150,694 research outputs found
Feedback control of thermal lensing in a high optical power cavity
This paper reports automatic compensation of strong thermal lensing in a suspended 80 m optical cavity with sapphire test mass mirrors. Variation of the transmitted beam spot size is used to obtain an error signal to control the heating power applied to the cylindrical surface of an intracavity compensation plate. The negative thermal lens created in the compensation plate compensates the positive thermal lens in the sapphire test mass, which was caused by the absorption of the high intracavity optical power. The results show that feedback control is feasible to compensate the strong thermal lensing expected to occur in advanced laser interferometric gravitational wave detectors. Compensation allows the cavity resonance to be maintained at the fundamental mode, but the long thermal time constant for thermal lensing control in fused silica could cause difficulties with the control of parametric instabilities.This research was supported by the Australian
Research Council and the Department of Education,
Science and Training and by the U.S. National Science Foundation,
through LIGO participation in the HOPF
Compensation of Strong Thermal Lensing in High Optical Power Cavities
In an experiment to simulate the conditions in high optical power advanced
gravitational wave detectors such as Advanced LIGO, we show that strong thermal
lenses form in accordance with predictions and that they can be compensated
using an intra-cavity compensation plate heated on its cylindrical surface. We
show that high finesse ~1400 can be achieved in cavities with internal
compensation plates, and that the cavity mode structure can be maintained by
thermal compensation. It is also shown that the measurements allow a direct
measurement of substrate optical absorption in the test mass and the
compensation plate.Comment: 8 page
Compensation for thermal effects in mirrors of Gravitational Wave Interferometers
In this paper we study several means of compensating for thermal lensing
which, otherwise, should be a source of concern for future upgrades of
interferometric detectors of gravitational waves. The methods we develop are
based on the principle of heating the cold parts of the mirrors. We find that
thermal compensation can help a lot but can not do miracles. It seems finally
that the best strategy for future upgrades (``advanced configurations'') is
maybe to use thermal compensation together with another substrate materials
than Silica, for example Sapphire.Comment: 20 pages, 12 figure
A particle swarm optimisation-based Grey prediction model for thermal error compensation on CNC machine tools
Thermal errors can have a significant effect on CNC machine tool accuracy. The thermal error compensation system has become a cost-effective method of improving machine tool accuracy in recent years. In the presented paper, the Grey relational analysis (GRA) was employed to obtain the similarity degrees between fixed temperature sensors and the thermal response of the CNC machine tool structure. Subsequently, a new Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To improve the accuracy of the proposed model, the generation coefficients of GMC(1, N) are calibrated using an adaptive Particle Swarm Optimisation (PSO) algorithm. The results demonstrate good agreement between the experimental and predicted thermal error. Finally, the capabilities and the limitations of the model for thermal error compensation have been discussed.
Keywords: CNC machine tool, Thermal error modelling, ANFIS, Fuzzy logic, Grey system theory
The application of ANFIS prediction models for thermal error compensation on CNC machine tools
Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.
A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system
Thermal-difference compensation for structural members
Aluminum-thermal difference link maintains constant length of strut despite environmental temperature changes. An extension spring decreases load on the compensator drive linkage when strut is in tension, when strut is in compression, a compression spring is used. Perforated titanium outer tube exposes link to external heat
A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation
The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools. The method combines the Adaptive Neuro Fuzzy Inference System (ANFIS) and Grey system theory to predict thermal errors in machining. Instead of following a traditional approach, which utilises original data patterns to construct the ANFIS model, this paper proposes to exploit Accumulation Generation Operation (AGO) to simplify the modelling procedures. AGO, a basis of the Grey system theory, is used to uncover a development tendency so that the features and laws of integration hidden in the chaotic raw data can be sufficiently revealed. AGO properties make it easier for the proposed model to design and predict. According to the simulation results, the proposed model demonstrates stronger prediction power than standard ANFIS model only with minimum number of training samples
Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera
Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results.
In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model
Compensation of thermal nonlinearity effect in optical resonators
Thermal nonlinearity is known to cause bistability in Whispering Gallery Mode (WGM) resonators and to destabilize the red slope of the Lorentzian resonant curve. We demonstrate an optical technique that allows compensation of the thermal effect and forces the resonances to appear linear with both red and blue slopes stable
Overview of Advanced LIGO Adaptive Optics
This is an overview of the adaptive optics used in Advanced LIGO (aLIGO),
known as the thermal compensation system (TCS). The thermal compensation system
was designed to minimize thermally-induced spatial distortions in the
interferometer optical modes and to provide some correction for static
curvature errors in the core optics of aLIGO. The TCS is comprised of ring
heater actuators, spatially tunable CO laser projectors and Hartmann
wavefront sensors. The system meets the requirements of correcting for nominal
distortion in Advanced LIGO to a maximum residual error of 5.4nm, weighted
across the laser beam, for up to 125W of laser input power into the
interferometer
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