In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing. (January 2022)

• Record Type: Journal Article
• Authors: Li, Jingchang; Cao, Longchao; Xu, Jie; Wang, Shengyi; Zhou, Qi
• Is Part Of: Measurement. Volume 187(2022)
• Publication Date: 2022-01
• Subjects: Selective laser melting -- Additive manufacturing -- Porosity classification -- Melt pool -- Machine learning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110232
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The melt pool images during the multi-track and multi-layer printing process are collected to realize porosity classification. A novel image and data processing method is proposed to deal with the huge amounts of melt pool image data. The extracted features can be effectively related to the different porosity modes. DBN has the best classification accuracy and the shortest classification time. Abstract: 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 multi-layer 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.