Due to the various heat sources on a machine tool, there exists a complex temperature distribution across its structure. This causes an inherent thermal hysteresis which is undesirable as it affects the systematic tool -to-workpiece positioning capability. To monitor this, two physical quantities (temperature and strain) are measured at multiple locations. This article is concerned with the use of Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) to fuse this potentially large amount of data from multiple sources. PCA reduces the dimensionality of the data and thus reduces training time for the ANN which is being used for thermal modelling. This paper shows the effect of different levels of data compression and the application of rate of change of sensor values to reduce the effect of system hysteresis. This methodology has been successfully applied to the ram of a 5-axis gantry machine with 90 % correlation to the measured displacement.
|Title of host publication||Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015|
|Number of pages||10|
|Publication status||Published - 2015|
|Event||11th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance - Huddersfield, United Kingdom|
Duration: 17 Mar 2015 → 18 Mar 2015
Conference number: 11
|Conference||11th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance|
|Abbreviated title||LAMDAMAP 2015|
|Period||17/03/15 → 18/03/15|
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- Department of Engineering and Technology - Subject Area Leader (Mechanical and Automotive Engineering)
- School of Computing and Engineering
- Centre for Precision Technologies - Member
- Centre for Thermofluids, Energy Systems and High-Performance Computing - Associate Member