A knowledge generation mechanism of machining process planning using cloud technology

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


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.


Intelligent process planning Knowledge generation Cloud technology Smart manufacturing 



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


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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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