A Data Services-Based Quality Analysis System for the Life Cycle of Tire Production

  • Yuliang Shi
  • Yu Chen
  • Shibin Sun
  • Lei Liu
  • Lizhen CuiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9936)


In the background of actual production demands, we develop data services to solve the problem of “information isolated island” in the tire production for achieving the unified management for data from diverse production systems. Based on the data services, the management system for tire production is designed. The system uses the decision tree algorithm with data fitting and data screening technologies to analyze the data from the whole production process and realize the forecast of product quality and defects analysis. The system has been applied to the production by Shandong Linglong Tire Co., Ltd. The practice has proved that our data services and system not only improve the tire pass rate and production efficiency, but also help enterprises to achieve the efficient management of production. In addition, we apply the service to the actual manufacturing industry, which plays a positive role in the promotion and improvement of service application.


Data services Data extraction Quality analysis Big data Tire 



The research work was supported by the National Natural Science Foundation of China under Grant No. 61572295, 61272241, the Innovation Methods Work Special Project No. 2015IM010200, the TaiShan Industrial Experts Programme of Shandong Province, the Natural Science Foundation of Shandong Province under Grant No. ZR2014FM031, ZR2013FQ014, the Shandong Province Science and Technology Major Special Project No. 2015ZDJQ01002, 2015ZDXX0201B03, 2015ZDXX0201A04, the Shandong Province Key Research and Development Plan No. 2015GGX101015, the Fundamental Research Funds of Shandong University No. 2015JC031.


  1. 1.
  2. 2.
  3. 3.
    Ruan, J.F., Yu, W.J., Yang, Y., Hu, JB.: Design and realize of tire production process monitoring system based on cyber-physical systems. In: 2015 International Conference on Computer Science and Mechanical Automation, Hangzhou, pp. 175–179 (2015)Google Scholar
  4. 4.
    Abou-Ali, M.G., Khamis, M.: TIREDDX: an integrated intelligent defects diagnostic system for tire production and service. Expert Syst. Appl. 24(3), 247–259 (2003)CrossRefGoogle Scholar
  5. 5.
    Zimmermann, A., Pretz, M., Zimmermann, G., Firesmith, D.G., Petrov, L., El-Sheikh, E.: Towards service-oriented enterprise architectures for big data applications in the cloud. In: 2013 17th IEEE International Enterprise Distributed Object Computing Conference Workshops, pp. 130–135. IEEE, Vancouver (2013)Google Scholar
  6. 6.
    Li, S., Zhang, Q.Q., Chen, S.B., Tan, W.A., Tang, A.Q., Hu, X.M.: Reliable service computing platform architecture for cross-organizational workflows. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3066–3701. IEEE, San Diego (2014)Google Scholar
  7. 7.
    Lin, F., Yang, L.Q., Zeng, W.H., Wang, Y.: Service oriented CSOMA model for risk evaluation of cloud computing system. Metall. Min. Ind. 7(9), 281–289 (2015)Google Scholar
  8. 8.
    Lv, C., Jiang, W., Hu, S.L.: Dynamic environment-oriented self-adaptation of service composition. Chin. J. Comput. 39(2), 305–322 (2016)Google Scholar
  9. 9.
    Di Cosmo, R., Eiche, A., Mauro, J., Zacchiroli, S., Zavattaro, G., Zwolakowski, J.: Automatic deployment of services in the cloud with aeolus blender. In: Barros, A., Grigori, D., Narendra, N.C., Dam, H.K. (eds.) ICSOC 2015. LNCS, vol. 9435, pp. 397–411. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-48616-0_28 CrossRefGoogle Scholar
  10. 10.
    Ichikawa, K., Yada, K., Washio, T.: Development of data mining platform MUSASHI towards service computing. In: 2010 IEEE International Conference on Granular Computing, pp. 235–240. IEEE, San Jose (2010)Google Scholar
  11. 11.
    Hou, F., Mao, X.J., Wu, W., Liu, L., Panneerselvam, J.: A cloud-oriented services self-management approach based on multi-agent system technique. In: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp. 261–268. IEEE/ACM, London (2015)Google Scholar
  12. 12.
    Yu, W.: A service components pipeline model based on multi-source data extraction. Signal Process. 124, 5–12 (2015)Google Scholar
  13. 13.
    Tao, Y., Li, Y., Wang, Y.Q., Ma, Y.Y.: On-line point cloud data extraction algorithm for spatial scanning measurement of irregular surface in copying manufacture. Int. J. Adv. Manuf. Technol. (2016). doi: 10.1007/s00170-016-8592-6
  14. 14.
    Luor, D.C.: A comparative assessment of data standardization on support vector machine for classification problems. Intell. Data Anal. 19(3), 529–546 (2015)CrossRefGoogle Scholar
  15. 15.
    Chatterjee, S., Kyasa, R.C., Gopidi, N.R., Rav,i P.P.: Data standardization and analysis model for enhanced global productivity. In: 14th Symposium on International Automotive Technology, Pune (2015)Google Scholar
  16. 16.
    Ma, Y.H., Kong, F.S., Liu, Z.H., Zhu, X.Y., Pan, Y.C.: Processing quality forecast and diagnosis for gear-box shell production line in manufacturing system. In: 2012 International Conference on Applied Mechanics and Manufacturing System, Guangzhou (2013)Google Scholar
  17. 17.
    Zhao, C.H.: A quality-relevant sequential phase partition approach for regression modeling and quality prediction analysis in manufacturing processes. IEEE Trans. Autom. Sci. Eng. 11(4), 983–991 (2013)CrossRefGoogle Scholar
  18. 18.
    Ruhaizan, I., Zalinda, O., Azuraliza, A.B.: Associative prediction model and clustering for product forecast data. In: 2010 10th International Conference on Intelligent Systems Design and Applications, Cairo, pp. 1459–1464 (2010)Google Scholar
  19. 19.
    Du, S.: Improved product quality through causality analysis in product engineering. Int. J. Mater. Struct. Integrity 3(1), 47–65 (2009)CrossRefGoogle Scholar
  20. 20.
    Wu, R.C., Chen, R.S.: The application of data mining technology for intelligent product quality analysis improvement system. WSEAS Trans. Inf. Sci. Appl. 4(4), 693–699 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yuliang Shi
    • 1
  • Yu Chen
    • 1
  • Shibin Sun
    • 1
  • Lei Liu
    • 1
  • Lizhen Cui
    • 1
    Email author
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina

Personalised recommendations