Structured surfaces, defined as surfaces characterized by topography features whose shape is defined by design specifications, are increasingly being used in industry for a variety of applications, including improving the tribological properties of surfaces. However, characterization of such surfaces still remains an issue. Techniques have been recently proposed, based on identifying and extracting the relevant features from a structured surface so they can be verified individually, using methods derived from those commonly applied to standard-sized parts. Such emerging approaches show promise but are generally complex and characterized by multiple data processing steps making performance difficult to assess. This paper focuses on the segmentation step, i.e. partitioning the topography so that the relevant features can be separated from the background. Segmentation is key for defining the geometric boundaries of the individual feature, which in turn affects any computation of feature size, shape and localization. This paper investigates the effect of varying the segmentation algorithm and its controlling parameters by considering a test case: a structured surface for bearing applications, the relevant features being micro-dimples designed for friction reduction. In particular, the mechanisms through which segmentation leads to identification of the dimple boundary and influences dimensional properties, such as dimple diameter and depth, are illustrated. It is shown that, by using different methods and control parameters, a significant range of measurement results can be achieved, which may not necessarily agree. Indications on how to investigate the influence of each specific choice are given; in particular, stability of the algorithms with respect to control parameters is analyzed as a means to investigate ease of calibration and flexibility to adapt to specific, application-dependent characterization requirements.

Comparison of segmentation techniques to determine the geometric parameters of structured surfaces

SENIN, Nicola;
2014

Abstract

Structured surfaces, defined as surfaces characterized by topography features whose shape is defined by design specifications, are increasingly being used in industry for a variety of applications, including improving the tribological properties of surfaces. However, characterization of such surfaces still remains an issue. Techniques have been recently proposed, based on identifying and extracting the relevant features from a structured surface so they can be verified individually, using methods derived from those commonly applied to standard-sized parts. Such emerging approaches show promise but are generally complex and characterized by multiple data processing steps making performance difficult to assess. This paper focuses on the segmentation step, i.e. partitioning the topography so that the relevant features can be separated from the background. Segmentation is key for defining the geometric boundaries of the individual feature, which in turn affects any computation of feature size, shape and localization. This paper investigates the effect of varying the segmentation algorithm and its controlling parameters by considering a test case: a structured surface for bearing applications, the relevant features being micro-dimples designed for friction reduction. In particular, the mechanisms through which segmentation leads to identification of the dimple boundary and influences dimensional properties, such as dimple diameter and depth, are illustrated. It is shown that, by using different methods and control parameters, a significant range of measurement results can be achieved, which may not necessarily agree. Indications on how to investigate the influence of each specific choice are given; in particular, stability of the algorithms with respect to control parameters is analyzed as a means to investigate ease of calibration and flexibility to adapt to specific, application-dependent characterization requirements.
2014
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1330312
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