Machine Learning-Assisted Characterization of Pore-Induced Variability in Mechanical Response of Additively Manufactured Components

Rezasefat, Mohammad; Hogan, James D.

doi:10.3390/modelling5010001

Cited by 3 publications

(1 citation statement)

References 77 publications

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“…More advanced methods like Generative Adversarial Networks (GANs) [27], cycle-consistent adversarial neural networks (CycleGAN) [28], and conditional generative adversarial networks (cGAN) [29] have also been used for the generation of images that predict field data [12,[30][31][32]. As an example, Hoq et al [12] used several machine learning methods such as artificial neural networks (ANNs) [33], CNNs, and cGAN for the prediction of stress fields in structures with random heterogeneity and concluded that CNNs and cGAN can outperform classical machine learning methods (e.g. random forest and K-nearest neighbors) for such tasks.…”

Section: Introductionmentioning

confidence: 99%

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Rezasefat,

Hogan

2024

Mach. Learn.: Sci. Technol.

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This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the Multi-Decoder CNN (MUDE-CNN) and the Multiple Encoder-Decoder Model with Transfer Learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder-decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via additive manufacturing. The temporal model evaluation demonstrated MTED-TL's consistent superiority over MUDE-CNN, owing to transfer learning's advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.

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