Intelligent design technology of automobile inspection tool based on 3D MBD model intelligent retrieval

Abstract

The existing process equipment design resource utilization rate in automobile industry is low, so it is urgent to change the design method to improve the design efficiency. This paper proposed a fast design method of process equipment driven by classification retrieval of 3D model-based definition (MBD). Firstly, an information integration 3D model is established to fully express the product information definition and to effectively express the design characteristics of the existing 3D model. Through the classification machine-learning algorithm of 3D MBD model based on Extreme Learning Machine (ELM), the 3D MBD model with similar characteristics to the auto part model to be designed was retrieved from the complex process equipment case database. Secondly, the classification and retrieval of the model are realized, and the process equipment of retrieval association mapping with 3D MBD model is called out. The existing process equipment model is adjusted and modified to complete the rapid design of the process equipment of the product to be designed. Finally, a corresponding process equipment design system was developed and verified through a case study. The application of machine learning to the design of industrial equipment greatly shortens the development cycle of equipment. In the design system, the system learns from engineers, making them understand the design better than engineers. Therefore, it can help any user to quickly design 3D models of complex products.

Keywords

Design of process equipment MBD model retrieval ELM case reuse

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References

Zhang

Wang

CK.

Integrating case-based with rule-based reasoning in body-in-white fixture design. Int J Adv Manuf Technol 2016; 85: 1807–1824.

Zhou

Wang

A feature-based fixture design methodology for the manufacturing of aircraft structural parts. Robot Comput Integr Manuf 2011; 27(6): 986–993.

Zhou

Liu

, et al. A similarity-based reuse system for injection mold design in automotive interior industry. Int J Adv Manuf Technol 2016; 87: 1783–1795.

Zhang

TH.

Knowledge component-based intelligent method for fixture design. Int J Adv Manuf Technol 2018; 94(9–12): 4139–4157.

Wang

Xue

, et al. 3D model retrieval and classification by semi-supervised learning with content-based similarity. Inf Sci 2014; 281: 703–713.

Kasie

Bright

Walker

An intelligent decision support system for on-demand fixture retrieval, adaptation and manufacture. J Manuf Technol Manag 2017; 28(2): 189–211.

Gao

Zhang

Pan

Semantic oriented 3D model classification and retrieval using Gaussian processes. J Comput Inform Syst 2011; 7(4): 1029–1037.

Chen

Tian

Shen

, et al. On visual similarity based 3D model retrieval. Comput Graph Forum 2003; 22(3): 223–232.

Mukul

Satyandra

Antonio

, et al. Machining feature-based similarity assessment algorithms for prismatic machined parts. Comput Aided Des 2006; 38(9): 954–972.

10.

Chin-Chia

Sheng

FL.

Content-based 3D model classification using geometric graph representation. Int J Appl Environ Sci 2011; 5(2): 116–122.

11.

Qiao

, et al. A 3D assembly model retrieval method based on assembly information. Assem Autom 2019; 39(4): 556–565.

12.

Leng

Zhang

Yao

, et al. 3D object classification using deep belief networks. In: MultiMedia modeling, Dublin, Ireland, 6–10 January 2014, pp.128–139.

13.

Nie

Wang

, et al. The assessment of 3D model representation for retrieval with CNN-RNN networks. Multimed Tools Appl 2019; 78: 16979–16994.

14.

Tao

Wang

Zheng

, et al. Cad model retrieval based on inexact graph matching. J Comput Aided Des Comput Graphics 2010; 22(3): 545–552.

15.

Bai

Chen

Gao

SM.

A flexible assembly retrieval approach for model reuse. Comput Aided Des 2012; 44(6): 554–574.

16.

You

Tsai

YL.

3D solid model retrieval for engineering reuse based on local feature correspondence. Int J Adv Manuf Technol 2010; 46(5–8): 649–661.

17.

Zhu

Jiang

, et al. Ontology and CBR based automated decision-making method for the disassembly of mechanical products. Adv Eng Inf 2016; 30(3): 564–584.

18.

Wang

Guo

MY.

Exploration of non-revolved parts modeling based on MBD and pro/engineer. Adv Mater Res 2013; 712–715: 1171–1174.

19.

Alemanni

Destefanis

Vezzetti

Model-based definition design in the product lifecycle management scenario. Int J Adv Manuf Technol 2011; 52: 1–14.

20.

Huang

Zhang

Bai

, et al. An effective subpart retrieval approach of 3D CAD models for manufacturing process reuse. Comput Ind 2015; 67: 38–53.

21.

Seyyed

Hedieh

Monitoring air pollution by deep features and extreme learning machine. J Exp Theor Artif Intell 2019; 31(4): 517–531.

22.

Akusok

Miche

Hegedus

, et al. A two-stage methodology using K-NN and false-positive minimizing ELM for nominal data classification. Cognit Comput 2014; 6(3): 432–445.

23.

Grana

Chyzhyk

Savio

Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Networks 2015; 68: 23–33.

24.

, et al. A robust extreme learning machine for modeling a small-scale turbojet engine. Appl Energy 2018; 218: 22–35.

25.

Liu

Chen

, et al. TOSELM: timeliness online sequential extreme learning machine. Neurocomputing 2014; 128: 119–127.

26.

Wen

Yang

LM.

Lagrangian supervised and semi-supervised extreme learning machine. Appl Intell 2019; 49(2): 303–318.

27.

Bai

Gao

Tang

, et al. Design reuse oriented partial retrieval of CAD models. Comput Aided Des 2010; 42(12): 1069–1084.

28.

Wen

Lin

, et al. News event evolution model based on the reading willingness and modified TF-IDF formula. J High Speed Networks 2017; 23(1): 33–47.