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ORIGINAL RESEARCH article

Front. Mech. Eng.
Sec. Digital Manufacturing
Volume 10 - 2024 | doi: 10.3389/fmech.2024.1503182

Product Perceptual Design Optimization Model Based on BP Neural Network

Provisionally accepted
Lei Yong Lei Yong *
  • Ningbo Polytechnic, Ningbo, China

The final, formatted version of the article will be published soon.

    1) Background: User reviews of online shopping platforms can truly reflect users ' feelings about the use of products. They have the advantages of large sample size, wide range and uniform distribution, and can help optimize the perceptual model of product design; (2) Methods: The web crawler crawls user comments, and TFIDF-SP quantifies them. The principal component analysis method selects the perceptual evaluation index, the morphological analysis method disassembles the product into several main structures, and the BP neural network constructs the perceptual optimization model; (3) Results: Taking the paint tray as an example, according to the trained BP neural network, the shape factor combination with the highest perceptual evaluation value is predicted. The experimental results verify the accuracy of the model; (4) Conclusions: The model based on BP neural network has the ability to quickly and accurately combine the best form factors, improves the design efficiency of product design perceptual optimization, improves the rationality of product design, and provides a new idea for consumer demand market-oriented product design.

    Keywords: Kansei Engineering, BP neural network, Algorithm model, Product design optimization, paint tray

    Received: 28 Sep 2024; Accepted: 11 Nov 2024.

    Copyright: © 2024 Yong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Lei Yong, Ningbo Polytechnic, Ningbo, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.