A methodology of integrating affective design with defining engineering specifications for product design

Jiang, Huimin; Kwong, C. K.; Liu, Ying; Ip, W. H.

doi:10.1080/00207543.2014.975372

Cited by 45 publications

(17 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That is the sole restriction of KE technique [10], and the weak point of design assistance system that we aim to apply to different groups. Kuang and Jiang [11] separate database in KE into groups according to target groups that share the same appreciation of the same product prototype. This leads to increasing accuracy of KE.…”

Section: Introductionmentioning

confidence: 99%

Application of Kansei Engineering and Data Mining in Developing an Ingenious Product Co-design System

Sakornsathien¹,

Sinthupinyo²,

Anuntavoranich³

2019

IJMLC

View full text Add to dashboard Cite

This research intends to be a part in developing an automatic system that shows the potential of designing a product form by co-designing with the user in order to make it suitable for each consumer by applying Kansei Engineering Technique. However, the operation of KE needs data collection from specific target group in order to devise a formula and interpret the results when applying to study the same target group, or else the accuracy will be reduced. In order to eliminate the drawbacks, Data Mining techniques will be applied with KE system in order to use as an ingenious product co-design system.The style and preference of each user will be used as a categorizing factor clustering the database into groups with K-means technique, in this research. Each classifying cluster will use its own database in the system processing in order to gain a set of design elements precisely from the system. Decision Tree classification technique is selected for the study. For the model validation, we apply the cross-validation as an unbiased model performance evaluation. In order to build the KE system, the sentiments in this research are indicated by 5 pair-words constituting the system data. The result shows that, this method offers more accurate prediction of design elements comparing to the method which undivided users.Index Terms-Data mining, machine learning, Kansei engineering, product development.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of Kansei Engineering and Data Mining in Developing an Ingenious Product Co-design System

Sakornsathien¹,

Sinthupinyo²,

Anuntavoranich³

2019

IJMLC

View full text Add to dashboard Cite

show abstract

“…For example, Yang [12] used support vector regression (SVR) and nondominated sorting genetic algorithm-II (NSGA-II) in the construction of a hybrid Kansei engineering system (KES) for product form design. Jiang et al [13] proposed a chaos-based NSGA-II for solving an MOO model in product design. However, these studies have focused mainly on obtaining Pareto solutions, not on further combining MCDM methods to find optimal design solution according to the consumers' preference.…”

Section: Introductionmentioning

confidence: 99%

Product Form Design Model Based on Multiobjective Optimization and Multicriteria Decision‐Making

Shieh

Yang

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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.

show abstract

“…An increasing number of companies are adopting integrated co-design strategies in new product development to minimize the risk of their products not being accepted (Durugbo, 2014). This requires constructing and measuring user satisfaction (Jiang et al, 2015). In this perspective, acceptability estimation can be used for estimating the probability of success of the new concept (Luo, Su, and Lee, 2011).…”

Section: Introductionmentioning

confidence: 99%

Improving users’ product acceptability: an approach based on Bayesian networks and a simulated annealing algorithm

Garces

Rakotondranaivo

Bonjour

2016

International Journal of Production Research

View full text Add to dashboard Cite

Developing products that are properly suited to users' needs and preferences in order to be accepted is one of the main challenges designers and engineers face constantly. Evaluating and improving users' product acceptability has become an important research question. Many approaches leave the acceptability evaluation question for the last phases of the New Product Development process (NPD), when an almost finished prototype is available and when there is no time left for important modifications. In the early phase of the NPD process, the project managers need models and methods to evaluate the potential acceptability of the new concept and if required, to define actions to improve this concept. In this paper, a method with two main goals is proposed to tackle this problem. Its first goal consists in evaluating an index of users' product acceptability. When this index is too low, the second goal concerns the optimal selection of the most appropriate actions (improvement scenario) to increase this previously assessed index and to optimize the supplementary cost. As information collected from users in the early phase is subject to uncertainty, the proposed method exploits the inference properties of Bayesian networks (BN) making it possible to make useful estimations of the acceptability index. Furthermore, the improvement scenarios are composed of actions that make it possible to improve di↵erent criteria composing the users' acceptability index. The improvement problem is formulated as an optimization problem to be solved by a simulated annealing (SA) algorithm. In order to illustrate its interest, the proposed method is applied to a real case concerning the design of a medical-stocking threading device.

show abstract

A methodology of integrating affective design with defining engineering specifications for product design

Cited by 45 publications

References 57 publications

Application of Kansei Engineering and Data Mining in Developing an Ingenious Product Co-design System

Application of Kansei Engineering and Data Mining in Developing an Ingenious Product Co-design System

Product Form Design Model Based on Multiobjective Optimization and Multicriteria Decision‐Making

Improving users’ product acceptability: an approach based on Bayesian networks and a simulated annealing algorithm

Contact Info

Product

Resources

About