scholarly journals Application of Fuzzy-Based Hybrid Taguchi Method for Multiobjective Optimization of Product Form Design

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Yongfeng Li ◽  
Meng-Dar Shieh ◽  
Chih-Chieh Yang ◽  
Liping Zhu
Keyword(s):  
Taguchi Method ◽  
Robust Design ◽  
Affective Response ◽  
Affective Responses ◽  
Product Form ◽  
Design Approach ◽  
Characteristic Index ◽  
Design Variables ◽  
Form Design ◽  
Product Form Design

In today’s competitive market, industrial product form design is moving towards being consumer centric. Affective responses relate to customers’ affective needs and are receiving increasing attention. To design a product form that can appeal to consumers, designers should consider multiple affective responses (MARs). This paper proposes a robust design approach that uses a fuzzy-based hybrid Taguchi method to derive the optimal product form design concerning MARs. First, design analysis is used to identify design variables and MARs. According to the results, a Taguchi experiment is designed in which fuzzy sets are used to measure the MARs; then, signal-to-noise (S/N) ratios are calculated. Subsequently, a fuzzy questionnaire with multiple answers is employed to acquire consumers’ preference weights for MARs, following which Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is adopted to transform the multiple S/N ratios into a multiperformance characteristic index (MPCI). On the basis of the MPCI, the effects of design variables are identified through analysis of variance and the response table and the response graph are obtained. Consequently, the optimal form design is achieved. A car profile design was used as an example to demonstrate the proposed approach. The results indicate that this approach can effectively improve consumers’ affective response qualities and can be used as a robust design approach to optimize product form design.

Modeling Product Form Preference Using Gestalt Principles, Semantic Space, and Kansei

2012 ◽  
Author(s):  
José E. Lugo ◽  
Stephen M. Batill ◽  
Laura Carlson
Keyword(s):  
Semantic Space ◽  
Product Form ◽  
Sources Of Information ◽  
Gestalt Principles ◽  
Design Concepts ◽  
Design Variables ◽  
Form Design ◽  
Product Form Design ◽  
Different Sources ◽  
Product Preference

Engineers describe design concepts using design variables. Users develop their visual judgment of products by mentally grouping design variables according to Gestalt principles, extracting meaning using semantic dimensions and attaching attributes to the products, as reflected in Kansei methodology. The goal of this study was to assess how these different sources of information and representations of product form (design variables, Gestalt variables, Kansei attributes, and semantic dimensions) could combine to best predict product preference for both designers and users. Sixteen wheel rim designs were created using four design variables that were also combined into higher-order Gestalt variables. Sixty-four participants viewed each rim, and rated it according to semantic dimensions and Kansei attributes, and provided an overall “like” rating. The most reliable prediction of product preference were developed using Gestalt variables in combination with the meaning and emotion the users attached to the product. Finally, implications for designers are discussed.

Grey-based Taguchi Method to Optimize the Multi-response Design of Product Form Design

2021 ◽  
Vol 20 (2) ◽  
pp. 136
Author(s):  
Sugoro Bhakti Sutono
Keyword(s):  
Taguchi Method ◽  
Product Design ◽  
Optimization Problems ◽  
Optimization Method ◽  
Product Form ◽  
Design Parameters ◽  
Optimal Product Design ◽  
Form Design ◽  
Response Optimization ◽  
Product Form Design

This paper presents a multi-response optimization method that uses the grey-based Taguchi method as the integrative product form design optimization method, and it serves as a tool for product form design to determine the optimal combination of design parameters in Kansei engineering (KE). This method is unique in that it combines the Taguchi method (TM) and grey relational analysis (GRA), allowing it to take advantage of the benefits of both methods. The TM is used to design experiments and generate combinative product form design samples which can be used to improve product quality. The GRA is applied to multi-response optimization problems. Factor effect analysis and analysis of variance (ANOVA) are used to determine which combinations of design parameters will result in the optimal product design. To demonstrate the applicability of the grey-based TM, a case study of a car form design is presented, and a confirmation test is performed to verify the performance of the optimal product design. The results show that the grey-based TM can deal with optimization problems with multiple Kansei responses and determine an optimal car form design that is representative of the consumers' perception in a systematic manner. The confirmation test results also show that the optimal product design generated by the grey-based TM can be used to improve the overall quality of a product form.

scholarly journals Product Form Design Model Based on Multiobjective Optimization and Multicriteria Decision-Making

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Meng-Dar Shieh ◽  
Yongfeng Li ◽  
Chih-Chieh Yang
Keyword(s):  
Decision Making ◽  
Multiobjective Optimization ◽  
Multicriteria Decision Making ◽  
Affective Responses ◽  
Product Form ◽  
Type I ◽  
Multicriteria Decision ◽  
Model Based ◽  
Form Design ◽  
Product Form Design

Affective responses concern customers’ affective needs and have received increasing attention in consumer-focused research. To design a product that appeals to consumers, designers should consider multiple affective responses (MARs). Designing products capable of satisfying MARs falls into the category of multiobjective optimization (MOO). However, when exploring optimal product form design, most relevant studies have transformed multiple objectives into a single objective, which limits their usefulness to designers and consumers. To optimize product form design for MARs, this paper proposes an integrated model based on MOO and multicriteria decision-making (MCDM). First, design analysis is applied to identify design variables and MARs; quantification theory type I is then employed to build the relationship models between them; on the basis of these models, an MOO model for optimization of product form design is constructed. Next, we use nondominated sorting genetic algorithm-II (NSGA-II) as a multiobjective evolutionary algorithm (MOEA) to solve the MOO model and thereby derive Pareto optimal solutions. Finally, we adopt the fuzzy analytic hierarchy process (FAHP) to obtain the optimal design from the Pareto solutions. A case study of car form design is conducted to demonstrate the proposed approach. The results suggest that this approach is feasible and effective in obtaining optimal designs and can provide great insight for product form design.

Product form design model based on the robust posterior preference articulation approach

2019 ◽  
Vol 27 (2) ◽  
pp. 126-143
Author(s):  
Yongfeng Li ◽  
Liping Zhu
Keyword(s):  
Optimization Model ◽  
Affective Responses ◽  
Product Form ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Signal To Noise ◽  
Multi Objective ◽  
Model Based ◽  
Form Design ◽  
Product Form Design

Affective responses reflect consumers’ affective needs and have attracted considerable attention in industrial product form design. When designing a product for consumers, designers should take into account multiple affective responses. Therefore, designing products that can satisfy multiple affective responses is a multi-objective optimization problem. In this article, a novel model based on the robust posterior preference articulation approach is proposed to optimize product form design by simultaneously considering multiple affective responses. First, design analysis is performed to determine design variables and affective responses. Subsequently, the Taguchi method is used, and the signal-to-noise ratios are calculated. Based on the results, predictive models for signal-to-noise ratios concerning multiple affective responses are built and then a multi-objective optimization model is constructed. The reference-point-based many-objective non-dominated sorting genetic algorithm–II (called NSGA-III) is used to solve the multi-objective optimization model for obtaining Pareto solutions. Finally, a combination of the fuzzy Kano model and the fuzzy optimum selection model is adopted to select the optimal solution from the obtained Pareto solutions. A car profile design was employed to present the proposed approach. The results reveal that the proposed approach can effectively achieve an optimal design and is a robust approach for optimizing product form design.

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