Prediction of Kansei engineering features for bottle design by a Knowledge Based System

The aesthetic appeal of industrial products is an important requirement in the modern global market; even more relevant in case of large-scale products without special requirements in terms of performances such as bottles for mass distribution. The present paper proposes a Knowledge-Based System that integrates a semi-automatic tool for product design generation with a concept-tool for predicting the emotional impact of bottle design. With the aim to ensure the feasibility of the product without modifying the designer intention, the system combines rules and constraints related to geometries, materials, manufacturing processes, the top load and the inner pressure resistance. It is focused on a predictive case-based procedure, which exploits Artificial Neural Networks performances. The implemented tool assists the user to elaborate a preliminary forecast about the perception of an object based on some of its features through the Kansei engineering approach.