Infrastructure monitoring and quality diagnosis in CNC machining: A review

被引：20
|
作者：
Ntemi, Myrsini [1 ]
Paraschos, Spyridon [1 ]
Karakostas, Anastasios [1 ]
Gialampoukidis, Ilias [1 ]
Vrochidis, Stefanos [1 ]
Kompatsiaris, Ioannis [1 ]
机构：
[1] Ctr Res & Technol Hellas, Informat Technol Inst, 6th Km Charilaou Thermi, Thessaloniki 57001, Greece
来源：
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY | 2022年 / 38卷
关键词：
SmartCNCmilling; Infrastructuremonitoring; Qualitydiagnosis; TOOL WEAR ESTIMATION; SEMI-MARKOV MODEL; SURFACE-ROUGHNESS PREDICTION; EXTENDED KALMAN FILTER; USEFUL LIFE PREDICTION; CHATTER DETECTION; NUMERICAL-SIMULATION; FEATURE-SELECTION; PARTICLE FILTER; FAULT-DIAGNOSIS;
D O I：
10.1016/j.cirpj.2022.06.001
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Infrastructure monitoring and rapid quality diagnosis comprise the key solutions to achieve zero-defect smart manufacturing. The most fundamental systems in manufacturing industries are computer numerical controlled (CNC) tools. Automating and optimizing their functionality is a highly challenging task because complex dynamics and non-linear relationships govern the overall machining operations. Recent scientific advances in machining processes, incorporate intelligence in CNC tools to improve both the reliability and the productivity of the real-time cutting operations, while reducing waste and cost. This study extensively reviews these advances focusing on three fundamental aspects: Surface roughness prediction, tool wear prediction, and chatter detection in CNC cutting processes.(c) 2022 CC_BY_NC_ND_4.0
引用
收藏
页码：631 / 649
页数：19
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