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"FLATNESS MEASUREMENT"
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Optical Dual Laser Based Sensor Denoising for OnlineMetal Sheet Flatness Measurement Using Hermite Interpolation
Flatness sensors are required for quality control of metal sheets obtained from steel coils by roller leveling and cutting systems. This article presents an innovative system for real-time robust surface estimation of flattened metal sheets composed of two line lasers and a conventional 2D camera. Laser plane triangulation is used for surface height retrieval along virtual surface fibers. The dual laser allows instantaneous robust and quick estimation of the fiber height derivatives. Hermite cubic interpolation along the fibers allows real-time surface estimation and high frequency noise removal. Noise sources are the vibrations induced in the sheet by its movements during the process and some mechanical events, such as cutting into separate pieces. The system is validated on synthetic surfaces that simulate the most critical noise sources and on real data obtained from the installation of the sensor in an actual steel mill. In the comparison with conventional filtering methods, we achieve at least a 41% of improvement in the accuracy of the surface reconstruction.
Journal Article
Pairwise Registration Algorithm for Large-Scale Planar Point Cloud Used in Flatness Measurement
In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a large-scale planar point cloud to ensure that the flatness measurement is precise. To that end, the proposed algorithm extracts the boundary of the point cloud to obtain more effective feature descriptors of the keypoints. Then, it eliminates the invalid keypoints by neighborhood evaluation to obtain the initial matching point pairs. Thereafter, clustering combined with the geometric consistency constraints of correspondences is conducted to realize coarse registration. Finally, the iterative closest point (ICP) algorithm is used to complete fine registration based on the boundary point cloud. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of boundary extraction and registration performance.
Journal Article
Nine-Probe Third-Order Matrix System for Precise Flatness Error Detection
Large-scale, high-density flatness measurement is critical for manufacturing reference surfaces in ultra-precision machine tools. Traditional methods exhibit degradation in both accuracy and efficiency as measurement points and area size increase. In order to overcome these limitations to meet the requirements for integrated in-process measurement and machining of structural components in ultra-precision machine tools, this paper proposes a novel nine-probe third-order matrix system that integrates the Fine Sequential Three-Point (FSTRP) method with automated scanning path planning. The system utilizes a multi-probe error separation algorithm based on the FSTRP principle, combined with real-time adaptive sampling, to decouple machine tool motion errors from intrinsic workpiece flatness deviations. This system breaks through traditional multi-probe 1D straightness measurement limitations, enabling direct 2D flatness measurement (with X/Y error decoupling), higher sampling density, and a repeatability standard deviation of 0.32 μm for large precision machine tool components. This high-efficiency, high-precision solution is particularly suitable for automated flatness inspection of large-scale components, providing a reliable metrology solution for integrated measurement-machining of flatness on precision machine tool critical components.
Journal Article
Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach
Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.
Journal Article
Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part II—Experimental Implementation
Coordinate measuring machines (CMM) are main instruments of measurement in laboratories and in industrial quality control. A compensation error model has been formulated (Part I). It integrates error and uncertainty in the feature measurement model. Experimental implementation for the verification of this model is carried out based on the direct testing on a moving bridge CMM. The regression results by axis are quantified and compared to CMM indication with respect to the assigned values of the measurand. Next, testing of selected measurements of length, flatness, dihedral angle, and roundness features are accomplished. The measurement of calibrated gauge blocks for length or angle, flatness verification of the CMM granite table and roundness of a precision glass hemisphere are presented under a setup of repeatability conditions. The results are analysed and compared with alternative methods of estimation. The overall performance of the model is endorsed through experimental verification, as well as the practical use and the model capability to contribute in the improvement of current standard CMM measuring capabilities.
Journal Article
Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development
The development of an error compensation model for coordinate measuring machines (CMMs) and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included.
Journal Article
Analysis Of Factors Affecting Gravity-Induced Deflection For Large And Thin Wafers In Flatness Measurement Using Three-Point-Support Method
Accurate flatness measurement of silicon wafers is affected greatly by the
(GID) of the wafers, especially for large and thin wafers. The three-point-support method is a preferred method for the measurement, in which the GID uniquely determined by the positions of the supports could be calculated and subtracted. The accurate calculation of GID is affected by the initial stress of the wafer and the positioning errors of the supports. In this paper, a
(FEM) including the effect of initial stress was developed to calculate GID. The influence of the initial stress of the wafer on GID calculation was investigated and verified by experiment. A systematic study of the effects of positioning errors of the support ball and the wafer on GID calculation was conducted. The results showed that the effect of the initial stress could not be neglected for ground wafers. The wafer positioning error and the circumferential error of the support were the most influential factors while the effect of the vertical positioning error was negligible in GID calculation.
Journal Article
The Research of the Genetic Algorithm Combined with Chromosome Fitness to Optimize the Flatness Error Evaluation
This paper suggests an improved genetic algorithm to seek the minimum range value in the ideal-plane flatness measurement. This algorithm increases measurement accuracy by using dynamic cross factor, mutation factor and a new concept called chromosome fitness. It was proved in simulation experiments that its accuracy is better than other flatness error evaluating algorithms like the minimal territory evaluating algorithm and the computational geometry algorithm etc. So it can be used for measuring industrial production components error and verifying assumed models in reverse engineering etc.
Journal Article
Investigating the development of digital patterns for customized apparel
Purpose - The paper aims to provide an overview of the area of digital pattern developing for customized apparel.Design methodology approach - The paper outlines several methods of digital pattern developing for customized apparel, and discusses the principles, characters and applications. Digital pattern developing process has two paths. One path develops apparel according to traditional 2D pattern-making technology. There are three methods: parametric design, traditional grading technique, and pattern generating based on artificial intelligence (AI). Another path develops pattern through surface flattening directly from individual 3D apparel model.Findings - For parametric method, it can improve greatly the efficiency of pattern design or pattern alteration. However, the development and application of parametric Computer-Aided-Design (CAD) systems in apparel industry are difficult, because apparel pattern has fewer laws in graphical structure. For grading technique, it is the most practical method because of its simple theory, with which pattern masters are familiar. But these methods require users with higher experience. Creating expert pattern system based on AI can reduce the experience requirements. Meanwhile, a great deal of experiments should be conducted for each garment with different style to create their knowledge databases. For 3D CAD technology, two methods of surface flattening have been outlined, namely geometry flattening and physical flattening. But many improvements should be done if the 3D CAD systems are applied in apparel mass customization.Originality value - The paper provides information of value to the future research on developing a practical made-to-measure apparel pattern system.
Journal Article