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

Product form evolutionary design system construction based on neural network model and multi-objective optimization

The product form evolutionary design based on multi-objective optimization can satisfy the complex emotional needs of consumers for product form, but most relevant literatures mainly focus on single-objective optimization or convert multiple-objective optimization into the single objective by weighting method. In order to explore the optimal product form design, we propose a hybrid product form design method based on back propagation neural networks (BP-NN) and non-dominated sorting genetic algorithm-II (NSGA-II) algorithms from the perspective of multi-objective optimization. First, the product form is deconstructed and encoded by morphological analysis method, and then the semantic difference method is used to enable consumers to evaluate product samples under a series of perceptual image vocabularies. Then, the nonlinear complex functional relation between the consumers’ perceptual image and the morphological elements is fitted with the BP-NN. Finally, the trained BP-NN is embedded into the NSGA-II multi-objective evolutionary algorithm to derive the Pareto optimal solution. Based on the hybrid BP-NN and NSGA-II algorithms, a multi-objective optimization based product form evolutionary design system is developed with the electric motorcycle as a case. The system is proved to be feasible and effective, providing theoretical reference and method guidance for the multi-image product form design.

Kansei Engineering Analysis of Purple-clay Teapot Based on Online Comment Data

Product form has become an important communication medium between designers and consumers. Therefore, the collection and analysis of consumer evaluation of products can provide an important reference index for product form design. In this paper, purple-clay teapot was taken as an example and comments of Tmall consumers were collected through web crawler, and the product image vocabulary was extracted to analyze the needs of users. Using the research method of Kansei Engineering, the semantic space of the modeling and image of purple-clay teapot was established, and the relationship between the modeling elements and the image of purple-clay teapot was searched, which could provide valuable reference for the modeling design of purple-clay teapot.

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

As a Kansei engineering design expert system, the product form design multi-objective evolutionary algorithm model (PFDMOEAM) contains various methods. Among them, the multi-objective evolutionary algorithm (MOEA) is the key to determine the performance of the model. Due to the deficiency of MOEA, the traditional PFDMOEAM has limited innovation and application value for designers. In this paper, we propose a novel PFDMOEAM with an improved strength Pareto evolutionary algorithm 2 (ISPEA2) as the core and combining the elliptic Fourier analysis (EFA) and the entropy weight and technique for order preference by similarity to ideal solution (entropy-TOPSIS) methods. Based on the improvement of the original operators in SPEA2 and the introduction of a new operator, ISPEA2 outperforms SPEA2 in convergence and diversity simultaneously. The proposed model takes full advantage of this superiority, and further combines the EFA method’s high accuracy and degree of multi-method integration, as well as the entropy-TOPSIS method’s good objectivity and operability, so it has excellent comprehensive performance and innovative application value. The feasibility and effectiveness of the model are verified by a case study of a car form design. The simulation system of the model is developed, and the simulation results demonstrate that the model can provide a universal and effective tool for designers to carry out multi-objective evolutionary design of product form.

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

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.