A fiducial-aided data fusion method for the measurement of multiscale complex surfaces

Multiscale complex surfaces, possessing high form accuracy and geometric complexity, are widely used for various applications in fields such as telecommunications and biomedicines. Despite the development of multi-sensor technology, the stringent requirements of form accuracy and surface finish still present many challenges in their measurement and characterization. This paper presents a fiducial-aided data fusion method (FADFM), which attempts to address the challenge in modeling and fusion of the datasets from multiscale complex surfaces. The FADFM firstly makes use of fiducials, such as standard spheres, as reference data to form a fiducial-aided computer-aided design (FA-CAD) of the multiscale complex surface so that the established intrinsic surface feature can be used to carry out the surface registration. A scatter searching algorithm is employed to solve the nonlinear optimization problem, which attempts to find the global minimum of the transformation parameters in the transforming positions of the fiducials. Hence, a fused surface model is developed which takes into account both fitted surface residuals and fitted fiducial residuals based on Gaussian process modeling. The results of the simulation and measurement experiments show that the uncertainty of the proposed method was up to 3.97 × 10 −5 μm based on a surface with zero form error. In addition, there is a 72.5% decrease of the measurement uncertainty as compared with each individual sensor value and there is an improvement of more than 36.1% as compared with the Gaussian process-based data fusion technique in terms of root-mean-square (RMS) value. Moreover, the computation time of the fusion process is shortened by about 16.7%. The proposed method achieves final measuring results with better metrological quality than that obtained from each individual dataset, and it possesses the capability of reducing the measurement uncertainty and computational cost

Diamond machining of freeform-patterned surfaces on precision rollers

Rapid development of freeform surfaces faces the challenges of not only higher form accuracy and smoother surface finishing, but also high machining efficiency and lower manufacturing cost. Combining diamond turning and roll-to-roll embossing technologies is a promising solution to fulfil these requirements. This paper presents a generic method to design and machine freeform surfaces on precision rollers. The freeform surface designed on the flat substrate is first transferred onto the cylindrical roller surface. The freeform-patterned roller surface is then diamond turned using the toolpath generated by a purposely developed toolpath generator. With the proposed method, the complex freeform surfaces designed on flat substrate can be transferred to and precisely machined on the cylindrical roller surfaces. A cutting experiment has been conducted to demonstrate the capability of the proposed method. In the experiment, a sinusoidal surface was designed and diamond turned on a precision roller. The results demonstrate that the proposed method is accurate and effective. The proposed method provides guidance for the design and precision manufacturing of freeform-patterned surfaces on precision rollers

Nanoscale measurement with pattern recognition of an ultra-precision diamond machined polar microstructure

Due to the low resolution of pattern recognition and disorganized textures of the surfaces of most natural objects observed under a microscope, computer vision technology has not been widely applied in precision positioning measurement on machine tools, which needs high resolution and accuracy. This paper presents a systematic method to solve the surface recognition problem which makes use of ultra-precision diamond machining to produce a functional and polar-coordinate surface named ‘polar microstructure’. The unique characteristic of a polar microstructure is the distinctive pattern of any locations including rotation in the global surface which provides the feasibility of achieving precise absolute positions by matching the patterns by utilizing computer vision technology. A polar microstructure which possesses orientation characteristics is also able to measure the displacement of rotation angle. A series of simulation experiments including feature point extraction, orientation detection as well as resolution of pattern recognition was conducted, and the results show that a polar microstructure can achieve a resolution of 9.35 nm which is capable of providing a novel computer vision-based nanometric precision measurement method which can be applied in positioning on machine tools in the future

A rotational stitching method for measuring cylindrical surfaces

It is difficult for traditional measurement instruments such as white light interferometers and coordinate measuring machines to measure cylindrical surfaces such as grinding wheels since most of these instruments only have 2.5D measurement capability so that the data may be missing in the bottom section of the workpieces. Moreover, the area near the edge where the surface gradient is high would be susceptible to large measurement uncertainty for some instruments. To address these shortcomings, this paper presents a method named Rotational Stitching Method (RSM) which attempts to measure the whole cylindrical surface. The method is used to measure a grinding wheel mounted on the shaft of a step motor which can be controlled and rotated with pre-set micro steps. A series of measurement experiments are designed to measure the top surfaces of the grinding wheel with a white light interferometer for every certain angle and the measurements are designed to have overlapped regions for registration. After the sub-measurements cover the whole cylindrical surface, the measurement datasets of the sub-surfaces are stitched and fused together to form the holistic surface of the grinding wheel. The motion errors of the grinding wheel including the rotation error of the motor, and alignment error can be eliminated or minimized by the stitching process. The capability of the method is realized by measurement experiments

A study of extrapolation of freeform surfaces to improve the edge effect in surface filtering

Surface filtering is a hot research topic especially in the field of freeform surface metrology, since filtering is an important data processing technique before further characterization of the measured surfaces. There is a large number of surface filtering algorithms developed by researchers to improve the robustness and accuracy of the filtering results. However, the filtering result is still far from complete which is particularly true in the edge area where is always found to have large distortion. This is so-called the edge effect which is mainly caused by a lack of data when performing convolution in the edge area in the filtering algorithms. In this paper, a Gaussian process machine learning-based surface extrapolation method of the measurement data is presented to extend the measured surface before conducting surface filtering. A Gaussian process data modelling method is utilized for the surface extrapolation and hence a Gaussian filtering method is used for the surface filtering. A series of simulation and practical measurement experiments have been conducted to evaluate the performance of the proposed method. The accuracy and efficiency of the new filtering method are demonstrated and analyzed in the experiments. The results show that the edge effect can be significantly improved and the efficiency can also be improved by introducing the extrapolation method. The proposed method provides a new way for surface filtering and thus surface characterization for the complex freeform surfaces

On-machine surface defect detection using light scattering and deep learning

This paper presents an on-machine surface defect detection system using light scattering and deep learning. A supervised deep learning model is used to mine the information related to defects from light scattering patterns. A convolutional neural network is trained on a large dataset of scattering patterns that are predicted by a rigorous forward scattering model. The model is valid for any surface topography with homogeneous materials and has been verified by comparing with experimental data. Once the neural network is trained, it allows for fast, accurate and robust defect detection. The system capability is validated on micro-structured surfaces produced by ultra-precision diamond machining

Gaussian process machine learning-based surface extrapolation method for improvement of the edge effect in surface filtering

Filtering for signal and data is an important technology to reduce and/or remove noise signal for further extraction of desired information. However, it is well known that significant distortions may occur in the boundary areas of the filtered data because there is no sufficient data to be processed. This drawback largely affects the accuracy of topographic measurements and characterizations of precision freeform surfaces, such as freeform optics. To address this issue, a Gaussian process machine learning-based method is presented for extrapolation of the measured surface to an extended measurement area with high accuracy prior to filtering the surface. With the extrapolated data, the edge distortion can be effectively reduced. The effectiveness of this method was evaluated using both simulated and experimental data. Successful implementation of the proposed method not only addresses the issue in surface filtering but also provides a promising solution for numerous applications involving filtering processes

Optimization of Tool Path for Uniform Scallop-Height in Ultra-precision Grinding of Free-form Surfaces

Free-form surfaces have been widely used in complex optical devices to improve the functional performance of imaging and illumination quality and reduce sizes. Ultra-precision grinding is a kind of ultra-precision machining technology for fabricating free-form surfaces with high form accuracy and good surface finish. However, the complexity and variation of curvature of the free-form surface impose a lot of challenges to make the process more predictable. Tool path as a critical factor directly determines the form error and surface quality in ultra-precision grinding of free-form surfaces. In conventional tool path planning, the constant angle method is widely used in machining free-form surfaces, which resulted in non-uniform scallop-height and degraded surface quality of the machined surfaces. In this paper, a theoretical scallop-height model is developed to relate the residual height and diverse curvature radius. Hence, a novel tool-path generation method is developed to achieve uniform scallop-height in ultra-precision grinding of free-form surfaces. Moreover, the iterative closest-point matching method, which is a well-known algorithm to register two surfaces, is exploited to make the two surfaces match closely through rotation and translation. The deviation of corresponding points between the theoretical and the measured surfaces is determined. Hence, an optimized tool-path generator is developed that is experimentally verified through a series of grinding experiments conducted on annular sinusoidal surface and single sinusoidal surface, which allows the realization of the achievement of uniform scallop-height in ultra-precision grinding of free-form surfaces

A Study of Optimized Tool Path for Uniform Scallop-height in Ultra-precision Grinding of Freeform Surfaces

Freeform surfaces have been widely used in complex optical devices to improve the functional performance of imaging and illumination quality and reduce sizes. Ultra-precision grinding is a kind of ultra-precision machining technology for fabricating freeform surfaces with high form accuracy and surface finish. However, the complexity and variation of curvature of the freeform surface impose a lot of challenges to make the process to be more predictable. Tool path as a critical factor directly determines the form error and surface quality in ultra-precision grinding of freeform surfaces. In order to study the influence of wheel path and path parameters on the surface generation in ultra-precision grinding of freeform surfaces. In this paper, a freeform mold is designed and two kinds of tool planning strategies are used to fabricate the freeform surfaces. They include constant angle and constant arc-length methods and the form errors and surface scallop-height are analyzed. Moreover, a theoretical surface generation model is developed to study the influence of grinding parameters and the radius of curvature for freeform surface profile on ground surface evolution. Hence, iterative closest point (ICP) matching method is adopted to determine the surface error between the measured surface and the designed surface. Hence, an optimized tool path generator is built to realize both the uniform scallop-height and good surface finish on the freeform surfaces