Thinking process rules extraction for manufacturing process design
Received date: 2017-07-04
Revised date: 2017-11-13
Online published: 2017-12-25
Supported by
The work was supported by the National Key Technology R&D Program (Grant No.2015BAF17B01).
To realize the reuse of process design knowledge and improve the efficiency and quality of process design, a method for extracting thinking process rules for process design is proposed. An instance representation model of the process planning reflecting the thinking process of technicians is established to achieve an effective representation of the process documents. The related process attributes are extracted from the model to form the related events. The manifold learning algorithm and clustering analysis are used to preprocess the process instance data. A rule extraction mechanism of process design is introduced, which is based on the related events after dimension reduction and clustering, and uses the association rule mining algorithm to realize the similar process information extraction in the same cluster. Through the vectorization description of the related events, the final process design rules are formed. Finally, an example is given to evaluate the method of process design rules extraction.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0205-6/fulltext.html
Jing-Tao Zhou , Xiang-Qian Li , Ming-Wei Wang , Rui Niu , Qing Xu . Thinking process rules extraction for manufacturing process design[J]. Advances in Manufacturing, 2017 , 5(4) : 321 -334 . DOI: 10.1007/s40436-017-0205-6
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