Abstract
In the product design field, modeling consumers’ affective responses (CARs) for product form design is very helpful for developing successful products. It is also important for product designers to identify critical product form features (PFFs) to aid them in producing appealing products. In the present paper, a classification-based Kansei engineering system (KES) is proposed for modeling CARs and analyzing PFFs in a systematic manner. First, single adjectives are collected as initial affective dimensions for consumers to evaluate a set of representative products in the first questionnaire experiment. Factor analysis (FA) combined with Procrustes analysis (PA) is then used to extract representative affective dimensions. Second, these representative adjectives are regarded as class labels for consumers to describe their affective responses toward product form design. A large set of product samples are analyzed and their PFFs are encoded into numerical format. In the second questionnaire experiment, consumers are asked to assign one most suitable class labels to each product samples. A multiclass support vector machine (SVM) classification model is constructed for relating CARs and the PFFs. Optimal training parameters of SVM can be determined by a two-step cross-validation (CV). Third, support vector machine recursive feature elimination (SVM-RFE) is applied to pin point critical PFFs by wither using overall ranking or class-specific ranking. The relative importance of each PFF can be also analyzed by examining the weight distribution of the PFFs in each elimination step. A case study of digital camera design is also given to demonstrate the effectiveness of the proposed method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.