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This work proposes a novel approach for representing the thermal state of a machine tool. Modal analysis and K-Means clustering are used to extract the descriptor Proper Orthogonal Decomposition (POD) modes in the temeprature data which encode the thermal state of the machine tool. These descriptor POD modes identify the different conditions experienced during machining. These features are then used in determining whether any future observed data contains thermal states in the training process. The results obtained show that the approach is able to quantify the differences in the machine’s thermal state. These finding will be used to improve thermal error modelling in machine tools.
|Title of host publication||Laser metrology and machine performance XIV|
|Subtitle of host publication||14th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM & Robotic Performance : Lamdamap 2021|
|Editors||Liam Blunt, Andreas Archenti|
|Number of pages||10|
|Publication status||Published - 1 Dec 2021|
|Event||14th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance - Virtual, Virtual Online|
Duration: 10 Mar 2021 → 11 Mar 2021
Conference number: 14
|Name||Laser Metrology and Machine Performance XIV - 14th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2021|
|Conference||14th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance|
|Abbreviated title||LAMDAMAP 2021|
|Period||10/03/21 → 11/03/21|
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- 1 Active
Jiang, J., Martin, H., Longstaff, A., Kadirkamanathan, V., Turner, M. S., Keogh, P., Scott, P., McLeay, T., Blunt, L., Zeng, W., Huntley, J. M., Bills, P., Fletcher, S., Gao, F., Coupland, J. M., Kinnell, P., Mahfouf, M. & Mullineux, G.
1/10/16 → 30/09/23