4,057 research outputs found
Representing the Process of Machine Tool Calibration in First-order Logic
Machine tool calibration requires a wide range of measurement techniques that can be carried out in many different sequences. Planning a machine tool calibration is typically performed by a subject expert with a great understanding of International standards and industrial best-practice guides. However, it is often the case that the planned sequence of measurements is not the optimal. Therefore, in an attempt to improve the process, intelligent computing methods can be designed for plan suggestion. As a starting point, this paper presents a way of converting expert knowledge into first-order logic that can be expressed in the PROLOG language. It then shows how queries can be executed against the logic to construct a knowledge-base of all the different measurements that can be performed during machine tool calibration
Comparison of Volumetric Analysis Methods for Machine Tools with Rotary Axes
Confidence in the ability of a production machine to meet manufacturing
tolerances requires a full understanding of the accuracy of the machine.
However, the definition of âthe accuracy of the machineâ is open to
interpretation. Historically, this has been in terms of linear positioning accuracy
of an axis with no regard for the other errors of the machine. Industry awareness
of the three-dimensional positioning accuracy of a machine over its working
envelope has slowly developed to an extent that people are aware that
âvolumetric accuracyâ gives a better estimation of machine performance.
However, at present there is no common standard for volumetric errors of
machine tools, although several researchers have developed models to predict
the effect of the combined errors.
The error model for machines with three Cartesian axes has been well
addressed, for example by the use of homogenous transformation matrices.
Intuitively, the number of error sources increases with the number of axes
present on the machine. The effect of the individual axis geometric errors can
become increasingly significant as the chain of dependent axes is extended.
Measurement of the âvolumetric errorâ or its constituents is often restricted
to a subset of the errors of the Cartesian axes by solely relying on a laser
interferometer for measurement. This leads to a volumetric accuracy figure that
neglects the misalignment errors of rotary axes. In more advanced models the
accuracy of the rotary axes are considered as a separate geometric problem
whose volumetric accuracy is then added to the volumetric accuracy of the
Cartesian axes.
This paper considers the geometric errors of some typical machine
configurations with both Cartesian and non-Cartesian axes and uses case studies
to emphasise the importance of measurement of all the error constituents.
Furthermore, it shows the misrepresentation when modelling a five-axis
machine as a three-plus-two error problem. A method by which the five-axis
model can be analysed to better represent the machine performance is
introduced.
Consideration is also given for thermal and non-rigid influences on the
machine volumetric accuracy analysis, both in terms of the uncertainty of the
model and the uncertainty during the measurement. The magnitude of these
errors can be unexpectedly high and needs to be carefully considered whenever
testing volumetric accuracy, with additional tests being recommended
Multiple-sensor integration for efficient reverse engineering of geometry
This paper describes a multi-sensor measuring system for reverse engineering applications. A sphere-plate artefact is developed for data unification of the hybrid system. With the coordinate data acquired using the optical system, intelligent feature recognition and segmentation algorithms can be applied to extract the global surface information of the object. The coordinate measuring machine (CMM) is used to re-measure the geometric features with a small amount of sampling points and the obtained information can be subsequently used to compensate the point data patches which are measured by optical system. Then the optimized point data can be exploited for accurate reverse engineering of CAD model. The limitations of each measurement system are compensated by the other. Experimental results validate the accuracy and effectiveness of this data optimization approach
Investigation of a new method for improving image resolution for camera tracking applications
Camera based systems have been a preferred choice in many motion tracking applications due to the ease of installation and the ability to work in unprepared environments. The concept of these systems is based on extracting image information (colour and shape properties) to detect the object location. However, the resolution of the image and the camera field-of- view (FOV) are two main factors that can restrict the tracking applications for which these systems can be used. Resolution can be addressed partially by using higher resolution cameras but this may not always be possible or cost effective.
This research paper investigates a new method utilising averaging of offset images to improve the effective resolution using a standard camera. The initial results show that the minimum detectable position change of a tracked object could be improved by up to 4 times
Towards obtaining robust boundary condition parameters to aid accuracy in FEA thermal error predictions
Finite Element Analysis (FEA) is used as a design tool within engineering industries due to the
capability for rapid summative analysis accompanied by the visual aid. However, to represent realistic behaviour, FEA relies heavily on input parameters which must ideally be based on true figures such as data from experimental testing which sometimes requires time-consuming testing regimes. In the case of machine tool assemblies where complex structural joints and linkages are present, access to those areas can be a primary constraint to obtaining related boundary parameters such as heat flow across joints, for which, assumptions are incorporated to the FEA model which in effect increase the uncertainty in the FEA predictions. Similarly, in the case of thermal error modelling, simplifications are made when representing thermal boundary conditions such as the application of a uniform convection parameter to an assembly with parts assembled in both horizontal and vertical orientations. This research work aims to reduce the number of
assumptions by providing experimentally obtained thermal boundary condition parameters. This
work acknowledges experimental regimes that focus on obtaining thermal parameters related
to the conduction across assembly joints (Thermal Contact Conductance-TCC) and measures the
convection around areas such as belt drives and rotating parts to obtain convection parameters
as inputs to the FEA. It provides TCC parameters for variable interfacial behaviour based on the
varying contact pressure and the heat flow through dry and oiled contacts such as the conduction from spindle bearings to the surrounding housing and conduction from guideways into the associated assembly through carriages and contact bearings. It provides convection parameters across the test mandrel rotating at different speeds and around stationary structures such as convection parameters observed during TCC tests. It also provide details on the methods used to obtain all these parameters such as the use of thermal imaging, sensors placements and methods to obtain these boundary condition parameters. The significance of this work is to improve dramatically FEA thermal predictions, which are a critical part of engineering design. Although the focus is on machine tool design, the process and parameters can equally be applied to other areas of thermodynamic behaviour
Efficient estimation by FEA of machine tool distortion due to environmental temperature perturbations
Machine tools are susceptible to exogenous influences, which mainly derive from varying environmental conditions such as the day and night or seasonal transitions during which large temperature swings can occur. Thermal gradients cause heat to flow through the machine structure and results in non-linear structural deformation whether the machine is in operation or in a static mode. These environmentally stimulated deformations combine with the effects of any internally generated heat and can result in significant error increase if a machine tool is operated for long term regimes. In most engineering industries, environmental testing is often avoided due to the associated extensive machine downtime required to map empirically the thermal relationship and the associated cost to production. This paper presents a novel offline thermal error modelling methodology using finite element analysis (FEA) which significantly reduces the machine downtime required to establish the thermal response. It also describes the strategies required to calibrate the model using efficient on-machine measurement strategies. The technique is to create an FEA model of the machine followed by the application of the proposed methodology in which initial thermal states of the real machine and the simulated machine model are matched. An added benefit is that the method determines the minimum experimental testing time required on a machine; production management is then fully informed of the cost-to-production of establishing this important accuracy parameter. The most significant contribution of this work is presented in a typical case study; thermal model calibration is reduced from a fortnight to a few hours. The validation work has been carried out over a period of over a year to establish robustness to overall seasonal changes and the distinctly different daily changes at varying times of year. Samples of this data are presented that show that the FEA-based method correlated well with the experimental results resulting in the residual errors of less than 12 ÎŒm
FEA-based design study for optimising non-rigid error detection on machine tools
Non-rigid-body behaviour can have a considerable effect on the overall accuracy performance of machine tools. These errors originate from bending of the machine structure due to change in distribution of its own weight or from movement of the workpiece and fixture. These effects should be reduced by good mechanical design, but residual errors can still be problematic due to realistic material and cost limitations. One method of compensation is to measure the deformation directly with sensors embedded in a metrology frame. This paper presents an FEA-based design study which assesses finite stiffness effects in both the machine structure and its foundation to optimise the sensitivity of the frame to the resulting errors. The study results show how a reference artefact, optimised by the FEA study, can be used to detect the distortion
Evaluation of measurement technique for a precision aspheric artefact using a nano-measuring machine
A precision aspheric artefact is measured in 3D by a commercially available nano-measuring machine (NMM) integrated with a contact inductive sensor as the probe. The mathematics of 3D compensation of the error caused by the probe radius is derived. The influence of the probe radius measurement uncertainty on the compensation errors for the 3D measurements is discussed. If the calibration uncertainty of probe radius is 1m and 0.1 m respectively, the compensation errors for a paraboloid artefact are within 100 nm and 10 nm respectively, and the artefact measurement uncertainties are 103 nm and 26 nm respectively. The artefact calibration uncertainty depends more on the uncertainty of the probe radius calibration than the probe radius
Five-Axis Machine Tool Condition Monitoring Using dSPACE Real-Time System
This paper presents the design, development and SIMULINK implementation of the lumped parameter model of C-axis drive from GEISS five-axis CNC machine tool. The simulated results compare well with the experimental data measured from the actual machine. Also the paper describes the steps for data acquisition using ControlDesk and hardware-in-the-loop implementation of the drive models in dSPACE real-time system. The main components of the HIL system are: the drive model simulation and input â output (I/O) modules for receiving the real controller outputs. The paper explains how the experimental data obtained from the data acquisition process using dSPACE real-time system can be used for the development of machine tool diagnosis and prognosis systems that facilitate the improvement of maintenance activities
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Live Hot, Die Young: Transmission Distortion in Recombination Hotspots
There is strong evidence that hotspots of meiotic recombination in humans are transient features of the genome. For example, hotspot locations are not shared between human and chimpanzee. Biased gene conversion in favor of alleles that locally disrupt hotspots is a possible explanation of the short lifespan of hotspots. We investigate the implications of such a bias on human hotspots and their evolution. Our results demonstrate that gene conversion bias is a sufficiently strong force to produce the observed lack of sharing of intense hotspots between species, although sharing may be much more common for weaker hotspots. We investigate models of how hotspots arise, and find that only models in which hotspot alleles do not initially experience drive are consistent with observations of rather hot hotspots in the human genome. Mutations acting against drive cannot successfully introduce such hotspots into the population, even if there is direct selection for higher recombination rates, such as to ensure correct segregation during meiosis. We explore the impact of hotspot alleles on patterns of haplotype variation, and show that such alleles mask their presence in population genetic data, making them difficult to detect.</p
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