Dry turning of Ti6A14V: tool wear curve reconstruction based on cognitive sensor monitoring

被引：16

作者：

Caggiano, Alessandra ^{[1
,2
]}

Napolitano, Francesco ^{[1
,3
]}

Teti, Roberto ^{[1
,3
]}

机构：

[1] Fraunhofer Joint Lab Excellence Adv Prod Technol, Naples, Italy

[2] Univ Naples Federico II, Dept Ind Engn, Ple Tecchio 80, I-80125 Naples, Italy

[3] Univ Naples Federico II, Dept Chem Mat & Ind Prod Engn, Ple Tecchio 80, I-80125 Naples, Italy

来源：

10TH CIRP CONFERENCE ON INTELLIGENT COMPUTATION IN MANUFACTURING ENGINEERING - CIRP ICME '16 | 2017年 / 62卷

关键词：

Sensor monitoring; Titanium alloy; Dry turning; Tool wear; SURFACE INTEGRITY; TITANIUM-ALLOY; MACHINABILITY;

D O I：

10.1016/j.procir.2017.03.046

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Dry turning of Ti6A14V alloy is a hard process due to the low thermal conductivity and flexibility of this difficult-to-machine material, generating very high temperatures in both workpiece and tool cutting edge, rapid tool wear and high vibrations during machining. The use of coolants offers the advantage to reduce the high process temperatures, but is not suitable in a green technology perspective due to its high environmental impact. With the aim to allow for dry turning of Ti6A14V alloy, monitoring of tool wear during the process is required. To achieve this goal, a cognitive sensor monitoring procedure based on the acquisition and processing of force, acoustic emission and vibration sensor signals is implemented, allowing for an accurate tool wear curve reconstruction. (C) 2017 Published by Elsevier B.V.

引用

页码：203 / 208

页数：6

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