Issue |
E3S Web Conf.
Volume 450, 2023
International Conference on SDGs and Bibliometric Studies (ICoSBi 2023)
|
|
---|---|---|
Article Number | 02009 | |
Number of page(s) | 9 | |
Section | Engineering and Technology | |
DOI | https://doi.org/10.1051/e3sconf/202345002009 | |
Published online | 29 November 2023 |
Automatic shoe drying oven integrated with Raspberry Pi Cloud system for advanced footwear industry
1 Departement Mechanical Engineering, State University of Surabaya, Surabaya, Indonesia
2 School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney NSW, Australia
* Corresponding author: yunus@unesa.ac.id
The drying oven functions to reduce the moisture content of raw materials until it reaches a certain moisture content to slow the rate of product damage due to biological and chemical activity. This research aims to design an automatic shoe-drying oven equipped with an LCD screen, exhaust fan, 8-level shelf, and electric heating temperature controller to provide maximum and even heating of leather shoes when gluing and drying leather shoes. Raspberry PI aims to combine and collect production process data in cloud storage in the form of a database, which will then be sent and displayed directly to the operator. The research methodology includes design, manufacturing, assembly, operational testing, performance testing, and evaluation stages. It can be concluded that the research results and progress reports include: (1) The design of the work system and mechanical system for the shoe drying oven can work well. (2) The Raspberry PI Cloud system can collect and display actual drying time data, (3) glue drying temperature of 60°C with a drying time of ±5 minutes, (4) skin heating temperature (texture creation) of 50°C-55°C requires warm-up time ±40 minutes, (5) This machine requires 850 watts of power.
© 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.