Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 15, 2024
|
|
---|---|---|
Article Number | 10 | |
Number of page(s) | 13 | |
DOI | https://doi.org/10.1051/smdo/2024006 | |
Published online | 24 May 2024 |
Research Article
Computer-aided design of hand-drawn art food packaging design based on a deep neural network model
Art and Design Department, Zibo Vocational Institute, Zibo, Shandong 255000, China
* e-mail: huicui1980@126.com
Received:
18
January
2024
Accepted:
18
March
2024
Background: The term “hand-drawn art food packaging design” (HAFPD) refers to a novel and creative method of food package design that makes use of hand-drawn illustrations, typography, and graphi graphics. This kind of design technique allows for a more individual touch, giving the package a special feeling that might strike a more meaningful connection with customers. Materials and Methods: Hand-drawn art on food packaging is a powerful tool for brand identification and awareness since it helps communicate the company's values, personality, and history. When it comes to developing HAFPD, computer-aided design (CAD) may be a very useful tool. The study proposes a deep neural network (DNN)-based CAD system for HAFPD. Results: The approach blends conventional hand-drawing processes with modern digital tools to empower designers to produce package designs that are both aesthetically pleasing and practically viable. Conclusion: To track duration statistics for watch design using CAD software, designers can use a time-tracking tool or plugin. These tools can track the time spent on different tasks, such as sketching, modeling, rendering, and refining the design in food packaging. The proposed technique offers designers an effective and simple way to produce distinctive food packaging designs while preserving the authenticity of hand-drawn artwork.
Key words: hand-drawn illustrations / deep neural network (DNN) / Computer-aided design (CAD) / food packaging / hand-drawn art food packaging design (HAFPD)
© H. Cui, Published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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