期刊
APPLIED SCIENCES-BASEL
卷 8, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/app8122383
关键词
temperature field; support vector machine (SVM); process monitoring; quality prediction; selective laser sintering (SLS)
类别
资金
- National Natural Science Foundation of China [61803023]
- Fundamental Research Funds for the Central Universities [FRF-TP-16-005A1]
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.
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