Modelling and optimization of grinding processes

被引:57
|
作者
Brinksmeier, E
Tonshoff, HK
Czenkusch, C
Heinzel, C
机构
[1] Stiftung Inst Werkstofftech, Div Mfg Technol, D-28359 Bremen, Germany
[2] Univ Hannover, Inst Fertigungstech & Spanende Werkzeugmaschinen, D-30159 Hannover, Germany
关键词
grinding modelling; neural networks; fuzzy set theory; genetic algorithm; grinding information system;
D O I
10.1023/A:1008908724050
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The paper describes different methods for modelling and optimization of grinding processes. First the process and product quality characterizing quantities have to be measured. Afterwards different model types, e.g. physical-empirical basic grinding models as well as empirical process models based on neural networks, fuzzy set theory and standard multiple regression methods, are discussed for an off-line process conceptualization and optimization using a genetic algorithm. The assessment of grinding process :results, which build the individuals in the genetic algorithm's population, is carried out using a target tree method. The methods presented are integrated into an existing grinding information system, which is part of a three control loop system for quality assurance.
引用
收藏
页码:303 / 314
页数:12
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