Issue |
E3S Web Conf.
Volume 450, 2023
International Conference on SDGs and Bibliometric Studies (ICoSBi 2023)
|
|
---|---|---|
Article Number | 02004 | |
Number of page(s) | 8 | |
Section | Engineering and Technology | |
DOI | https://doi.org/10.1051/e3sconf/202345002004 | |
Published online | 29 November 2023 |
Automatic shoe embossing machine integrated with real-time data cloud system to improve SMEs footwear productivity
1 Departement Mechanical Engineering, State University of Surabaya, Surabaya, Indonesia.
2 School of Electronic Engineering and Computer Science, Queen Mary University of London, London.
3 Master Student of Information System, Sepuluh Nopember Institute of Technology (ITS), Surabaya.
* Corresponding author: iskandar@unesa.ac.id
The primary function of the automatic shoe embossing machine integrated with a real-time data cloud system is to enhance the shoe manufacturing process by imprinting designs onto leather shoes, while simultaneously collecting and transmitting production data for monitoring and analysis. This research project aims to design and develop a cuttingedge shoe embossing machine. To achieve this, we employ a Raspberry Pibased cloud system to gather and store production data in a database format, allowing for real-time monitoring and operator feedback. The research methodology encompasses several stages, including design, manufacturing, assembly, operational testing, performance evaluation, and data analysis. In conclusion, this research project demonstrates the successful development of an automatic shoe embossing machine integrated with a real-time data cloud system. This innovation streamlines the shoe manufacturing process, ensuring high-quality embossing while providing valuable real-time production data for enhanced control and decision-making.
© 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.