ANN tool wear modelling in the machining of nickel superalloy industrial products

被引：47

作者：

D'Addona, D. ^{[1
]}

Segreto, T. ^{[1
]}

Simeone, A. ^{[1
]}

Teti, R. ^{[1
]}

机构：

[1] Univ Naples Federico II, Dept Mat & Prod Engn, Fraunhofer Joint Lab Excellence Adv Prod Technol, Piazzale Tecchio 80, I-80125 Naples, Italy

来源：

CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY | 2011年 / 4卷 / 01期

关键词：

Inconel; 718; Turning; Tool wear; Artificial neural networks;

D O I：

10.1016/j.cirpj.2011.07.003

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Cognitive modelling of tool wear progress based on neural network supervised training, derived from investigational tool wear measurements during industrial turning of Inconel 718 aircraft engine products, is employed to obtain a dependable trend of tool wear curves for optimal utilisation of tool life and step increase of productivity, while preserving the surface integrity of the machined parts. (C) 2011 CIRP.

引用

收藏

页码：33 / 37

页数：5

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