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Related Experiment Video

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Convolutional Neural Network Models Combined with Kansei Engineering in Product Design.

Yuping Hu1, Kechun Yan2

  • 1Academy of Art and Design, Shaoyang University, Shaoyang 422099, Hunan, China.

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Summary

This study integrates deep learning with Kansei Engineering to create user-centered product designs. A convolutional neural network (CNN) model enhances product perception analysis, improving market competitiveness.

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence
  • Product Design

Background:

  • Discusses sensory engineering and its application in product design.
  • Reviews Kansei Engineering theory and convolutional neural network (CNN) algorithms.

Purpose of the Study:

  • To propose an efficient design method combining deep learning and user perception.
  • To enhance product competitiveness by meeting user perceptual needs.

Main Methods:

  • Established a perceptual evaluation system for product design using a CNN model.
  • Analyzed the CNN model's effectiveness using electronic scale design examples.

Main Results:

  • The CNN model improved the logical depth of perceptual information in product design.
  • Demonstrated a correlation between user perception and product design shapes for electronic scales.

Conclusions:

  • CNN models and Kansei Engineering are significant for product design image recognition and perceptual modeling.
  • Product perception analysis using CNNs accurately correlates design elements with sensory engineering principles.