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0225U004637,
(0124U003279)
,
Machine Learning Enhanced Additive Manufacturing
Розробка сурогатних (базових) моделей машинного навчання для методу Робокастинг
Zhalat-Lozynskyi Ostap B., Доктор технічних наук
15-12-2025
Frantsevich Institute for Problems of Materials Science National Academy of Science of Ukraine
The goal of the project is to develop a new, data-driven approach to the control of the additive manufacturing process through the application of innovative principles based on machine learning.
Сonducted research on the development of surrogate (base) machine learning models that predict the values of deviations of geometric dimensions of printed products of simple shape depending on the printing parameters, and are also capable of quantitatively assessing the model's uncertainty in the accuracy of the forecast for a specific combination of printing parameters. A number of machine learning models with different regression algorithms were built and their predictive ability was analyzed. According to the results, regression on Gaussian processes was selected as the main algorithm that satisfies the prediction conditions for both the prediction of deviation values and the model's uncertainty regarding their forecast, which allow Bayesian optimization of the experiment and the model at the next stage of work.
OSTAP B. ZGHALAT-LOZYNSKYI
Nazarii R. Mediukh
Vasiliev Oleksandr O.
Naumenko Vladyslav Yu.
Krasikov Anton I.
Shevchuk Kateryna S.
Bilyi Vladyslav O.
2025-12-15
Updated: 2025-12-17
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