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A performance evaluation method based on combination of knowledge graph and surrogate model

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Abstract

To satisfy the requirements of individual design and rapid performance evaluation of complex products, this paper proposes a hybrid approach to build a performance evaluation model and perform the rapid evaluation of design schemes. This approach consists of a surrogate model and knowledge graph (KG). Firstly, the KG of complex electromechanical products is established by Web Ontology Language to provide information about parts and evaluation indexes for the sampling process. It includes building ontology and writing inference and query rules at the framework level. Secondly, based on the sample points, a dynamics model is built and used for simulation. Using the Design of Experiments, the variables that have the greatest impact are found. The relevant variables will be input into the model to obtain the data set. According to the data set, a surrogate model based on the radial basis function is built as a performance evaluation model, which can improve computing efficiency to achieve evaluation results rapidly. In this study, the bogie design is used as a test case to evaluate the proposed method. And the results show that it can improve design efficiency for design issues such as part selection.

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Data availability

The data that support the findings of this study are available from the corresponding author, Guijie Liu or Honghui Wang upon reasonable request.

References

  • Abadi, A., Ben-Azza, H., & Sekkat, S. (2018). Improving integrated product design using SWRL rules expression and ontology-based reasoning. Procedia Computer Science, 127, 416–425.

    Article  Google Scholar 

  • Bastinos, A. Š, & Krisper, M. (2013). Multi-criteria decision making in ontologies. Information Sciences, 222, 593–610.

    Article  Google Scholar 

  • Chen, X., Jia, S., & Xiang, Y. (2020). A review: Knowledge reasoning over knowledge graph. Expert Systems with Applications, 141, 112948.

    Article  Google Scholar 

  • Cheng, W., Kasneci, G., Graepel, T., Stern, D., & Herbrich, R. (2011). Automated feature generation from structured knowledge. Paper presented at the Proceedings of the 20th ACM international conference on Information and knowledge management. https://doi.org/10.1145/2063576.2063779

  • Díaz-Manríquez, A., Toscano, G., & Coello Coello, C. A. (2017). Comparison of metamodeling techniques in evolutionary algorithms. Soft Computing, 21, 5647–5663. https://doi.org/10.1007/s00500-016-2140-z

    Article  Google Scholar 

  • Eum, K., Kang, M., Kim, G., Park, M. W., & Kim, J. K. (2013). Ontology-based modeling of process selection knowledge for machining feature. International Journal of Precision Engineering and Manufacturing, 14(10), 1719–1726. https://doi.org/10.1007/s12541-013-0231-7

    Article  Google Scholar 

  • Guo, L., Yan, F., Li, T., Yang, T., & Lu, Y. (2022). An automatic method for constructing machining process knowledge base from knowledge graph. Robotics and Computer-Integrated Manufacturing, 73, 102222.

    Article  Google Scholar 

  • Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E., Navigli, R., Neumaier, S., & Ngomo, A. C. (2021). Knowledge graphs. ACM Computing Surveys (CSUR), 54(4), 1–37. https://doi.org/10.1145/3447772

    Article  Google Scholar 

  • Hosder, S., Watson, L. T., Grossman, B., Mason, W. H., Kim, H., Haftka, R. T., & Cox, S. E. (2001). Polynomial response surface approximations for the multidisciplinary design optimization of a high speed civil transport. Optimization and Engineering, 2(4), 431–452. https://doi.org/10.1023/A:1016094522761

    Article  Google Scholar 

  • Huai-long, S., Ren, L., & Jing, Z. (2021). Review on domestic and foreign dynamics evaluation criteria of high-speed train. Journal of Traffic and Transportation Engineering, 21(1), 36. https://doi.org/10.19818/j.cnki.1671-1637.2021.01.002

    Article  Google Scholar 

  • Kamath, C. (2022). Intelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis. Machine Learning with Applications, 9, 100373.

    Article  Google Scholar 

  • Lin, D. K., Simpson, T. W., & Chen, W. (2001). Sampling strategies for computer experiments: design and analysis. International Journal of Reliability and Applications, 2(3), 209–240.

    Google Scholar 

  • O’Connor, M. J., & Das, A. K. (2009). SQWRL: a query language for OWL. Paper presented at the OWLED. https://ceur-ws.org/Vol-529/owled2009_submission_42.pdf

  • Panwar, V., Sharma, D. K., Kumar, K. P., Jain, A., & Thakar, C. (2021). Experimental investigations and optimization of surface roughness in turning of en 36 alloy steel using response surface methodology and genetic algorithm. Materials Today: Proceedings, 46, 6474–6481.

    Google Scholar 

  • Thombre, M. N., Preisig, H. A., & Addis, M. B. (2015). Developing surrogate models via computer based experiments. In Computer Aided Chemical Engineering (Vol. 37, pp. 641–646): Elsevier

  • Tiwari, S., Al-Aswadi, F. N., & Gaurav, D. (2021). Recent trends in knowledge graphs: theory and practice. Soft Computing, 25, 8337–8355. https://doi.org/10.1007/s00500-021-05756-8

    Article  Google Scholar 

  • Tseranidis, S., Brown, N. C., & Mueller, C. T. (2016). Data-driven approximation algorithms for rapid performance evaluation and optimization of civil structures. Automation in Construction, 72, 279–293.

    Article  Google Scholar 

  • Wan, S., Li, D., Gao, J., & Li, J. (2019). A knowledge based machine tool maintenance planning system using case-based reasoning techniques. Robotics and Computer-Integrated Manufacturing, 58, 80–96.

    Article  Google Scholar 

  • Wang, H., Xu, X., Zhang, C., & Hu, T. (2018). A hybrid approach to energy-efficient machining for milled components via STEP-NC. International Journal of Computer Integrated Manufacturing, 31(4–5), 442–456. https://doi.org/10.1080/0951192X.2017.1322220

    Article  Google Scholar 

  • Wang, X., Xia, Z., Zhou, X., Guo, Y., Gu, X., & Yan, H. (2021). Multiobjective path optimization for arc welding robot based on dmoea/d-et algorithm and proxy model. IEEE Transactions on Instrumentation and Measurement, 70, 1–13.

    Article  Google Scholar 

  • Wang, Z., & Wang, P. (2014). A maximum confidence enhancement based sequential sampling scheme for simulation-based design. Journal of Mechanical Design, 136(2), 021006.

    Article  Google Scholar 

  • Yahya, M., Breslin, J. G., & Ali, M. I. (2021). Semantic web and knowledge graphs for industry 4.0. Applied Sciences, 11(11), 5110.

    Article  Google Scholar 

  • Zhang, B., Wang, Q., Liu, X., Zu, L., & Yuan, H. (2022). Aeroelastic optimization design of composite materials blade based on RBF/ROM and CCA reliability analysis. Composite Structures, 300, 116162.

    Article  Google Scholar 

  • Zhang, Y., Luo, X., Zhang, H., & Sutherland, J. W. (2014). A knowledge representation for unit manufacturing processes. The International Journal of Advanced Manufacturing Technology, 73, 1011–1031. https://doi.org/10.1007/s00170-014-5864-x

    Article  Google Scholar 

  • Zhang, Z., Cao, L., Chen, X., Tang, W., Xu, Z., & Meng, Y. (2020). Representation learning of knowledge graphs with entity attributes. IEEE Access, 8, 7435–7441.

    Article  Google Scholar 

  • Zhao, D., Ma, M., & You, X.-Y. (2022). A Kriging-based adaptive parallel sampling approach with threshold value. Structural and Multidisciplinary Optimization, 65(8), 225. https://doi.org/10.1007/s00158-022-03310-0

    Article  Google Scholar 

  • Zheng, H., Feng, Y., Gao, Y., & Tan, J. (2018). A robust predicted performance analysis approach for data-driven product development in the industrial internet of things. Sensors, 18(9), 2871.

    Article  Google Scholar 

  • Zheng, P., Xia, L., Li, C., Li, X., & Liu, B. (2021). Towards Self-X cognitive manufacturing network: An industrial knowledge graph-based multi-agent reinforcement learning approach. Journal of Manufacturing Systems, 61, 16–26.

    Article  Google Scholar 

  • Zhou, B., Bao, J., Li, J., Lu, Y., Liu, T., & Zhang, Q. (2021). A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops. Robotics and Computer-Integrated Manufacturing, 71, 102160.

    Article  Google Scholar 

  • Zou, H., Wu, Q., & Zou, X. (2022). Research on optimization design of suspension parameters of railway vehicle bogies based on surrogate model. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-14022-4

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the financial support of National Key Research and Development Program of China (Grant No. 2020YFB1708003), and the Taishan Scholars Program of Shandong Province No. ts20190914.

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Authors and Affiliations

  1. School of Engineering, Ocean University of China, Qingdao, 266100, China

    Xu Han, Xinyu Liu, Honghui Wang & Guijie Liu

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  1. Xu Han
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  2. Xinyu Liu
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  3. Honghui Wang
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  4. Guijie Liu
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Correspondence to Honghui Wang or Guijie Liu.

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Han, X., Liu, X., Wang, H. et al. A performance evaluation method based on combination of knowledge graph and surrogate model. J Intell Manuf 35, 3441–3457 (2024). https://doi.org/10.1007/s10845-023-02210-4

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