A knowledge generation mechanism of machining process planning using cloud technology

  • Yan Yang
  • Tianliang Hu
  • Yingxin Ye
  • Wenbin Gao
  • Chengrui Zhang
Original Research
  • 66 Downloads

Abstract

Nowadays, machining task becomes more and more complex and customized product needs small quantity manufacturing, which come up with higher demands for CNC machine tools to realize smart manufacturing. However, current process planning is generally made by process planners depending on their professional knowledge and experience rather than CNC machine tools. Several knowledge bases are developed to make CNC machine tools more intelligent. In process planning, a proper methodology for capturing knowledge is essential for constructing a knowledge base to support process planning decision making. Therefore, this research presents a knowledge generation mechanism to generate machining solutions taking advantages of Map/Reduce framework to handle large-scale data. Knowledge unit is defined, which consists of generalized feature and operation to store process knowledge. Workingstep is as the instantiation of knowledge unit, which is stored in database as the data foundation for query and reasoning. According to the top-down idea of hierarchical clustering algorithm, all workingsteps are clustered to get several similar workingsteps. Then query engine tries to inquiry workingsteps which contain same generalized features to the task, if there is no workingstep fulfilling requirements, reasoning engine is dealing with this situation to generate new workingsteps. After that, a machining solution is generated according to reasoning rules for working sequencing. Finally, a test example is given to validate the proposed approach and result is presented to demonstrate and verify the generation mechanism.

Keywords

Intelligent process planning Knowledge generation Cloud technology Smart manufacturing 

Notes

Acknowledgements

The work is supported by National Natural Science Foundation of China (Grant No. 51405270).

References

  1. Amaitik SM, Kiliç SE (2007) An intelligent process planning system for prismatic parts using STEP features. Int J Adv Manuf Technol 31:978–993CrossRefGoogle Scholar
  2. Cakir MC, Irfan O, Cavdar K (2005) An expert system approach for die and mold making operations. Robot Comput Integr Manuf 21:175–183CrossRefGoogle Scholar
  3. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27CrossRefGoogle Scholar
  4. Chi YL (2010) Rule-based ontological knowledge base for monitoring partners across supply networks. Expert Syst Appl 37:1400–1407CrossRefGoogle Scholar
  5. Deb S, Ghosh K, Paul S (2006) A neural network based methodology for machining operations selection in computer-aided process planning for rotationally symmetrical parts. J Intell Manuf 17:557–569CrossRefGoogle Scholar
  6. Deb S, Parra-Castillo JR, Ghosh K (2011) An integrated and intelligent computer-aided process planning methodology for machined rotationally symmetrical parts Castillo, p 13Google Scholar
  7. Lee SW, Song JY, Lee HK (2008) Construction and operation of a knowledge base on intelligent machine tools 7Google Scholar
  8. Li X, Gao L, Shao X (2012) An active learning genetic algorithm for integrated process planning and scheduling. Expert Syst Appl 39:6683–6691CrossRefGoogle Scholar
  9. Liu XJ, Yi H, Ni ZH (2013) Application of ant colony optimization algorithm in process planning optimization. Springer, New YorkGoogle Scholar
  10. Mell P, Grance T (2009) The NIST definition of cloud computing. Commun ACM 53:50–50Google Scholar
  11. Molano JIR, Lovelle JMC, Montenegro CE, Granados JJR, Crespo RG (2017) Metamodel for integration of internet of things, social networks, the cloud and industry 4.0. J Ambient Intell Humaniz Comput 1–15 (In Press)Google Scholar
  12. Nassehi A, Newman ST, Allen RD (2006) STEP-NC compliant process planning as an enabler for adaptive global manufacturing. Robot Comput Integr Manuf 22:456–467CrossRefGoogle Scholar
  13. Pallis G (2010) Cloud computing: the new frontier of internet computing. IEEE Internet Comput 14:70–73CrossRefGoogle Scholar
  14. Radwan A (2000) A practical approach to a process planning expert system for manufacturing processes. J Intell Manuf 11:75–84CrossRefGoogle Scholar
  15. Ren L, Zhang L, Tao F, Zhao C, Chai X, Zhao X (2015) Cloud manufacturing: from concept to practice. Enterp Inf Syst 9:186–209CrossRefGoogle Scholar
  16. Saucedo-Martínez JA, Pérez-Lara M, Marmolejo-Saucedo JA, Salais-Fierro TE, Vasant P (2017) Industry 4.0 framework for management and operations: a review. J Ambient Intell Humaniz Comput 1–13Google Scholar
  17. Suh SH, Cheon SU (2002) A Framework for an Intelligent CNC and data model. Int J Adv Manuf Technol 19:727–735CrossRefGoogle Scholar
  18. Tao F, Guo H, Zhang L, Cheng Y (2012) Modelling of combinable relationship-based composition service network and the theoretical proof of its scale-free characteristics. Enterp Inf Syst 6:373–404CrossRefGoogle Scholar
  19. Tao F, Cheng Y, Xu LD, Zhang L, Li BH (2014a) CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans Industr Inf 10:1435–1442CrossRefGoogle Scholar
  20. Tao F, Zuo Y, Xu LD, Zhang L (2014b) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Industr Inf 10:1547–1557CrossRefGoogle Scholar
  21. Tao F, Cheng J, Cheng Y, Gu S, Zheng T, Yang H (2017a) SDMSim: a manufacturing service supply–demand matching simulator under cloud environment. Robot Comput Integr Manuf 45:34–46CrossRefGoogle Scholar
  22. Tao F, Cheng J, Qi Q, Tao F, Cheng J, Qi Q (2017b) IIHub: an industrial internet-of-things hub towards smart manufacturing based on cyber-physical system. IEEE Trans Industr Inf 1–1Google Scholar
  23. Tao F, Qi Q, Liu A, Kusiak A (2018) Data-driven smart manufacturing. J Manuf SystGoogle Scholar
  24. Wang K (1998) An integrated intelligent process planning system (IIPPS) for machining. J Intell Manuf 9:503–514CrossRefGoogle Scholar
  25. Wang J, Shu QL (2009) A framework of new generation intelligent CNC system. Appl Mech Mater 16–19:896–899CrossRefGoogle Scholar
  26. Wang H, Xu X, Zhang C, Hu T (2017a) A hybrid approach to energy-efficient machining for milled components via STEP-NC. Int J Comput Integr Manuf 1–15Google Scholar
  27. Wang L, Chen X, Liu Q (2017b) A lightweight intelligent manufacturing system based on cloud computing for plate production. Mob Netw Appl 1–12Google Scholar
  28. Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: methods and applications. J Manuf SystGoogle Scholar
  29. Xu XW, Newman ST (2006) Making CNC machine tools more open, interoperable and intelligent—a review of the technologies. Comput Industry 57:141–152CrossRefGoogle Scholar
  30. Ye Y, Hu T, Zhang C, Luo W (2016) Design and development of a CNC machining process knowledge base using cloud technology. Int J Adv Manuf Technol 1–13Google Scholar
  31. Ye Y, Hu T, Yang Y, Zhu W, Zhang C (2018) A knowledge based intelligent process planning method for controller of computer numerical control machine tools. J Intell Manuf.  https://doi.org/10.1007/s10845-018-1401-3 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China
  2. 2.Key Laboratory of High Efficiency and Clean Mechanical Manufacture (Shandong University)Ministry of EducationJinanPeople’s Republic of China
  3. 3.National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University)JinanPeople’s Republic of China

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