ScholarWorks@경상국립대학교

초록

Purpose: This study aims to assess the reliability of fatigue life for additively manufactured polylactic acid (PLA) specimens by considering the physical uncertainty of additive manufacturing (AM) conditions based on a deep neural network (DNN). Methods: The fatigue specimens were manufactured by fused deposition modeling (FDM) in accordance with AM conditions. The fatigue life test was then conducted using the rotational fatigue test equipment. Furthermore, to consider the physical uncertainties resulting from AM processes, the variation of AM parameters was assumed as a normal distribution. The DNN model was trained to predict the fatigue life based on experimental data in accordance with AM conditions. The reliability assessment was then performed with Monte Carlo simulation (MCs). Results: The DNN-based reliability assessment is carried out by employing MCs. The result shows that the reliability is converged by increasing the number of sample data. In addition, the predicted reliability is verified by error evaluation under a 95% confidence level. Conclusion: The framework of DNN-based reliability assessment is suggested to predict and assess the reliability of fatigue life in accordance with AM conditions. This framework can be used for the reliability assessment of fatigue behavior for various product designs manufactured with AM.

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