基于最大熵阈值分割算法的激光选区熔化过程溅射特征研究

Investigation of Spatter Characteristics in Selective Laser Melting Based on Maximum Entropy Threshold Segmentation Algorithm

激光选区熔化(SLM)加工过程中的溅射形态随工艺参数变化,难以实现所有工艺参数下的溅射提取。基于传统阈值分割的溅射提取方法仅支持部分工艺参数且没有误差分析工作,处理结果不能反映真实溅射状态。基于高速摄像机采集的SLM过程溅射图像,提出一种强鲁棒性图像处理方法对其进行提取和处理。图像处理方法包含五个步骤,其中阈值分割过程基于最大熵阈值分割算法完成。结果表明,该溅射图像处理方法可准确提取多工艺参数下的溅射信息。当激光功率在100~150 W范围时,溅射面积和数量的变化取决于粉末熔化状态;当激光功率在150~200 W范围时,溅射面积和数量的减少由溅射叠加导致。

The spatter morphology during selective laser melting(SLM)processing varies with process parameters,and it is difficult to achieve spatter extraction under all process parameters.The spatter extraction method based on traditional threshold segmentation only supports some process parameters and has no error analysis work,and the processing results cannot reflect the real spatter state.This paper proposes a robust image processing method to extract and process them based on the spatter images of the SLM process collected by highspeed cameras.The image processing method includes five steps,in which the threshold segmentation process depends on maximum entropy threshold segmentation algorithm.The results show that the spatter image processing method can accurately extract the spatter information under multiple process parameters.When the laser power is in the range from 100 W to 150 W,the change in the spatter area and number is determined by the molten state of the powder.And the reduction of spatter area and number is caused by spatter superposition when the laser power is in the range from 150 W to 200 W.