## 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 limited-size 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.

Original language | English |
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Title of host publication | Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016 |

Editors | P. Bointon, R. K. Leach, N. Southon |

Publisher | Euspen |

Pages | 97-98 |

Number of pages | 2 |

ISBN (Electronic) | 9780956679086 |

Publication status | Published - 30 May 2016 |

Event | 16th International Conference of the European Society for Precision Engineering and Nanotechnology - Nottingham, United Kingdom Duration: 30 May 2016 → 3 Jun 2016 |

### Conference

Conference | 16th International Conference of the European Society for Precision Engineering and Nanotechnology |
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Abbreviated title | EUSPEN 2016 |

Country/Territory | United Kingdom |

City | Nottingham |

Period | 30/05/16 → 3/06/16 |

## Keywords

- Bayesian regularized artificial neural network (BRANN)
- coordinate measuring machine (CMM)
- uncertainty of measurement
- UKRI
- EPSRC
- EP/I033424/1