Asymmetric morphological filter for roughness evaluation of multifunctional surfaces

Zakharov, Oleg V.; Lysenko, Valery G.; Ivanova, Tatyana N.

doi:10.1016/j.isatra.2023.12.016

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…The non-contact measuring techniques have become extremely popular due to their reduction in time [11]. Many studies of scholarly and industrial centres are based on contactless systems when measuring surface topography [12]. Their advantages over tactile techniques were reviewed and highlighted in many application comparisons [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of High-Frequency Measurement Errors from Turned Surface Topography Data Using Machine Learning Methods

Podulka,

Kulisz,

Antosz

2024

Materials

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Manufacturing processes in industry applications are often controlled by the evaluation of surface topography. Topography, in its overall performance, includes form, waviness, and roughness. Methods of measurement of surface roughness can be roughly divided into tactile and contactless techniques. The latter ones are much faster but sensitive to external disturbances from the environment. One type of external source error, while the measurement of surface topography occurs, is a high-frequency noise. This noise originates from the vibration of the measuring system. In this study, the methods for reducing high-frequency errors from the results of contactless roughness measurements of turned surfaces were supported by machine learning methods. This research delves into optimizing filtration methods for surface topography measurements through the application of machine learning models, focusing on enhancing the accuracy of surface roughness assessments. By examining turned surfaces under specific machining conditions and employing a variety of digital filters, the study identifies the Gaussian regression filter and spline filter as the most effective methods at a 22.5 µm cut-off. Utilizing neural networks, support vector machines, and decision trees, the research demonstrates the superior performance of SVMs, achieving remarkable accuracy and sensitivity in predicting optimal filtration methods.

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