摘要
建立了Co基硬质合金激光熔覆工艺优化的BP人工神经网络模型,应用该模型对熔覆粉末中TiC百分含量和熔覆工艺参数对硬质合金熔覆层质量的影响进行建模学习训练,成功地预测了熔覆工艺参数对其熔覆层显微硬度和气孔数的影响。当激光功率一定时,熔覆层的显微硬度随扫描速度的增加而增大;激光光斑为2.5mm×6mm的椭圆光斑。在激光输出功率为2900W、扫描速度为18mm/s的优化实验条件下,所得到的Co基硬质合金熔覆层平均显微硬度高达HV0.21197且具有较少的气孔缺陷。结果表明,所建模型有利于Co基硬质合金粉末成分设计和工艺参数优化。
The model predicted the influence of TiC proportion in the coating powder and the laser cladding parameters on the coating quality. The results indicate that microhardness value of the coating increase with the increment of scanning speed under a certain laser power; Co-based hard alloy coating with the average microhardness of HV0.21197 and less porosity was made under the optimizing condition that laser power is 2900 W, and scanning speed is 18 mm/s when the laser beam size is 2.5 mm×6 mm. The results show that the model can posses the ability to design the composition proportion of the coating powder, and optimize the laser cladding parameters.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2004年第3期352-355,共4页
Journal of Optoelectronics·Laser
基金
国家自然科学基金资助项目(50271028)
