SLM成型件表面球化程度表征方法及等级检测

球化现象是选区激光熔化(SLM)成型过程中最常发生的缺陷,同时影响了最终成型部件的疲劳寿命和物理性能。合理控制部件成型过程中球化现象的发生,对维持成型过程的持续进行以及获得高质量的成型部件具有重大意义。本文在研究SLM成型过程中部件表面球化特征提取方法的基础上提出了球化程度表征方法,并通过正交实验验证了球化程度表征方法的有效性,建立了球化程度与激光能量密度之间的关联关系。同时对球化程度等级做了界定,最终构建了深度卷积神经网络(CNN)模型自动识别部件表面球化程度等级,以辅助实验及生产人员做出相应的决策。模型识别结果表明,在小的图像分割集上,网络识别精度达到了964,当在所采集的全局显微图像集上,其识别精度达到了100,取得了良好的识别效果。本研究将为SLM成型过程中成型质量的实时控制提供有效实现途径。

Characterization method and grade detection of surface balling degree of SLM formed parts

Balling phenomenon is the most common defect in selective laser melting(SLM) forming process,which affects the fatigue life and physical properties of the final formed part. Reasonable control of the occurrence of balling phenomenon is of great significance to maintain the continuous forming process and obtain high quality formed parts. In this paper,a balling degree characterization method was proposed based on the study of balling feature extraction method for parts surface during SLM forming process. The effectiveness of the method through orthogonal experiments is verified,and the correlation between balling degree and laser power density was also established. Moreover,the grade of balling degree was defined,and finally a deep convolutional neural network(CNN) model was constructed to automatically identify the grade of balling degree of the part surface to assist the experiment and production personnel to make corresponding decisions. The recognition results showed that the network recognition accuracy reaches 96.4% on the small segmentation image set,and the recognition accuracy reaches 100% on the global microscopic image set,which achieved a good recognition effect. This paper will provide an effective way to realize real time control of forming quality in the SLM forming process.