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| 基于PSO–BP–GA混合算法的激光熔覆工艺多目标优化 |
| Multi-objective Optimization of Laser Cladding Process Based on PSO-BP-GA Hybrid Algorithm |
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| DOI：10.3969/j.issn.1674-6457.2023.07.023 |
| 中文关键词: 激光熔覆 工艺参数优化 PSO–BP神经网络 GA算法 |
| 英文关键词: laser cladding process parameter optimization PSO-BP neural network genetic algorithm |
| 基金项目:川大–泸州战略合作资金（2021CDLZ–2）；国家自然科学基金（51505268）；陕西省教育厅重点科研计划（20JS020）；陕西省教育厅一般专项科研计划（22JK0312） |
| Author Name | Affiliation | | HONG Zhen | Luzhou Vocational & Technical College, Sichuan Luzhou 646000, China | | ZHANG Wei-bo | School of Mechanical Engineering,Shaanxi University of Technology, Shaanxi Hanzhong 723001, China | | GONG Jiang-tao | School of Mechanical Engineering,Shaanxi University of Technology, Shaanxi Hanzhong 723001, China | | SHU Lin-sen | School of Mechanical Engineering,Shaanxi University of Technology, Shaanxi Hanzhong 723001, China
Shaanxi Key Laboratory of Industrial Automation, Shaanxi Hanzhong 723001, China |
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| 中文摘要: |
| 目的 为了提高镍基高温合金熔覆涂层的综合质量，提出了一种基于PSO–BP–GA混合算法的激光熔覆工艺优化方法。方法 选取工艺参数（激光功率、扫描速度、送粉速率）为优化变量、熔覆层质量（稀释率、显微硬度、热影响区深度）为优化目标，根据正交试验结果建立PSO–BP神经网络预测模型，采用线性加权法和层次分析法建立熔覆层质量的综合评价体系，结合GA算法探寻综合质量最优的工艺参数组合。结果 PSO–BP神经网络模型预测值与试验值之间的相对误差不超过6%，最优工艺参数组合如下：激光功率为2 158 W、扫描速度为10.4 mm/s、送粉速率为2.9 r/min，其熔覆层稀释率降低了70.4%、显微硬度增大了25.4%、热影响区深度减少了41.8%。结论 该算法为制备出高性能镍基高温合金熔覆涂层提供了一定的参考与借鉴。 |
| 英文摘要: |
| The work aims to propose a laser cladding process optimization method based on PSO-BP-GA hybrid algorithm to improve the quality of nickel-based alloy coatings. Process parameters (laser power, scanning speed, powder feeding rate) were selected as the optimization variables and the cladding layer quality (dilution rate, microhardness, heat affected zone depth) was selected as the optimization objective. According to the orthogonal test results, a PSO-BP neural network prediction model was established, and the comprehensive evaluation system of cladding layer quality was established through the linear weighting method and the analytic hierarchy process (AHP), and the process parameter combination when the comprehensive quality was optimal was explored in combination with the genetic algorithm (GA). The results showed that the relative error between the predicted value of PSO-BP neural network model and the test value was not more than 6%. The optimal combination of process parameters was laser power 2 158 W, scanning speed 10.4 mm/s, powder feeding rate 2.9 r/min. For the cladding layer obtained, the dilution rate was reduced by 70.4%. The microhardness increased by 25.4%, and the depth of heat affected zone decreased by 41.8%. This algorithm provides a certain reference for preparation of high performance nickel based superalloy cladding coatings on mechanical parts. |
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