Decision rule mining for machining method chains based on rough set theory

被引：4
|
作者：
Wang, Rui [1 ]
Guo, Xiangyu [2 ]
Zhong, Shisheng [2 ]
Peng, Gaolei [1 ]
Wang, Lin [1 ]
机构：
[1] Harbin Inst Technol, Sch Ocean Engn, 2 Wenhuaxi Rd, Weihai 264200, Peoples R China
[2] Harbin Inst Technol, Sch Mechatron Engn, 92 West Dazhi St, Harbin 150000, Peoples R China
来源：
JOURNAL OF INTELLIGENT MANUFACTURING | 2022年 / 33卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Machining method; Process planning; Rule mining; Rough set theory; Derivation rules; TOOL;
D O I：
10.1007/s10845-020-01692-w
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Decision rules for machining method chains mined from historical machining documents can help technologists quickly design new machining method chains. However, the main factor that limits the practical application of existing rough set models is that the boundary regions are too large. Therefore, a decomposition-reorganization method (DRM) is proposed to mine rules for machining method chains. First, binary coding is used to decompose the existing machining method chains, and the decision rules for a single machining method are mined based on rough set reduction. Then, machining method chains are obtained by reorganizing the machining methods in accordance with the decision rules. DRM can eliminate the boundary regions without human intervention and recommend machining method chains for all features whose parameters have appeared in historical machining documents. Finally, three types of shell parts are used to verify the effectiveness of DRM.
引用
收藏
页码：799 / 807
页数：9
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