Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

被引:86
作者
Nakai, Mauricio Eiji [1 ]
Aguiar, Paulo Roberto [1 ]
Guillardi, Hildo, Jr. [1 ]
Bianchi, Eduardo Carlos [1 ]
Spatti, Danilo Hernane [1 ]
D'Addona, Doriana Marilena [2 ]
机构
[1] Univ Estadual Paulista, UNESP, Sch Engn, Dept Elect Mech Engn, BR-17033360 Bauru, SP, Brazil
[2] Univ Naples Federico II, Dept Mat & Prod Engn, I-80125 Naples, Italy
关键词
Ceramic grinding; Intelligent systems; Neural networks; Advanced ceramics; IN-PROCESS MEASUREMENT; ACOUSTIC-EMISSION; WHEEL WEAR; SURFACE-ROUGHNESS; SYSTEM; COMPENSATION; PREDICTION; MECHANISM; FORCE; POWER;
D O I
10.1016/j.eswa.2015.05.008
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Grinding wheel wear, which is a very complex phenomenon, causes changes in most of the shapes and properties of the tool during machining, reducing the efficiency of the grinding operation and impairing workpiece quality. Therefore, monitoring the condition of the tool during the grinding process plays a key role in the quality of workpieces being manufactured. In this study, diamond tool wear was estimated during the grinding of advanced ceramics using intelligent systems composed of four types of neural networks. Experimental tests were performed on a surface grinding machine and tool wear was measured by the imprint method throughout the tests. Acoustic emission and cutting power signals were acquired during the tests and statistics were obtained from these signals. Training and validating algorithms were developed for the intelligent systems in order to automatically obtain the best estimation models. The combination of signals and statistics along with the intelligent systems brings an innovative aspect to the grinding process. The results indicate that the models are highly successful in estimating tool wear. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:7026 / 7035
页数:10
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