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Uncertainty quantification in dimensions dataset of additive manufactured NIST standard test artifact.

ABSTRACT: The printed features on an additive manufactured part will often deviate from the nominal values of the 3D model's features due to the factors such as printer resolution, printing parameters, printing technology, and the measurement method. The National Institute of Standards and Technology (NIST) standard test artifact contains a collection of various features that can be used to characterize a 3D printer's performance and has been used to benchmark metal printers. There is limited documentation on how well different additive manufacturing processes can fabricate the NIST artifact. This dataset records the dimensional uncertainty of selective printed features of the NIST artifact manufactured with polymer and resin printing processes. It contains the post-processing dimensional measurements of geometric features on the printed test artifacts. In order to generate the data, a total of 16 samples of the test artifact were printed with fused deposition modelling (FDM) and stereolithography (SLA) additive manufacturing methods. The percentage error between the measurement of features in the printed samples and their nominal computer aided design (CAD) values are calculated. For future reusability of this data, the same NIST test artifact CAD model can be printed, and the features' measurements can be compared with the dataset presented in this article.

SUBMITTER: Mac G

PROVIDER: S-EPMC8427264 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Individualized treatment rules can improve health outcomes by recognizing that patients may respond differently to treatment and assigning therapy with the most desirable predicted outcome for each individual. Flexible and efficient prediction models are desired as a basis for such individualized treatment rules to handle potentially complex interactions between patient factors and treatment. Modern Bayesian semiparametric and nonparametric regression models provide an attractive avenue in this regard as these allow natural posterior uncertainty quantification of patient specific treatment decisions as well as the population wide value of the prediction-based individualized treatment rule. In addition, via the use of such models, inference is also available for the value of the optimal individualized treatment rules. We propose such an approach and implement it using Bayesian Additive Regression Trees as this model has been shown to perform well in fitting nonparametric regression functions to continuous and binary responses, even with many covariates. It is also computationally efficient for use in practice. With Bayesian Additive Regression Trees, we investigate a treatment strategy which utilizes individualized predictions of patient outcomes from Bayesian Additive Regression Trees models. Posterior distributions of patient outcomes under each treatment are used to assign the treatment that maximizes the expected posterior utility. We also describe how to approximate such a treatment policy with a clinically interpretable individualized treatment rule, and quantify its expected outcome. The proposed method performs very well in extensive simulation studies in comparison with several existing methods. We illustrate the usage of the proposed method to identify an individualized choice of conditioning regimen for patients undergoing hematopoietic cell transplantation and quantify the value of this method of choice in relation to the optimal individualized treatment rule as well as non-individualized treatment strategies.

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Uncertainty quantification in dimensions dataset of additive manufactured NIST standard test artifact.

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