Data mining approach for knowledge-based process planning

被引:28
|
作者
Denkena, Berend [1 ]
Schmidt, Justin [1 ]
Krueger, Max [1 ]
机构
[1] Univ Hannover, Hannover Ctr Prod Technol PZH, Inst Prod Engn & Machine Tools IFW, D-30823 Garbsen, Germany
关键词
process planning; data mining; manufacturing data; process parameters; knowledge discovery; SURFACE-ROUGHNESS;
D O I
10.1016/j.protcy.2014.09.095
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Concepts like gentelligent products, smart objects or cyber-physical systems have already proven a high potential especially for decentralized production planning and control. In this context, decentralized communication, new sensor technologies and the increased application of simulation and monitoring systems lead to an enormous increase of manufacturing data. Additionally, a new approach for the assessment of manufacturing quality based on process signals from the machine tool is proposed, which provides current tool state and surface roughness information for every manufacturing process. In order to reuse and evaluate this data for knowledge-based process planning, an approach to manufacturing data collection and evaluation using data mining methods was developed. The advantages of the proposed data mining approach for process planning is demonstrated by an exemplary testing case. (C) 2014 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:406 / 415
页数:10
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