In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing

Selective laser melting (SLM) has shown unique advantages in fabricating metal components. However, the part quality still largely suffered from the porosity defects that are not easily detected and eliminated. In this work, the objective is to realize the porosity classification based on high-speed melt pool images. A coaxial high-speed in situ monitoring system was first developed to capture the melt pool images during the multi-track and multilayer printing process. Then, a novel image and data processing method was proposed to extract the critical and high-level melt pool features data. Three intelligent machine learning algorithms of back propagation neural network (BPNN), support vector machine (SVM), and deep belief network (DBN) were finally developed to match the features data with porosity modes. Results show that it is feasible and effective for the proposed method to realize porosity classification during the SLM process, which can provide a potential to reduce porosity defects.

Automated summary

The study achieved porosity classification based on high-speed melt pool images and developed corresponding intelligent machine learning algorithms. The results show that the proposed method can effectively classify porosity during the SLM process, potentially reducing porosity defects.