A prediction approach of SLM based on the ensemble of metamodels considering material efficiency, energy consumption, and tensile strength

As a rapid developing additive manufacturing (AM) technology, selective laser melting (SLM) provides a promising way for intelligent manufacturing. The SLM part quality depends largely on the process parameters in the manufacturing process. Therefore, understanding the relationships between the input process parameters and the output part performances is critical to improve the part quality. In this work, the ensemble of metamodels (EM) is adopted and an adaptive hybrid leave-one-out error-based EM (EM-AHL) is developed to predict the powder utilization rate, the energy consumption, and the tensile strength of the as-built parts. First, the Taguchi experiment design is applied to obtain the sample points and the corresponding SLM experiments are conducted to get the experimental results. Second, the correlations between the process parameters (i.e., laser power, layer thickness, scanning speed) and the three responses are fitted using the proposed EM-AHL, which is constructed by aggregating three metamodels, Kriging, Radial basis fuction (RBF), and Support vector regression (SVR), according to the local measures. Finally, K-fold cross-validation and additional experiments validation methods are adopted to evaluate the prediction accuracy of the proposed EM-AHL. Results illustrate that the proposed EM-AHL not only outperforms the stand-alone metamodels but also provides more accurate results than the EM constructed by global measures (EM-G). Among the three prediction objectives, the prediction accuracy of the proposed EM-AHL has improved by up to 20% compared to the stand-alone metamodels. Besides, the main effects and contribution rates of process parameters on the responses are analyzed. Overall, the proposed EM-AHL method exhibits the excellent capability of guiding the actual SLM manufacturing.