Next Article in Journal
Microbial Fuel Cells, Related Technologies, and Their Applications
Next Article in Special Issue
Superior Mechanical Behavior and Fretting Wear Resistance of 3D-Printed Inconel 625 Superalloy
Previous Article in Journal
Using the Taguchi-Genetic Algorithm to Improve Lithographic Photoresist Operating Conditions of Touch Panels to Upgrade After-Develop Inspection
Previous Article in Special Issue
Design & Manufacture of a High-Performance Bicycle Crank by Additive Manufacturing
Open AccessArticle

Online Monitoring Based on Temperature Field Features and Prediction Model for Selective Laser Sintering Process

1
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH 45220, USA
3
Beijing Materials Handling Research Institute Co., LTD, Beijing 100007, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2383; https://doi.org/10.3390/app8122383
Received: 13 November 2018 / Revised: 20 November 2018 / Accepted: 22 November 2018 / Published: 25 November 2018
(This article belongs to the Special Issue 3D Printing of Metals)
Selective laser sintering (SLS) is an additive manufacturing technology that can work with a variety of metal materials, and has been widely employed in many applications. The establishment of a data correlation model through the analysis of temperature field images is a recognized research method to realize the monitoring and quality control of the SLS process. In this paper, the key features of the temperature field in the process are extracted from three levels, and the mathematical model and data structure of the key features are constructed. Feature extraction, dimensional reduction, and parameter optimization are realized based on principal component analysis (PCA) and support vector machine (SVM), and the prediction model is built and optimized. Finally, the feasibility of the proposed algorithms and model is verified by experiments. View Full-Text
Keywords: temperature field; support vector machine (SVM); process monitoring; quality prediction; selective laser sintering (SLS) temperature field; support vector machine (SVM); process monitoring; quality prediction; selective laser sintering (SLS)
Show Figures

Figure 1

MDPI and ACS Style

Chen, Z.; Zong, X.; Shi, J.; Zhang, X. Online Monitoring Based on Temperature Field Features and Prediction Model for Selective Laser Sintering Process. Appl. Sci. 2018, 8, 2383.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop