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

页码：631 / 649

页数：19

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