DING Xiaoyan,CHENG Jun,DING Delin.Optimization of Process Parameters of Electrodepositing Ni-TiB2 Composite Coating based on Neural Network and Genetic Algorithm[J].Plating & Finishing,2020,(11):20-24.[doi:10.3969/j.issn.1001-3849.2020.11.0050]
基于神经网络和遗传算法的电沉积Ni-TiB2复合镀层工艺参数优化
- Title:
- Optimization of Process Parameters of Electrodepositing Ni-TiB2 Composite Coating based on Neural Network and Genetic Algorithm
- 关键词:
- 工艺参数优化; 电沉积Ni-TiB2复合镀层; 硬度; 神经网络; 遗传算法
- 文献标志码:
- A
- 摘要:
- 以获得高硬度Ni-TiB2复合镀层为目标，将神经网络与遗传算法相结合对电沉积Ni-TiB2复合镀层工艺参数进行优化。选择超声波功率、颗粒添加量、镀液温度、镀液pH值和电流密度作为因素，并以硬度作为评价指标，采用L16（45）正交表安排实验获取了训练样本和检验样本。构建了5-7-1型神经网络，经过训练和验证后可以比较准确的表达工艺参数与硬度之间的关系。用遗传算法对训练后的神经网络进行寻优，搜索到的最优工艺参数为：超声波功率160 W、颗粒添加量5.0 g/L、镀液温度55 ℃、镀液pH值4.0、电流密度5 A/dm2。重复实验验证了寻优得到的工艺参数是准确的，采用寻优工艺参数获得的Ni-TiB2复合镀层晶粒更细小且均匀性较好，平均晶粒尺寸为14.6 nm，硬度达到495 HV。
- Abstract:
- In order to obtain the Ni-TiB2 composite coating with high hardness, the process parameters of electrodepositing Ni-TiB2 composite coating were optimized by combining neural network and genetic algorithm. The training and test samples were obtained by L16（45） orthogonal experiment taking ultrasonic power, addition amount of particles, plating bath temperature, plating bath pH value and current density as the factors and hardness as the evaluation index. A 5-7-1 typed neural network was constructed, which can accurately express the relationship between process parameters and hardness after training and verification. Genetic algorithm was used to optimize the neural network after training, and the optimal process parameters determined were as follows: ultrasonic power 160 W, addition amount of particles 5.0 g/L, bath temperature 55 ℃, bath pH value 4.0, current density 5 A/dm2. Repeated experiments verified that the optimized process parameters were accurate. The Ni-TiB2 composite coating obtained by using the optimized process parameters has finer grains with an average grain size of 14.6 nm, and exhibited better uniformity, its hardness reached to 495 HV.
参考文献/References:
[1] 王雪艳. 钛合金表面Ni-TiB2复合镀层的结构与性能[J]. 电镀与环保, 2019, 39（2）:1-3.
Wang X Y. Structure and properties of Ni-TiB2 composite coatings prepared on titanium alloy[J]. Electroplating & Pollution Control, 2019, 39（2）:1-3（in Chinese）.
[2] Gyawali G, Cho S H, Lee S W. Electrodeposition and characterization of Ni-TiB2 composite coatings[J]. Metals and Materials International, 2013, 19:113-118.
[3] 张仲伟, 曹雷, 陈希亮, 等. 基于神经网络的知识推理研究综述[J]. 计算机工程与应用, 2019, 55（12）:8-19.
Zhang Z W, Cao L, Chen X L, et al. Survey of knowledge reasoning based on neural network[J]. Computer Engineering and Applications, 2019, 55（12）:8-19（in Chinese）.
[4] 马永杰, 云文霞. 遗传算法研究进展[J]. 计算机应用研究, 2012, 29（4）:1201-1206.
Ma Y J, Yun W X. Research progress of genetic algorithm[J]. Application Research of Computers, 2012, 29（4）: 1201-1206（in Chinese）.
[5] 郭强, 郑燕萍, 朱伟庆, 等. 基于BP神经网络遗传算法的高强钢成形研究[J]. 材料科学与工艺, 2020, 28（2）:89-96.
Guo Q, Zheng Y P, Zhu WW Q, et al. Research on high strength steel forming based on BP neural network genetic algorithms[J]. Materials Science and Technology, 2020, 28（2）: 89-96（in Chinese）.
[6] Meiabadi M S, Vafaeesefat A, Sharifi F. Optimization of plastic injection molding process by combination of artificial neural network and genetic algorithm[J]. Journal of Optimization in Industrial Engineering, 2013, 13: 49-54.
[7] 唐静, 袁秀芝. BP神经网络结合遗传算法优化黄芪多糖提取工艺[J]. 中国医院药学杂志, 2018, 38（15）:1609-1611.
Tang J, Yuan X Z. Extraction optimization of astragalus polysaccharides with back-propagation neural networks and genetic algorithm[J]. Chinese Journal of Hospital Pharmacy, 2018, 38（15）:1609-1611（in Chinese）.
[8] 郭力, 邓喻. 采用遗传算法优化神经网络的铸铁表面粗糙度声发射预测[J]. 机械科学与技术, 2018, 37（10）:1512-1516.
Guo L, Deng Y. Acoustic emission monitor grinding surface roughness of cast iron via BP neural networks and genetic algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37（10）: 1512-1516（in Chinese）.
[9] 王俊平, 李加彦. BP神经网络的学习过程与算法分析[J]. 计算机光盘软件与应用, 2014, 4:241-243.
[10] 丁雨田, 王兴茂, 孟斌, 等. GH3625合金无缝管材组织及性能调控研究[J]. 稀有金属, 2019, 43（3）:274-282.
Ding Y T, Wang X M, Meng B, et al. Microstructures and properties of GH3625 alloy tubes in various states with solution treatment[J]. Chinese Journal of Rare Metals, 2019, 43（3）:274-282（in Chinese）.
相似文献/References:
[1]黄建娜*,王 璇,刘松林.钛合金Ti6Al4V表面纳米SiC增强Ni-Co基复合材料的制备工艺参数优化[J].电镀与精饰,2019,(11):18.[doi:10.3969/j.issn.1001-3849.2019.11.005]
HUANG Jianna*,WANG Xuan,LIU Songlin.Optimization of Process Parameters for Preparation of Nano-SiC Reinforced Ni-Co Based Composite Material on Surface of Titanium Alloy Ti6Al4V[J].Plating & Finishing,2019,(11):18.[doi:10.3969/j.issn.1001-3849.2019.11.005]
[2]彭娟,杜学铭,刘生发,等.IGBT铜焊盘化学镀Ni-Fe-P的制备及耐蚀性研究[J].电镀与精饰,2022,(4):11.[doi:10.3969/j.issn.1001-3849.2022.04.003]
PENG Juan,DU Xuemin,LIU Shengfa,et al.Preparation and Corrosion Resistance of Electroless Ni-Fe-P Coating on IGBT Copper Pads[J].Plating & Finishing,2022,(11):11.[doi:10.3969/j.issn.1001-3849.2022.04.003]
备注/Memo
收稿日期： 2020-05-29；修回日期： 2020-07-27
基金项目： 丁小艳，teacher_ding28@126.com