Issue |
E3S Web Conf.
Volume 413, 2023
XVI International Scientific and Practical Conference “State and Prospects for the Development of Agribusiness - INTERAGROMASH 2023”
|
|
---|---|---|
Article Number | 04017 | |
Number of page(s) | 9 | |
Section | Green Chemistry and Sustainable Technologies | |
DOI | https://doi.org/10.1051/e3sconf/202341304017 | |
Published online | 11 August 2023 |
Predictive Modelling of Surface Roughness in Layered Manufacturing Using H15N5D4B and KH28M6
1 Moscow Aviation Institute (National Research University), 125080 Moscow, Russia
2 АО NPO «Saturn», Lenin Ave., 163, Yaroslavl region, 152903 Rybinsk, Russia
* Corresponding author: gxtl@mail.ru
Layered manufacturing (LM) technology has the capability to fabricate 3D physical models efficiently, overcoming the limitations of geometric complexities. However, the surface quality of LM-processed parts often falls short compared to parts made through traditional numerically controlled manufacturing technology. This issue of surface roughness has become a significant concern, despite the numerous potential advantages offered by LM. To address this, an elaborate methodology is proposed to predict the surface roughness of LM-processed parts. The proposed methodology takes into account both theoretical and real-world characteristics of surface roughness distributions to accurately reflect the actual roughness distributions in the predictions. This methodology was tested and used to evaluate properties of the H15N5D4B and KH28M6 materials. To achieve this, a design of the testing sample was developed, and a roughness distribution expression was introduced, utilizing measured roughness data from the aforementioned sample. This expression allows engineers to obtain surface roughness values for all surface angles, i.e., desired 3D models. The methodology also includes a prediction application, which demonstrates the validity and effectiveness of the proposed approach through several application examples.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.