Abstract
Coordinate measuring machines (CMMs) are complex measuring systems that are widely used in manufacturing industry for form, size, position, and orientation assessment. In essence, these systems collect a set of individual data points that in practice is often a relatively small sample of an object. Their software then processes these points in order to produce a geometric result or to establish a local coordinate system from datum features. The subject of CMM evaluation is a broad and multifaceted one. This paper is concerned with the uncertainty in the coordinates of each point within the measuring volume of the CMM. Therefore, a novel method for measurement uncertainty evaluation using limitedsize data sets is conceived and developed. The proposed method is based on a Bayesian regularized artificial neural network (BRANN) model consisting of three inputs and one output. The inputs are: The nominal coordinates; the ambient temperature; and the temperature of the workpiece. The output is the measured (actual) coordinates. An algorithm is developed and implemented before training the BRANN in order to map each nominal coordinate associated with the other inputs to the target coordinate. For validation the model is trained using a relatively small sample size of ten data sets to predict the variability of a larger sample size of ninety data sets. The calculated uncertainty is improved by more than 80% using the predicted variability compared to the uncertainty from the limited sample data set.
Language  English 

Title of host publication  Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016 
Publisher  euspen 
Number of pages  2 
ISBN (Electronic)  9780956679086 
Publication status  Published  2016 
Event  16th International Conference of the European Society for Precision Engineering and Nanotechnology  East Midlands Conference Centre, Nottingham, United Kingdom Duration: 30 May 2016 → 3 Jun 2016 Conference number: 16 https://www.euspen.eu/events/16thinternationalconferenceexhibition/ (Link to Conference Website) 
Conference
Conference  16th International Conference of the European Society for Precision Engineering and Nanotechnology 

Abbreviated title  EUSPEN 2016 
Country  United Kingdom 
City  Nottingham 
Period  30/05/16 → 3/06/16 
Other  This event offers the possibility to see latest advances in traditional precision engineering fields such as metrology, ultra precision machining, additive and replication processes, precision mechatronic systems & control and precision cutting processes. 
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A novel method based on Bayesian regularized artificial neural networks for measurement uncertainty evaluation. / Papananias, Moschos; Fletcher, Simon; Longstaff, Andrew; Mengot, Azibananye.
Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016. euspen, 2016.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY  GEN
T1  A novel method based on Bayesian regularized artificial neural networks for measurement uncertainty evaluation
AU  Papananias, Moschos
AU  Fletcher, Simon
AU  Longstaff, Andrew
AU  Mengot, Azibananye
PY  2016
Y1  2016
N2  Coordinate measuring machines (CMMs) are complex measuring systems that are widely used in manufacturing industry for form, size, position, and orientation assessment. In essence, these systems collect a set of individual data points that in practice is often a relatively small sample of an object. Their software then processes these points in order to produce a geometric result or to establish a local coordinate system from datum features. The subject of CMM evaluation is a broad and multifaceted one. This paper is concerned with the uncertainty in the coordinates of each point within the measuring volume of the CMM. Therefore, a novel method for measurement uncertainty evaluation using limitedsize data sets is conceived and developed. The proposed method is based on a Bayesian regularized artificial neural network (BRANN) model consisting of three inputs and one output. The inputs are: The nominal coordinates; the ambient temperature; and the temperature of the workpiece. The output is the measured (actual) coordinates. An algorithm is developed and implemented before training the BRANN in order to map each nominal coordinate associated with the other inputs to the target coordinate. For validation the model is trained using a relatively small sample size of ten data sets to predict the variability of a larger sample size of ninety data sets. The calculated uncertainty is improved by more than 80% using the predicted variability compared to the uncertainty from the limited sample data set.
AB  Coordinate measuring machines (CMMs) are complex measuring systems that are widely used in manufacturing industry for form, size, position, and orientation assessment. In essence, these systems collect a set of individual data points that in practice is often a relatively small sample of an object. Their software then processes these points in order to produce a geometric result or to establish a local coordinate system from datum features. The subject of CMM evaluation is a broad and multifaceted one. This paper is concerned with the uncertainty in the coordinates of each point within the measuring volume of the CMM. Therefore, a novel method for measurement uncertainty evaluation using limitedsize data sets is conceived and developed. The proposed method is based on a Bayesian regularized artificial neural network (BRANN) model consisting of three inputs and one output. The inputs are: The nominal coordinates; the ambient temperature; and the temperature of the workpiece. The output is the measured (actual) coordinates. An algorithm is developed and implemented before training the BRANN in order to map each nominal coordinate associated with the other inputs to the target coordinate. For validation the model is trained using a relatively small sample size of ten data sets to predict the variability of a larger sample size of ninety data sets. The calculated uncertainty is improved by more than 80% using the predicted variability compared to the uncertainty from the limited sample data set.
KW  Bayesian regularized artificial neural network (BRANN)
KW  Coordinate measuring machine (CMM)
KW  Uncertainty of measurement
UR  http://www.scopus.com/inward/record.url?scp=84984636154&partnerID=8YFLogxK
UR  http://www.euspen.eu/events/16thinternationalconferenceexhibition/
M3  Conference contribution
BT  Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016
PB  euspen
ER 