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,2015,44(10):86-92.
ZHAO Yun-cai,YANG Lei-lei,LIU Zong-yang.Process Parameter Optimization of Remanufactured HT250 Matrix Based on Different Optimization Algorithms[J].Surface Technology,2015,44(10):86-92 |
| 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 |
| 投稿时间：2015-07-17 修订日期：2015-10-20 |
| DOI：10.16490/j.cnki.issn.1001-3660.2015.10.015 |
| 中文关键词: 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 |
| 英文关键词:sub laser instant cladding remanufacture tensile strength optimization algorithm response surface methodology back propagation neural network-integrated simulated annealing algorithm |
| 基金项目:国家自然科学基金(51565017);江西省教育厅科技计划项目(GJJ14424) |
| 作者 | 单位 |
| 赵运才 | 江西理工大学 机电工程学院, 江西 赣州 341000 |
| 杨雷雷 | 江西理工大学 机电工程学院, 江西 赣州 341000 |
| 刘宗阳 | 江西理工大学 机电工程学院, 江西 赣州 341000 |
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| Author | Institution |
| ZHAO Yun-cai | School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China |
| YANG Lei-lei | School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China |
| LIU Zong-yang | School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China |
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| 中文摘要: |
| 目的 探讨不同优化算法下 HT250 基体再制造工艺参数的优化效果。 方法 利用 Taguchi 试验设计方法设计 4 因子 3 水平共 18 组试验,通过亚激光瞬间熔技术修复 HT250 基体的表面缺陷,利用响应曲面法(RSM)和 BP 神经网络-模拟退火算法(BPNN/ SAA)对其修复过程的工艺参数进行优化,分析输入功率 P,单次修复时间 t,速度 v 和保护气体流量 G 等 4 个因素对修复后试样抗拉强度的影响,并对不同优化算法的优化效果、准确性和稳定性进行探讨。 结果 HT250 基体修复过程中最优工艺参数为:输入功率 2960 W,持续时间 0. 62 s,速度 6 mm/ s,气体流量 3 L/ min。 在此参数下所获取的修复试样最大抗拉强度为 230. 52 MPa。 结论 抗拉强度受输入功率 P 和单次修复时间 t 影响显著,对其他元素呈弱依赖性。 BP 神经网络-模拟退火算法较响应曲面法更适合对亚激光瞬间熔的工艺参数进行优化。 |
| 英文摘要: |
| Objective To investigate the optimization effect of the remanufacturing process parameters of the HT250 matrix under different optimization algorithms. Methods Experiments were designed using a factorial design based on a Taguchi L18 orthogonal 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 algorithm (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. |
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