Application of multi sensor data fusion based on principal component analysis and artificial neural network for machine tool thermal monitoring

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

LanguageEnglish
Title of host publicationLaser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015
Publishereuspen
Pages224-233
Number of pages10
ISBN (Electronic)9780956679055
Publication statusPublished - 2015
Event11th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance - , United Kingdom
Duration: 17 Mar 201518 Mar 2015
Conference number: 11

Conference

Conference11th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance
Abbreviated titleLAMDAMAP 2015
CountryUnited Kingdom
Period17/03/1518/03/15

Fingerprint

machine tools
multisensor fusion
principal components analysis
sensors
hysteresis
gantry cranes
ram
data compression
fuses
heat sources
positioning
temperature distribution
education
methodology
causes
temperature

Cite this

Potdar, A. A., Longstaff, A. P., Fletcher, S., & Mian, N. S. (2015). Application of multi sensor data fusion based on principal component analysis and artificial neural network for machine tool thermal monitoring. In Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015 (pp. 224-233). euspen.
Potdar, Akshay A. ; Longstaff, Andrew P. ; Fletcher, Simon ; Mian, Naeem S. / Application of multi sensor data fusion based on principal component analysis and artificial neural network for machine tool thermal monitoring. Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015. euspen, 2015. pp. 224-233
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title = "Application of multi sensor data fusion based on principal component analysis and artificial neural network for machine tool thermal monitoring",
abstract = "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.",
author = "Potdar, {Akshay A.} and Longstaff, {Andrew P.} and Simon Fletcher and Mian, {Naeem S.}",
year = "2015",
language = "English",
pages = "224--233",
booktitle = "Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015",
publisher = "euspen",

}

Potdar, AA, Longstaff, AP, Fletcher, S & Mian, NS 2015, Application of multi sensor data fusion based on principal component analysis and artificial neural network for machine tool thermal monitoring. in Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015. euspen, pp. 224-233, 11th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance, United Kingdom, 17/03/15.

Application of multi sensor data fusion based on principal component analysis and artificial neural network for machine tool thermal monitoring. / Potdar, Akshay A.; Longstaff, Andrew P.; Fletcher, Simon; Mian, Naeem S.

Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015. euspen, 2015. p. 224-233.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Application of multi sensor data fusion based on principal component analysis and artificial neural network for machine tool thermal monitoring

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AB - 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.

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M3 - Conference contribution

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Potdar AA, Longstaff AP, Fletcher S, Mian NS. Application of multi sensor data fusion based on principal component analysis and artificial neural network for machine tool thermal monitoring. In Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015. euspen. 2015. p. 224-233