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Alignment measurements uncertainties for large assemblies using probabilistic analysis techniques
Big science and ambitious industrial projects continually push forward with technical requirements beyond the grasp of conventional engineering techniques. Example of those are ultra-high precision requirements in the field of celestial telescopes, particle accelerators and aerospace industry. Such...
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Lenguaje: | eng |
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2017
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Acceso en línea: | http://cds.cern.ch/record/2299206 |
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author | Doytchinov, Iordan Petrov |
author_facet | Doytchinov, Iordan Petrov |
author_sort | Doytchinov, Iordan Petrov |
collection | CERN |
description | Big science and ambitious industrial projects continually push forward with technical requirements beyond the grasp of conventional engineering techniques. Example of those are ultra-high precision requirements in the field of celestial telescopes, particle accelerators and aerospace industry. Such extreme requirements are limited largely by the capability of the metrology used, namely, it’s uncertainty in relation to the alignment tolerance required. The current work was initiated as part of Maria Curie European research project held at CERN, Geneva aiming to answer those challenges as related to future accelerators requiring alignment of 2 m large assemblies to tolerances in the 10 µm range. The thesis has found several gaps in current knowledge limiting such capability. Among those was the lack of application of state of the art uncertainty propagation methods in alignment measurements metrology. Another major limiting factor found was the lack of uncertainty statements in the thermal errors compensations applied to assembly’s alignment metrology. A novel methodology was developed by which mixture of probabilistic modelling and high precision traceable reference measurements were used to quantify both measurement and thermal models compensation uncertainty accurately. Results have shown that the suggested methodology can accurately predict CMM specific measurement uncertainty as well as thermal drift compensation made by empirical, FEM and FEM metamodels. The CMM task specific measurement uncertainties made at metrology laboratory were validated to be of maximum 7.96 µm (1σ) for the largest 2 m assemblies. The analysis of the results further showed how using this method a ‘virtual twins’ of the engineering structures can be calibrated with known uncertainty of thermal drift prediction behaviour in the micrometric range. Namely the Empirical, FEM and FEM Metamodels uncertainties of predictions were validated to be of maximum: 8.7 µm (1σ), 11.28 µm (1σ) and 12.24 µm (1σ). |
id | cern-2299206 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2017 |
record_format | invenio |
spelling | cern-22992062019-09-30T06:29:59Zhttp://cds.cern.ch/record/2299206engDoytchinov, Iordan PetrovAlignment measurements uncertainties for large assemblies using probabilistic analysis techniquesEngineeringAccelerators and Storage RingsBig science and ambitious industrial projects continually push forward with technical requirements beyond the grasp of conventional engineering techniques. Example of those are ultra-high precision requirements in the field of celestial telescopes, particle accelerators and aerospace industry. Such extreme requirements are limited largely by the capability of the metrology used, namely, it’s uncertainty in relation to the alignment tolerance required. The current work was initiated as part of Maria Curie European research project held at CERN, Geneva aiming to answer those challenges as related to future accelerators requiring alignment of 2 m large assemblies to tolerances in the 10 µm range. The thesis has found several gaps in current knowledge limiting such capability. Among those was the lack of application of state of the art uncertainty propagation methods in alignment measurements metrology. Another major limiting factor found was the lack of uncertainty statements in the thermal errors compensations applied to assembly’s alignment metrology. A novel methodology was developed by which mixture of probabilistic modelling and high precision traceable reference measurements were used to quantify both measurement and thermal models compensation uncertainty accurately. Results have shown that the suggested methodology can accurately predict CMM specific measurement uncertainty as well as thermal drift compensation made by empirical, FEM and FEM metamodels. The CMM task specific measurement uncertainties made at metrology laboratory were validated to be of maximum 7.96 µm (1σ) for the largest 2 m assemblies. The analysis of the results further showed how using this method a ‘virtual twins’ of the engineering structures can be calibrated with known uncertainty of thermal drift prediction behaviour in the micrometric range. Namely the Empirical, FEM and FEM Metamodels uncertainties of predictions were validated to be of maximum: 8.7 µm (1σ), 11.28 µm (1σ) and 12.24 µm (1σ).CERN-THESIS-2018-001oai:cds.cern.ch:22992062017-12-31T16:17:14Z |
spellingShingle | Engineering Accelerators and Storage Rings Doytchinov, Iordan Petrov Alignment measurements uncertainties for large assemblies using probabilistic analysis techniques |
title | Alignment measurements uncertainties for large assemblies using probabilistic analysis techniques |
title_full | Alignment measurements uncertainties for large assemblies using probabilistic analysis techniques |
title_fullStr | Alignment measurements uncertainties for large assemblies using probabilistic analysis techniques |
title_full_unstemmed | Alignment measurements uncertainties for large assemblies using probabilistic analysis techniques |
title_short | Alignment measurements uncertainties for large assemblies using probabilistic analysis techniques |
title_sort | alignment measurements uncertainties for large assemblies using probabilistic analysis techniques |
topic | Engineering Accelerators and Storage Rings |
url | http://cds.cern.ch/record/2299206 |
work_keys_str_mv | AT doytchinoviordanpetrov alignmentmeasurementsuncertaintiesforlargeassembliesusingprobabilisticanalysistechniques |