Knowledge-driven material design platform based on the whole-process simulation and modeling

Author:

Peng Gongzhuang1ORCID,Li Tie2,Zhai Xiang345,Liu Wenzheng6,Zhang Heming6

Affiliation:

1. National Engineering Research Center for Advanced Rolling Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China

2. Shanghai Electro-Mechanical Engineering Institute, Shanghai 201109, P. R. China

3. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing 100854, P. R. China

4. Beijing Complex Product Advanced Manufacturing Engineering Research Center, Beijing Simulation Center, Beijing 100854, P. R. China

5. Science and Technology on Space System Simulation Laboratory, Beijing Simulation Center, Beijing 100854, P. R. China

6. National CIMS Engineering Research Center, Tsinghua University, Beijing 100084, P. R. China

Abstract

In order to realize the agility, collaboration and visualization of alloy material development process, a product development platform based on simulation and modeling technologies is established in this study. In this platform, the whole-process simulation module builds multi-level simulation models based on metallurgical mechanisms from the production line level, the thermo-mechanical coupling field level and the microstructure evolution level. The design knowledge management module represents the multi-source heterogeneous material design knowledge through ontology model, including customers’ requirement knowledge, material component knowledge, process design knowledge and quality inspection knowledge, and utilizes the case-based reasoning approach to reuse the knowledge. The data-driven modeling module applies machine learning algorithms to mine the relationships between product mechanical properties, material components, and process parameters from historical samples, and utilizes multi-objective optimization algorithms to find the optimal combination of process parameters. Application of the developed platform in actual steel mills shows that the proposed method helps to improve the efficiency of product design process.

Funder

National Key R&D Program of China

Young Scientists Fund

Fundamental Research Funds for Central Universities of the Central South University

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Science Applications,Modeling and Simulation

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A data management perspective on building material classification: A systematic review;Journal of Building Engineering;2024-09

2. DuAK: Reinforcement Learning-Based Knowledge Graph Reasoning for Steel Surface Defect Detection;IEEE Transactions on Automation Science and Engineering;2023

3. Knowledge Management Through Product Lifecycle;Collaborative Knowledge Management Through Product Lifecycle;2023

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