Bisquare robust polynomial fitting method for dimple distortion minimization in surface quality analysis

Abstract

In this paper, the bisquare robust polynomial fitting (BSRPF) method for surface texture features assessment was proposed. Plateau‐honed cylinder liner bronze surfaces with additionally burnished microcavities (defined as dimple, holes, and/or cavities) were taken into consideration. Analyzed details were measured with stylus (Talyscan 150) or white light interferometer (Talysurf CCI Lite) methods. It was assumed that application of commonly used (in surface contact and noncontact measurement technology) polynomial fitting procedures did not allow to extract required features; distortion of microreservoir dimples for oil consumption assessment was falsely estimated when valleys were near edge or on the edge located. Application of bisquare robust polynomial fitting method caused a minimization of microflattens of dimples irrespective of valley distribution; the biggest degree of polynomial fitting method applied, the minimal distortions of oil reservoirs were noticed. The valley size (depth and width) and valley‐to‐valley distance were also taken into account.

Reduction of Influence of the High-Frequency Noise on the Results of Surface Topography Measurements

“…Free-of-dimple analysis, firstly proposed in this paper, indicates the areas of surface where oil reservoirs (oil pockets or dimples) did not occur. This type of analyzed detail is flat in general, waviness and form were eliminated; many papers contain relevant information about areal form removal of “engineered surfaces” [ 55 , 56 , 57 , 58 , 59 , 60 , 61 ] and its influence on the tribological performance, e.g., friction [ 62 ]. Therefore, the selection of roughness separation (F-operator) method was not scrutinized in the presented results.…”

Thresholding Methods for Reduction in Data Processing Errors in the Laser-Textured Surface Topography Measurements

“…Generally, all of the measurement errors can be selected into those directly related to the measuring methods [ 25 ], caused by the digitisation [ 26 ] or data processing [ 27 ], software [ 28 ], object measuring [ 29 ] or other errors [ 30 ]. Furthermore, those errors found when the measurement process occurs are defined as noise [ 31 ], or in particular, measurement noise [ 32 ].…”

Selection of Methods of Surface Texture Characterisation for Reduction of the Frequency-Based Errors in the Measurement and Data Analysis Processes

“…The whole process of surface texture evaluation [11], including the measurement and analysis of the results obtained, can be fraught with many issues that influence the accuracy of the studies performed. All of those errors can be roughly divided into those typical to the measuring method [12] and those caused by the digitisation or data processing [13], software [14], measuring object [15] or other errors [16,17]. One of the types of errors is facilitated when the measurement process occurs.…”