A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering

Featured Application The proposed CP-KEDL system is supposed to evaluate and predict users' perceptual preferences of complex products accurately and comprehensively and quickly generate a set of modeling feature elements that meet the perceptual needs of users to provide design inspiration for complex products for designers. Complex products (CPs) modeling design has a long development cycle and high cost, and it is difficult to accurately meet the needs of enterprises and users. At present, the Kansei Engineering (KE) method based on back-propagated (BP) neural networks is applied to solve the modeling design problem that meets users' affective preferences for simple products quickly and effectively. However, the modeling feature data of CPs have a wide range of dimensions, long parameter codes, and the characteristics of time series. As a result, it is difficult for BP neural networks to recognize the affective preferences of CPs from an overall visual perception level as humans do. To address the problems above and assist designers with efficient and high-quality design, a CP modeling design method based on Long Short-Term Memory (LSTM) neural network and KE (CP-KEDL) was proposed. Firstly, the improved MA method was carried out to transform the product modeling features into feature codes with sequence characteristics. Secondly, the mapping model between perceptual images and modeling features was established based on the LSTM neural network to predict the evaluation value of the product's perceptual images. Finally, the optimal feature sets were calculated by a Genetic Algorithm (GA). The experimental results show that the MSE of the LSTM model is only 0.02, whereas the MSE of the traditional Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) neural network models are 0.30 and 0.23, respectively. The results verified that the proposed method can effectively grapple with the CP modeling design problem with the timing factor, improve design satisfaction and shorten the R&D cycle of CP industrial design.

Automated summary

The proposed CP-KEDL system is a method for complex product modeling design based on Long Short-Term Memory (LSTM) neural network and Kansei Engineering (KE). It transforms product modeling features into feature codes with sequence characteristics using an improved MA method and establishes a mapping model between perceptual images and modeling features based on the LSTM neural network. The experimental results show that the proposed method effectively addresses the modeling design problem of complex products, improves design satisfaction, and shortens the R&D cycle of CP industrial design.