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Dataset for training machine learning models to print products from MoSi2 by the robocasting method
(2024-11) Krasikov Anton; Naumenko Vladyslav; Bilyi Vladyslav; Vasiliev Oleksandr; Zgalat-Lozynskyi Ostap
This dataset is designed for training machine learning models to optimize the robocasting method for MoSi₂-based ceramic products. Robocasting is a multi-stage 3D printing process that enables the fabrication of ceramic products with complex geometries. Despite its simplicity, each stage requires careful consideration of numerous parameters to ensure product quality. The dataset contains experimental data correlating printing parameters with the characteristics of the printed products. The data was collected using an Ender 5 printer equipped with Stoneflower 2.0 ceramic printing equipment and a custom control panel. The printing process utilized a ceramic paste comprising 60 wt.% MoSi₂ powder and 40 wt.% plasticizer, extruded through nozzles with diameters ranging from 3 mm to 0.4 mm. Variables include ambient temperature, humidity, desired and actual layer dimensions, nozzle speed, and extrusion multiplier. Each print produced 16 samples and an additional strip to stabilize extrusion. Number of the agreement under which the financing is provided: No. M/19-2024 of 16.05.2024 The dataset file, titled "cleaned_robocasting_df", is not publicly available for download. For access and collaboration, please contact a.krasikov@ipms.kyiv.ua.