Journal
MEASUREMENT
Volume 187, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2021.110232
Keywords
Selective laser melting; Additive manufacturing; Porosity classification; Melt pool; Machine learning
Funding
- National Natural Science Foundation of China (NSFC) [51805179]
- China Postdoctoral Science Foundation [2020M682397, 2020M682396]
- National Defense Innovation Program [18-163-00-TS-004-033-01]
- Research Funds of the Maritime Defense Technologies Innovation
Ask authors/readers for more resources
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.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available