A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering
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 t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/2/710 |
| _version_ | 1797446735710126080 |
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| author | Jin-Juan Duan Ping-Sheng Luo Qi Liu Feng-Ao Sun Li-Ming Zhu |
| author_facet | Jin-Juan Duan Ping-Sheng Luo Qi Liu Feng-Ao Sun Li-Ming Zhu |
| author_sort | Jin-Juan Duan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T13:45:49Z |
| format | Article |
| id | doaj.art-094d069989034410b2246bead661500a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:45:49Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-094d069989034410b2246bead661500a2023-11-30T21:00:22ZengMDPI AGApplied Sciences2076-34172023-01-0113271010.3390/app13020710A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei EngineeringJin-Juan Duan0Ping-Sheng Luo1Qi Liu2Feng-Ao Sun3Li-Ming Zhu4School of Wedding Culture & Media Arts, Beijing College of Social Administration, Beijing 102600, ChinaSchool of Mechanical Engineering, Tiangong University, Tianjin 300387, ChinaSchool of Literature, Nankai University, Tianjin 300371, ChinaSchool of Mechanical Engineering, Tiangong University, Tianjin 300387, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300350, ChinaComplex 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.https://www.mdpi.com/2076-3417/13/2/710CPsmodeling designKansei EngineeringLSTM neural networkGAtruck crane |
| spellingShingle | Jin-Juan Duan Ping-Sheng Luo Qi Liu Feng-Ao Sun Li-Ming Zhu A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering Applied Sciences CPs modeling design Kansei Engineering LSTM neural network GA truck crane |
| title | A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering |
| title_full | A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering |
| title_fullStr | A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering |
| title_full_unstemmed | A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering |
| title_short | A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering |
| title_sort | modeling design method for complex products based on lstm neural network and kansei engineering |
| topic | CPs modeling design Kansei Engineering LSTM neural network GA truck crane |
| url | https://www.mdpi.com/2076-3417/13/2/710 |
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