Author_Institution :
Sch. of Mech. & Automotive Eng., South China Univ. of Technol., Guangzhou, China
Abstract :
In this paper, taking the decision attributes, including order number, product number, manhour and comprehensive evaluation into account, the decision tree model of discrete production and manufacture has been presented. And C4.5 algorithm has been used to construct the decision tree recursively in a topdown manner, in which the topmost node is the root node, each internal node denotes an attribute test, each branch represents an outcome of the test, and each leaf node represents classes. Then RFC (Remote Function Call) has been used to extract data from SAP (Systems Applications and Products in Data Processing) R/3 system to decision-making database, and DTS (Data Transformation Service) used to extract data from MES (Manufacturing Execution System) to decision-making database, which can supply useful data resource for data mining. Finally, a case study has been analyzed to show the application of the decision tree model. The decision tree has been implied to be references to the real discrete production and manufacture.
Keywords :
data mining; database management systems; decision making; decision trees; manufacturing data processing; production engineering computing; C4.5 algorithm; data mining; data transformation service; decision making database; decision tree model; manufacturing execution system; multivariety discrete production; order number; product number; remote function call; Data mining; Data processing; Databases; Decision making; Decision trees; Manufacturing processes; Production; Pulp manufacturing; Testing; Virtual manufacturing; C4.5 algorithm; data mining; decision tree; discrete production and manufacture;
