User evaluation and eye tracking-based prediction model for tractor hood product design

School of mechanical and automotive engineering, Qilu University of Technology, Jinan, Shandong, 250353, China

^∗ Corresponding author’s e-mail: zhengfeng@qlu.edu.cn

Abstract

To objectively evaluate the product design of tractor hoods, they have been set as variables, and the remaining components of the hood have been taken as the rations. Eye-tracking and semantic difference-based experiments were performed to determine the level of attention a user gave to the hood and an image evaluation value for the same; morphological analysis was used to deconstruct the structural elements of the tractor hood. The structural elements and image evaluation values were implemented as input and output layers, respectively, in a back-propagation neural network (BPNN) used to train and verify a user-evaluation prediction model for tractor hood designs. The results show that the BPNN model can accurately predict a user’s evaluation of the tractor hood design, thereby providing a reference for designers in terms of the tractor hood shape, and quantify user evaluations of the hood design.