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A knowledge-enabled approach for user experience-driven product improvement at the conceptual design stage

Published online by Cambridge University Press:  17 August 2023

Jun Li
Affiliation:
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China Yibin Institute of Industrial Technology, Sichuan University Yibin Park, Yibin, China
Xin Guo
Affiliation:
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China Yibin Institute of Industrial Technology, Sichuan University Yibin Park, Yibin, China
Kai Zhang
Affiliation:
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China Yibin Institute of Industrial Technology, Sichuan University Yibin Park, Yibin, China
Wu Zhao*
Affiliation:
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China Yibin Institute of Industrial Technology, Sichuan University Yibin Park, Yibin, China
*
Corresponding author: Wu Zhao; Email: zhaowu@scu.edu.cn

Abstract

Improving existing products plays a vital role in enhancing customer satisfaction and coping with changes in the market. Analyzing user experience (UX) to find the deficiencies of existing products and establishing improved schemes is the key to UX-driven product improvement, especially at the conceptual design stage. Although some tools used in conceptual design, such as requirements analysis and knowledge reasoning, have advanced recently, they lack targeted goals and sufficient efficiency in identifying insufficient product attributes and improving existing functions and structures. The challenge lies in considering the influence imposed on design activities by the original product features (including attributes, functions, and structure). In this study, a knowledge-enabled approach and framework that integrates the conceptual design process, online reviews for UX, and knowledge is proposed to support product improvement. Specifically, a decision-making algorithm based on UX analysis is proposed to identify to-be-improved product attributes. Then, through optimizing the previous knowledge application model from knowledge requirement transformation, knowledge modeling, and knowledge reasoning, a smart knowledge reasoning model is established to push knowledge for functional solving of the to-be-improved attributes. A knowledge configuration method is used to modify product features to generate an improved scheme. To demonstrate the feasibility of the proposed approach, a case study of improving an agricultural sprayer is conducted. Through discussion, this study can help to regulate design activities for product improvement, enhance data and knowledge application, and promote divergent thinking during scheme modification.

Type
Research Article
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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