In situ capture of spatter signature of SLM process using maximum entropy double threshold image processing method based on genetic algorithm

Abstract

Although selective laser melting (SLM) has the advancements of fabricating complex geometric, multi-material and multi-functional structures, several defects still affect the process stability, in particular spatter. The formation of spatter in SLM depends on the process parameters, and these can potentially be used to tune the process to obtain better product quality. However, there is still a lack of efficient methods for processing spatter images to allow in situ detection of the onset of spatter. In this paper, an in situ monitoring method for acquiring spatter images in SLM was presented. A maximum-entropy double-threshold image processing algorithm based on genetic algorithm (MEDTIA-GA) was proposed to recognize spatter from images, and its results were compared with three conventional threshold segmentation methods: Otsu’s method, Triangle threshold segmentation algorithm, and K-means clustering algorithm. Results show that MEDTIA-GA method was able to eliminate three types of errors: noise sensitivity, spatter conglutination, and spatter omission. In addition, the average processing time of 37 ms for MEDTIA-GA method was far shorter than those for other three conventional threshold segmentation methods. Finally, the relationship between the spatter area as well as the number and the laser energy density were analyzed