表面技术Issue(10)：86-92,7.DOI:10.16490/j.cnki.issn.1001-3660.2015.10.015
基于不同优化算法的HT250基体再制造工艺参数优化
Process Parameter Optimization of Remanufactured HT250 Matrix Based on Different Optimization Algorithms
摘要
Abstract
Objective To investigate the optimization effect of the remanufacturing process parameters of the HT250 matrix un-der different optimization algorithms. Methods Experiments were designed using a factorial design based on a Taguchi L18 orthogo-nal array. The surface defects of HT250 substrate were repaired by sub laser instant cladding technology, and a hybrid method that included the response surface methodology ( RSM)-back propagation neural network ( BPNN)-integrated simulated annealing algo-rithm ( SAA) was proposed to search for an optimal parameter setting of the remanufactured HT250 matrix, and the effects of input power, processing time, velocity and gas flow on the tensile strength of the remanufactured sample were also analyzed in detail. In addition, the optimization results, stability and veracity were analyzed to compare the results of BPNN integrated SAA with that of the RSM approach. Results The optimal remanufactured HT250 matrix conditions were input power of 2960 W, processing time of 0. 6 s, speed of 6 mm/s, gas flow of 3 L/min. The maximum tensile strength of the remanufactured sample under these conditions was 230. 52 MPa. Conclusion The results showed that the tensile strength was significantly influenced by the input power P and single repair time t, while the influences of other factors were weak. The BPNN/SAA method was more effective than RSM for the optimization of remanufactured HT250 matrix.关键词
亚激光瞬间熔/再制造/抗拉强度/优化算法/响应曲面法/BP神经网络-模拟退火算法Key words
sub laser instant cladding/remanufacture/tensile strength/optimization algorithm/response surface methodolo-gy/back propagation neural network-integrated simulated annealing algorithm分类
矿业与冶金引用本文复制引用
赵运才,杨雷雷,刘宗阳..基于不同优化算法的HT250基体再制造工艺参数优化[J].表面技术,2015,(10):86-92,7.基金项目
国家自然科学基金(51565017) （51565017）
江西省教育厅科技计划项目(GJJ14424)Fund：Supported by the National Natural Science Foundation of China(51565017) and Science and Technology Project of Jiangxi Province Education Depart-ment(GJJ14424) （GJJ14424）
