Abstract
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.
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Abbreviations
- ALOO:
-
Average leave-one-out
- AM:
-
Additive manufacturing
- ANOVA:
-
Analysis of variance
- DOE:
-
Design of experiment
- EC:
-
Energy consumption
- EM:
-
Ensemble of metamodels
- EM-AHL:
-
Adaptive hybrid leave-one-out error-based EM
- EM-G:
-
EM constructed by global measures
- LOO:
-
Leave-one-out
- LP:
-
Laser power
- LT:
-
Layer thickness
- PUR:
-
Powder utilization rate
- RBF:
-
Radial basis function
- RMAE:
-
Relative maximum absolute error
- RMSE:
-
Root mean square error
- SLM:
-
Selective laser melting
- SS:
-
Scanning speed
- SVR:
-
Support vector regression
- TS:
-
Tensile strength
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Acknowledgements
This research has been supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 51805179, 51775203, and 51721092, the National Defense Innovation Program under Grant No. 18-163-00-TS-004-033-01, the research fund under Grant No. 61400020401, the Research Funds of the Maritime Defense Technologies Innovation under Grant YT19201901.
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Li, J., Cao, L., Hu, J. et al. A prediction approach of SLM based on the ensemble of metamodels considering material efficiency, energy consumption, and tensile strength. J Intell Manuf 33, 687–702 (2022). https://doi.org/10.1007/s10845-020-01665-z
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DOI: https://doi.org/10.1007/s10845-020-01665-z