Abstract
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.
Highlights
Selective laser sintering (SLS) is a powder bed-based additive manufacturing where parts are made directly from three-dimensional CAD data layer-by-layer from the fusion of powder materials [1].SLS can be used to process a variety of metal materials, and the parts usually have good dimensional accuracy and surface quality [2]
In the additive manufacturing processes of powder bed fusion, the temperature of the work space is determined by and reflects the actual values of process parameters, which will result in different quality
To predict the quality of parts based on the temperature field information in SLS, the concept of the temperature field’s key features is proposed in this paper
Summary
Selective laser sintering (SLS) is a powder bed-based additive manufacturing where parts are made directly from three-dimensional CAD data layer-by-layer from the fusion of powder materials [1]. By analyzing the temperature field images, the incidence relationship between the temperature field and the states of powder coating and melting pool is established to realize the monitoring and quality control of the SLS process [3,4]. The Doublenskaia team proposed using an infrared thermal imager to acquire image data in the additive manufacturing process, and analyzed the temperature change with time, the state of laser-powder interaction area, and the change of sputtering radiation, and emphasized the importance of analyzing global temperature data in both time and space dimensions to identify unstable factors in the process [5,6]. The model is trained based on a large amount of experimental data, and is verified by more experiments
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