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• An improved level-set model is proposed for honing groove feature segmentation. • The level-set Heaviside function is approximated using a piecewise linear function. • A 30% accuracy improvement over the best-tested algorithms has been observed. • A folder metal characterization case study validates the algorithm's performance. Honing groove features of a cylinder liner surface contain helpful information on surface performance. Traditional honing surface evaluation methods do not focus on groove features, which is significant to surface functions. Hence, a topographical feature segmentation algorithm and corresponding evaluation parameters are needed. This paper proposes an improved level-set-based segmentation algorithm for groove feature extraction. The proposed algorithm was tested on a real honing surface dataset compared to some state-of-art algorithms, including the classical height thresholding and watershed segmentation. The results show that the proposed method is robust against initial conditions and improves both segmentation accuracy and computational speed. Specifically, a 30% accuracy improvement over the best-tested level-set algorithm has been observed. Another case study on fold-mental characterization was conducted, which also validated the accuracy and speed performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Nonlinear errors severely interfere with the accuracy of chromatic confocal measurements at the submicron scale. • Nonlinear error stems from the deviation between the wavelength-distance mapping and the polynomial model. • The proposed method can effectively record the nonlinear error in calibration and compensate it in measurement. • The results show that the method can achieve a 40 % accuracy improvement over a 4 mm range. The mechanisms of the nonlinear error, primarily stemming from the deviation of the polynomial model fitting the wavelength-distance mapping relationship in chromatic confocal measurement, have been elucidated. A compensation method is proposed for the nonlinear error, based on a regression tree model. Through calibration experiments using our chromatic confocal sensor, an impressive improvement of over 40 % in precision could be achieved by our proposed compensation method, which advances from ±1 μm to ±0.6 μm within a 4 mm measurement range. [ABSTRACT FROM AUTHOR]

White-light interferometry (WLI) is an established method for surface measurement. In WLI, surface recovery algorithm from series of vertical scanning white light interferogram is pivotal technique and has been researched widely. However, the effectiveness of existing surface recovery algorithms is easy to be affected by some conditions like mechanical vibrations, low-reflection of the test surface and phase change caused by the reflection. In this paper, a new recovery algorithm is presented, in which correlation analysis of WLI envelop curves and a multi reference position based phase solution method are employed for robust and high precision surface recovery. Mathematical derivation of the algorithm is carried out, and simulation and experimental testing and comparison experiments are conducted, which show that the new algorithm is effective. [ABSTRACT FROM AUTHOR]

• We developed a cost-effective method for precision 2D stage error calibration. • Images of an arbitrarily textured plane are captured and analysed in the method. • A modified bundle adjustment algorithm was developed for global optimisation. • 3 μm accuracy within a 100 mm range was demonstrated using experiments. Positioning errors are a primary performance indicator of precision stages. Calibration of stage positioning errors usually requires expensive, specialised instruments or artefacts. In this paper, we introduce a simple and cost-effective, machine vision-based method for stage error calibration. The novelty is that we used an arbitrarily textured plane, for example an array of coins, as the imaging target, as opposed to costly precision machined artefacts. Positioning errors were extracted from overlapped texture images using a fast and globally optimised feature-matching algorithm. A computer simulation demonstrates that the method is accurate and numerically stable in terms of sensing noise. Trials with a coordinate measuring machine reveal that the method can achieve sub-pixel accuracy, with a maximum measurement error of approximately 3 μm within a 100 mm range. [ABSTRACT FROM AUTHOR]