RT Journal Article
T1 A statistical data-based approach to instability detection and wear prediction in radial turning processes
A1 Jimenez Cortadi, Alberto
A1 Irigoien, Itziar
A1 Boto, Fernando
A1 Sierra, Basilio
A1 Suarez, Alfredo
A1 Galar, Diego
AB Radial turning forces for tool-life improvements are studied, with the emphasis on predictive rather than preventive maintenance. A tool for wear prediction in various experimental settings of instability is proposed through the application of two statistical approaches to process data on tool-wear during turning processes: three sigma edit rule analysis and Principal Component Analysis (PCA). A Linear Mixed Model (LMM) is applied for wear prediction. These statistical approaches to instability detection generate results of acceptable accuracy for delivering expert opinion. They may be used for on-line monitoring to improve the processing of different materials. The LMM predicted significant differences for tool wear when turning different alloys and with different lubrication systems. It also predicted the degree to which the turning process could be extended while conserving stability. Finally, it should be mentioned that tool force in contact with the material was not considered to be an important input variable for the model.
YR 2018
FD 2018
LA eng
NO Jimenez Cortadi , A , Irigoien , I , Boto , F , Sierra , B , Suarez , A & Galar , D 2018 , ' A statistical data-based approach to instability detection and wear prediction in radial turning processes ' , Eksploatacja i Niezawodnosc - Maintenance and Reliability , vol. 20 , no. 3 , pp. 405-412 . https://doi.org/10.17531/ein.2018.3.8
NO Publisher Copyright: © 2018, Polish Academy of Sciences Branch Lublin. All rights reserved.
DS TECNALIA Publications
RD 1 jul 2024