{"id":11606,"date":"2026-09-06T15:24:27","date_gmt":"2026-09-06T07:24:27","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11606"},"modified":"2026-09-06T16:52:44","modified_gmt":"2026-09-06T08:52:44","slug":"jase-202612-35-015","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-015","title":{"rendered":"Uncertainty Analysis of 3D-Printed Dental Models Using a Hybrid Fuzzy Bayesian Model"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Jie Wu, Shuang Zhao, Xiaoguang Li<\/i><\/a><\/p>\n\n\n\n

Stomatology College of Jiamusi University, Jiamusi 154000, China<\/p>\n<\/div>\n\n\n\n

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Received: July 21, 2026
Accepted: August 19, 2026
Publication Date: September 06, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Hybrid fuzzy Bayesian uncertainty-modeling process for 3D-printed dental models<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.015<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This study proposes a hybrid fuzzy Bayesian model for analyzing the deviation field of 3D-printed dental models. The model represents the dental-model surface as a signed three-dimensional deviation field and integrates printing-process variables, local geometric features, and fuzzy memberships for low-error, medium-error, and high-risk states at each sampled surface point. A Student-t mixture likelihood is then used to identify latent error mechanisms, including low-error behavior, material shrinkage, local warping, and registration or measurement noise. Error-field reconstruction, latent-mode probabilities, and threshold-exceedance risks are estimated through Bayesian posterior inference. A simulation-driven benchmark dataset of 3D dental-model surfaces is constructed, and the proposed method is compared with linear regression, Gaussian mixture regression, Gaussian process regression, and a conventional Bayesian hierarchical model. The proposed model achieves the best overall performance in RMSE, MAE, CRPS, 95% interval coverage, and risk AUC. Specifically, its RMSE is 0.031 mm, its CRPS is 0.019, and its 95% interval coverage is 94.7%. Visualizations of the 3D dental-model surface show that the model reconstructs high-gradient deviations in cusps, margins, and support-adjacent regions more accurately while also generating posterior risk maps for quality control. These findings suggest that hybrid fuzzy Bayesian modeling is suitable for uncertainty quantification, scientific computing, and parameter-level decision support in digital dental additive manufacturing. The benchmark is simulation-driven and should therefore be interpreted as methodological validation rather than direct clinical evidence.<\/p>\n\n\n\n

Keywords: 3D printing; dental model; uncertainty quantification; fuzzy Bayesian model; hybrid model; deviation field; posterior simulation; additive manufacturing.<\/em><\/p>\n\n\n\n

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