高级检索

基于PCA与RSM-DE算法的激光熔覆参数多目标优化

Multi-objective optimization of laser cladding parameters based on PCA and RSM-DE algorithm

  • 摘要: 为获得激光熔覆Inconel 718粉末在Q690高强钢板上的最优熔覆工艺参数,设计响应曲面法中的BBD(Box-Benhnken Design)试验设计模型. 构建输入变量(激光功率、扫描速度、送粉速率)与响应值(稀释率、热影响区深度、显微硬度)之间的数学模型,通过主成分分析法建立熔覆层综合评价指标,利用差分进化算法进行寻优,确定最优工艺参数. 采用最优工艺参数进行试验验证,对其最优工艺参数下试件的宏观形貌与组织形态进行观察与分析,并与优选出的试件进行响应值比较. 结果表明,最优工艺参数为激光功率1 800 W、扫描速度28 mm/s、送粉速率1.9 r/min,该参数下获得的热影响区深度为294 μm,稀释率为14.2%,显微硬度为276.6 HV0.5. 最优工艺参数下的试件热影响区深度减小了6.8%,稀释率降低了24.7%,显微硬度增大了21.7%,且最优试件中的组织形态为较小的树枝晶与少量的胞状晶.

     

    Abstract: The Box-Benhnken Design (BBD) experimental design model, as one of the response surface methods, was designed to obtain the optimal parameters for laser cladding of Inconel 718 powder on Q690 high-strength steel plate. A mathematical model between input variables (laser power, scanning speed, powder delivery rate) and response values (dilution rate, heat affected zone depth, microhardness) was established. Principal component analysis method was used to establish the comprehensive evaluation index of cladding layer, and differential evolution algorithm was used to optimize and determine the optimal process parameters. The optimal process parameters were used for test verification, and the macro-morphology and microstructure of the specimen under the optimal process parameters were observed and analyzed, and the response values were compared with the optimized specimen. The results showed that the optimal processing parameters were laser power of 1 800 W, scanning speed of 28 mm/s, and powder feeding rate of 1.9 r/min, under which the heat affected zone depth was 294 μm, the dilution rate was 14.2%, and the microhardness was 276.6 HV0.5. The optimum specimens showed a 6.8% reduction in the depth of the heat affected zone, a 24.7% reduction in dilution, and a 21.7% increase in microhardness, and the optimum specimens showed small dendritic crystals with a small amount of cellular crystals.

     

/

返回文章
返回