李赞,张长胜,马涛,王卓.基于CGSOA-BPNN优化AlCoCrNiFe高熵合金涂层等离子喷涂工艺参数[J].表面技术,2022,51(1):311-324.
LI Zan,ZHANG Chang-sheng,MA Tao,WANG Zhuo.Optimization of Plasma Spraying Process Parameters of AlCoCrNiFe High Entropy Alloy Coating Based on CGSOA-BPNN[J].Surface Technology,2022,51(1):311-324
基于CGSOA-BPNN优化AlCoCrNiFe高熵合金涂层等离子喷涂工艺参数
Optimization of Plasma Spraying Process Parameters of AlCoCrNiFe High Entropy Alloy Coating Based on CGSOA-BPNN
投稿时间:2021-04-22  修订日期:2021-10-12
DOI:10.16490/j.cnki.issn.1001-3660.2022.01.034
中文关键词:  等离子喷涂  高熵合金涂层  工艺参数优化  BP神经网络  海鸥优化算法  改进logistic混沌  高斯变异
英文关键词:plasma spraying  high entropy alloy coating  process parameter optimization  BP neural network  seagull optimization algorithm  improved logistic chaos  gaussian mutation
基金项目:国家自然科学基金(61963022,51665025)
作者单位
李赞 昆明理工大学 信息工程与自动化学院,昆明 650093 
张长胜 昆明理工大学 信息工程与自动化学院,昆明 650093 
马涛 昆明理工大学 材料科学与工程学院,昆明 650093 
王卓 昆明理工大学 信息工程与自动化学院,昆明 650093 
AuthorInstitution
LI Zan Faculty of Information Engineering and Automation, Kunming 650093, China 
ZHANG Chang-sheng Faculty of Information Engineering and Automation, Kunming 650093, China 
MA Tao Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China 
WANG Zhuo Faculty of Information Engineering and Automation, Kunming 650093, China 
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中文摘要:
      目的 解决等离子喷涂工艺参数耦合导致的参数选取困难问题,提高AlCoCrNiFe高熵合金涂层力学性能。方法 提出全局混沌高斯融合的海鸥算法(CGSOA),优化权值和阈值,使BP(Back Propagation, 反向传递)神经网络训练输出理想控制参数。利用改进logistic混沌序列实现网络参数初始化种群的全局搜索,提高权值和阈值初始质量。引入改进logistic映射跳出局部最优,通过加强局部搜索能力,以提高算法收敛精度。引入高斯变异增加种群多样性,提高全局搜索能力。选取6个基准函数,对BAS、PSO、ACO、SOA及CGSOA算法进行测试,仿真结果表明,所提算法具有较快收敛速度、较高寻优精度和稳定性。结果 CGSOA算法优化BP神经网络得出最佳控制量为:喷涂距离99.7 mm,喷涂电流649.6 A,喷涂电压56.3 V,送粉载气203.1 L/h,送粉电压5.1 V。以其进行喷涂试验,涂层结合强度和显微硬度分别为25.2 MPa和616.8HV,与模型预测值的相对误差分别为3.02%和2.91%,验证了CGSOA-BPNN应用到实际喷涂过程的可行性。结论 CGSOA-BPNN对AlCoCrNiFe高熵合金涂层等离子喷涂工艺参数进行优化,进而提高涂层力学性能,具有一定的现实指导意义。
英文摘要:
      The aims is to solve the problem of difficult parameter selection caused by the coupling of plasma spraying process parameters to improve the mechanical properties of the AlCoCrNiFe high entropy alloy coating. An algorithm which seagull optimization algorithm based on global chaotic and Gaussian fusion (CGSOA) is proposed to optimize the weights and thresholds so that BP (Back Propagation) neural network training outputs ideal control parameters. The improved logistic chaotic sequence is used to realize the global search of the initial population of network parameters, and the initial quality of weights and thresholds is improved; the improved logistic mapping is introduced to jump out of the local optimum, and the local search capability is strengthened to improve the accuracy of the algorithm convergence; the introduction of Gaussian mutation increases the diversity of the population, improve the global search capability; select 6 benchmark functions to test the BAS, PSO, ACO, SOA and CGSOA algorithms. The simulation results show that the proposed algorithm had faster convergence speed, higher optimization accuracy and stability. CGSOA algorithm optimizes the BP neural network to obtain the best control amount:spraying distance 99.7 mm, spraying current 649.6 A, spraying voltage 56.3 V, powder feeding carrier gas 203.1 L/h, powder feeding voltage 5.1 V. The spraying test with this parameter shows that the bonding strength and microhardness of the coating were 25.2 MPa and 616.8HV, respectively, and the relative errors with the predicted value of the model were 3.02% and 2.91%, respectively. This result verifies the feasibility of applying the CGSOA algorithm to actual projects. CGSOA-BPNN has a certain guiding significance for optimizing the plasma spraying process parameters of AlCoCrNiFe high-entropy alloy coating, thereby improving the coating performance.
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