Form design of product image using grey relational analysis and neural network models
This paper presents a new approach to determining the best design combination of product form elements for matching a given product image represented by a word pair. A grey relational analysis (GRA) model is used to examine the relationship between product form elements and product image, thus identifying the most influential elements of product form for a given product image. A grey prediction (GP) model and a neural network (NN) model are used individually and in conjunction with the GRA model, in order to predict and suggest the best form design combination. An experimental study on the form design of mobile phones is conducted to evaluate the performance of these models. Based on expert surveys, the concept of Kansei Engineering is used to extract and evaluate the experimental samples, and a morphological analysis is used to extract form elements from these sample mobile phones. The evaluation result shows that all the NN-based models outperform the GP-based models, suggesting that the NN model should be used to help product designers determine the best combination of form elements for achieving a desirable product image. The GRA model can be incorporated into the NN model to help designers focus on the most influential elements in form design of mobile phones.
product image grey relational analysis neural networks product form grey prediction kansei engineering