Bayesian inference-based decision of fatigue life model for metal additive manufacturing considering effects of build orientation and post-processing
Source abstract
Abstract
Abstract This study proposes a Bayesian inference-based decision framework to quantify the physical uncertainty based on fatigue life tests on maraging steel according to post-processing treatments and build orientations. Uncertainty quantification of fatigue life models is performed to determine the most suitable models for the metal additive manufacturing process by employing Bayesian inference. To select one of the fatigue life models, we introduce a weighted-equivalent metric (WEM) to compare the evaluation results from different statistical metrics. By evaluating the WEM value, the logistic model and Zhurkov fatigue life model are identified as the suitable fatigue life models for maraging steel.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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