration system. The study results can improve the performance for the machining of TC18 by ASJ.

KEYWORDS:

ASJ cutting; SA-BP-AGA; TC18; retardation coefficient

PMID:
31200444
DOI:
10.3390/ma12121902
Free full text

Supplemental Content

Full text links

Icon for Multidisciplinary Digital Publishing Institute (MDPI)
Loading ...
Support Center