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|Title:||A gaussian process-based multi-sensor metrology system for precision measurement of freeform surfaces||Authors:||Liu, Mingyu||Advisors:||Cheung, Benny (ISE)
Cheng, C. H. (ISE)
Lee, W. B. (ISE)
|Keywords:||Surfaces (Technology) -- Measurement
|Issue Date:||2018||Publisher:||The Hong Kong Polytechnic University||Abstract:||Nowadays, precision freeform surfaces play an important role since they have superior performance and indispensable functionalities. Due to their geometrical complexity, high form accuracy and low surface roughness, precision freeform surfaces introduce a lot of research challenges in precision manufacturing and measurement processes. This is particularly true when the measurement is performed on traditional off-line single-sensor instruments such as white light interferometers (WLIs) and coordinate measuring machines (CMMs) whose measurement abilities are limited. For a single-sensor instrument, the measurement range and measurement resolution always need to strike a balance since the two terms appear to be contradictory. Moreover, when the workpiece is extremely large and error compensation procedure is needed to correct the form error of the workpiece, it is necessary to perform the measurement on machining facilities since repositioning error is unacceptable. However, off-line based measurement instruments cannot fulfil the in-situ measurement requirement. To address the above issues, this research firstly established a generic Gaussian process data modelling and image registration-based stitching method for the measurement of precision freeform surfaces based on traditional single-sensor surface measurement instruments using multiple measurement methods. With the proposed method, a dataset with a large measurement range and high resolution can be obtained. The proposed stitching method provides a turn-key solution for high dynamic range measurement using single-sensor instruments with a multiple measurement method. For multi-sensor instruments such as multi-sensor coordinate measuring machines (CMMs), this study proposes a Gaussian process-based data modelling and maximum likelihood data fusion method for the measurement of freeform surfaces for multi-sensor CMMs. The method utilizes an optical sensor such as laser sensor and a touch trigger probe mounted on the multi-sensor coordinate measuring machine for the measurement of freeform surfaces, and the measurement data are modelled using the Gaussian process modelling method. The combination of different kinds of sensors balances the measurement efficiency and accuracy since most optical sensors have a fast measurement speed and high density but low accuracy while contact sensors have an accurate measurement result but low efficiency. The measurement datasets from the laser sensor and touch trigger probe were fused with amaximum likelihood method so as to reduce the overall measurement uncertainty.
To address the in-situ measurement issue, this thesis proposes an autonomous multi-sensor in-situ metrology system for high dynamic range measurement of freeform surfaces for precision machine tools. The system utilizes a laser scanner and a motion sensor together with a designed trajectory so as to perform in-situ measurement on the machining facilities. The proposed system is independent of the machining facilities which makes it extendable to a wide range of industrial applications. Based on the theory developed for the autonomous multi-sensor in-situ metrology system, a homogeneous multi-sensor in-situ measurement metrology system was developed equipped with a laser line sensor and laser point sensor. The laser line sensor provides high lateral resolution data while the laser point sensor gives accurate data. The measurement data from these two kinds of sensors are fused to obtain a more accurate result without losing the high lateral resolution. The present study has very large potential applications in industry. The successful development of the Gaussian process and image registration-based stitching method provides an important means for high dynamic range measurement, while the Gaussian process-based data modelling and maximum likelihood-based data fusion method establishes a generic measurement strategy for multi-sensor coordinate measuring machines so as to improve the measurement accuracy for precision freeform surfaces. The proposed in-situ multi-sensor high dynamic range measurement method and hence the homogeneous multi-sensor in-situ metrology system enable the measurement ability of machine tools so as to improve the efficiency and accuracy of the precision manufacture of complex freeform surfaces. The outcome of the research contributes significantly to the measurement science and technology, especially in the field of multi-sensor measurement and in-situ measurement of precision freeform surfaces.
|Description:||xxi, 191 pages : color illustrations
PolyU Library Call No.: [THS] LG51 .H577P ISE 2018 Liu
|URI:||http://hdl.handle.net/10397/73137||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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Citations as of Dec 17, 2018
Citations as of Dec 17, 2018
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