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Methods of evaluation of the positioning capability of Cartesian and non-Cartesian machines

Longstaff, Andrew P. (2002) Methods of evaluation of the positioning capability of Cartesian and non-Cartesian machines. Doctoral thesis, University of Huddersfield.

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Manufacture and assembly of ever more precise components has been the driving force for
many research projects. Error avoidance and error correction are used to improve the
accuracy of the final output, but it is only by error evaluation that a manufacturer can
quantify his production capability.

The ability of a machine to perform the task designated to it is of critical importance.
In particular it is essential to be able to determine the capability of a production machine to
produce a part accurately, a measuring machine to dimension a part reliably or a handling
machine to place a part in the appropriate position. In order to achieve this it is necessary to
establish the positioning accuracy of the device throughout its working volume.

In addition to the need for the assessment of machining performance, there is a
strong desire within the machine-building community to allow for the post-assembly
correction of errors inherent in the manufacture of machines. Such techniques are often a
cost-effective complement to error avoidance.

During this project, a new geometric model and supporting measurement methods
are produced for the evaluation of errors in Cartesian-based machines. This work is an
extension of that performed by Ford, et. al. Ell as discussed in chapter 3. The new work
addresses the previously unresolved problem of determining the errors throughout the
working volume of a machine with volumetric compensation and a tool or probe offset. This
simulation method is in contrast to other techniques that quantify machine performance
based upon a small subset of the machine volume.

It is proposed that a figure for volumetric accuracy derived from these methods
cannot stand on its own as a description of the manufacturing capability of a machine. The
effects of measurement uncertainty on the synthesis technique have been examined and
modelled. This has produced a method of quantifying machining capability based upon
machine configuration, tool or head configuration, and supported by uncertainty based upon
the test data input to the model.

An alternative method of evaluating errors through the working volume is to
measure directly using, for example, a tracking laser. One such system (LaserTrace) is
based upon absolute position being resolved by trilateration from two tracking lasers. This
system has been investigated for its applicability to the measurement process. Two methods
have been produced to improve the accuracy of the system and reduce the time required for
its calibration. One is based upon photogrammetry techniques, the other on a novel use of a
checking gauge (MCG). The artefact is used for acquisition of data to perform
parameter identification on a model of the system that has been found from first principles.

This MCG-based calibration technique was successful, within the constraints of the
resolution and repeatability of the control loop. Attempts were then made to apply this
methodology to a second machine of the non-Cartesian type and configuration (UMD).
Simulation shows this technique to be applicable, but the instability of the prototype
precluded comprehensive on-machine testing.

In the course of this research a thennal model of the UMD has also been produced to
overcome the sensitivity of the prototype device to temperature changes. Such a model
could be used to provide software correction of LTMD position values.

Item Type: Thesis (Doctoral)
Additional Information: EThOS Persistent ID
Uncontrolled Keywords: Machine performance, Machinery Tools, Machinery
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Centre for Precision Technologies
School of Computing and Engineering > Centre for Precision Technologies > Engineering Control and Machine Performance Research Group
Related URLs:
Depositing User: Graham Stone
Date Deposited: 22 Oct 2009 14:50
Last Modified: 16 Nov 2015 07:13


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