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Additive manufacturing (AM) techniques have attracted widespread attention in aerospace and biomedical fields due to advantages like high material utilization and extensive design flexibility. However, process-induced defects in AM-built components pose significant challenges for evaluating fatigue performance. The AM-built components are subjected to complex alternating loads in service, making it imperative to develop accurate fatigue life prediction models. Currently, two main approaches are widely employed: theoretical analysis and data-driven methods. Traditional life prediction models like continuum damage mechanics (CDM) suffer from limitations such as low accuracy and restricted applicability. Conversely, data-driven models, such as artificial neural networks (ANN), encounter constraints when dealing with limited sample sizes. To address these issues, knowledge-data hybrid models have emerged as a promising approach that combines physical principles with data insights. In view of this, this study has developed a calibrated CDM model and seamlessly integrated it with an ANN-based data-driven model. Employing methods of feature, parameter, and output fusion, three types of hybrid models based on CDM and ANN have been developed. To quantitatively analyze the prediction accuracy and data requirements of these models, calculations using fatigue data obtained from laser powder bed fusion (LPBF)-processed AlSi10Mg alloy have been performed. The results highlight the crucial role played by the corrective function of training data in the parameter fusion-based model, while indicating a relatively minor influence from the CDM model in terms of prediction accuracy. Moreover, this model retains a commendable level of accuracy even with suboptimal fitting outcomes from the CDM model. The hybrid model, which leverages feature fusion, maximizes the utilization of physical information from the CDM model, thus achieving the highest prediction accuracy and stability when ample data are available. The model based on output fusion, primarily guided by results of the CDM model and enhanced by ANN adjustments, demonstrates relatively superior predictive capabilities in domains outside of the training set compared to other models. These findings provide significant reference value for the further development of high-accuracy, knowledge-data hybrid fatigue life prediction models in AM.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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