Decision model of product form design based on capsule network

被引：0
|
作者：
Pei H. [1 ]
Huang X. [1 ]
Li H. [2 ]
Bai Z. [1 ,3 ]
机构：
[1] School of Architecture and Art Design, Hebei University of Technology, Tianjin
[2] College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao
[3] National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 03期
关键词：
Capsule network; Convolutional neural network; Deep learning; Design decision; Digital capsule; Robot morphological characteristics;
D O I：
10.13196/j.cims.2022.03.019
中图分类号：
学科分类号：
摘要：
According to the problem of intelligent decision-making for design scheme image based on deep learning algorithm under the condition of the small sample data set, a product modality design decision model based on Capsule Network(CapsNet)was proposed. A multi-angle image data set based on product morphology semantics was built, and it was pre-processed and feature extracted using artificial intelligence method. The image feature characteristics were obtained by using convolutional layer, and several image features from different convolutional layers were divided into a group in order to generate a master capsule with rich semantic features. A set of digital capsules was obtained and the capsule network model was set up utilizing a dynamic routing algorithm. The performance of the model was improved by training the data set in order to improve the decisions accuracy of the product form design. A small sample data set of the intelligent escort robot was constructed, and the effectiveness and feasibility of the model established were verified. © 2022, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：853 / 863
页数：10
相关论文
共 25 条
  • [1] XU Zhanmin, ZHANG Quan, LU Changde, Et al., Modeling for style description of shape design, Application Research of Computers, 22, 11, pp. 38-41, (2005)
  • [2] PRESS M, COOPER R., The design experience: The role of design and designers in the twenty-first century, (2017)
  • [3] YANG Tao, YANG Yu, XUE Chengmeng, Et al., Multi-attribute decision-making evaluation method for product innovation design scheme with demand preferences of customers, Computer Integrated Manufacturing Systems, 21, 2, pp. 417-426, (2015)
  • [4] HUBER A., Research utilization in the design decision making process, International Journal of Architectural Research, 10, 1, pp. 4-25, (2016)
  • [5] AMINE M E, PAILHES J, PERRY N., Integration of concept maturity in decision-making-for engineering design: An application to a solar collector development, Research in Engineering Design, 28, 2, pp. 235-250, (2017)
  • [6] WANG Liping, JIANG Bo, QIU Feiyue, Multi-objective particle swarm optimization based on decision preferences and its application, Computer Integrated Manufacturing Systems, 16, 1, pp. 140-148, (2010)
  • [7] CHEN Ying, JING Shikai, WANG Yedong, Group decision making method with consensus of product scheme based on design rationale, Journal of Computer-Aided Design & Computer Graphics, 30, 7, pp. 1356-1365, (2018)
  • [8] CHEN Jian, MO Rong, YU Suihuai, Et al., Multi-objective group decision method for crowdsourcing product modeling design scheme in cloud environment, Journal of Zhejiang University: Engineering Science, 8, pp. 1517-1524, (2019)
  • [9] YANG Chunying, Research on the decision model of automobile conceptual design based on the theory of data mining, (2011)
  • [10] ZHU Xiaojie, WANG Guoye, BIAN Feng, An intelligent decision support system for automobile scheme design, Automobile Science and Technology, 2, pp. 28-31, (2005)
123