Study on imagery modeling of electric recliner chair: based on combined GRA and Kansei engineering

Zhou, C, Jiang, L and Kaner, J ORCID: https://orcid.org/0000-0002-7946-7433, 2023. Study on imagery modeling of electric recliner chair: based on combined GRA and Kansei engineering. Applied Sciences, 13 (24): 13345. ISSN 2076-3417

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Abstract

This study aims to integrate data-driven methodologies with user perception to establish a robust design paradigm. The study consists of five steps: (1) theoretical research—a review of the subject background and applications of Kansei engineering and gray relational analysis (GRA); (2) algorithmic framework research—the discussion delves into the intricate realm of Kansei engineering theory, accompanied by a thorough elucidation of the gray relational analysis (GRA) algorithmic framework, a crucial component in constructing a fuzzy logic model for product image modeling; (3) Kansei data collection—18 groups of perceptual words and six classic samples are selected, and the electric recliner chair samples are scored by the Kansei words; (4) Kansei data analysis—morphological analysis categorizes the electric recliner chair into four variables. followed by the ranking and key consideration areas of each area; (5) GRA fuzzy logic model verification—the GRA fuzzy logic model performs simple–complex (S-C) imagery output on 3D models of three modeling instances. By calculating the RMSE value of the seat image modeling design GRA fuzzy logic model, it is proven that the seat image modeling design GRA fuzzy logic model performs well in predicting S-C imagery. The subsequent experimental study results also show that the GRA fuzzy logic model consistently produces lower root mean square error (RMSE) values. These results indicate the efficacy of the GRA fuzzy logic approach in forecasting the visual representation of the electric recliner chair shape’s 3D model design. In summary, this research underscores the practical utility of the GRA model, harmoniously merged with perceptual engineering, in the realm of image recognition for product design. This synergy could fuel the extensive exploration of product design, examining perceptual engineering nuances in product modeling design.

Item Type:	Journal article
Publication Title:	Applied Sciences
Creators:	Zhou, C., Jiang, L. and Kaner, J.
Publisher:	MDPI
Date:	18 December 2023
Volume:	13
Number:	24
ISSN:	2076-3417
Identifiers:	Number Type 10.3390/app132413345 DOI 1851255 Other
Rights:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > Nottingham School of Art & Design
Record created by:	Jonathan Gallacher
Date Added:	15 Jan 2024 10:25
Last Modified:	15 Jan 2024 10:25
URI:	https://irep.ntu.ac.uk/id/eprint/50684

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