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

Podulka, Przemysław

doi:10.3390/s22030791

Cited by 26 publications

(11 citation statements)

References 51 publications

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“…Holes, dimples, scratches, or other non-high-frequency-noise features (shortly non-noise features), represent frequencies in a different domain than a high-frequency spectrum. It was also previously indicated that the occurrence of non-noise features caused NS was not isotropic [16]. Therefore, both properties require the NS to be in the high-frequency domain and isotropic.…”

Section: Selection Of Cut-off Value With a Feature-based Techniquementioning

confidence: 96%

“…However, the measurement noise in the high-frequency domain [15] is studied in the present analysis. Generally, in the non-contact (optical) measurement of surface (topography), according to the non-uniform distribution of the light intensity, the received signal is susceptible to being skewed and asymmetric; therefore, a lot of high-frequency noise can be included [16]. Seeking the source, high-frequency noise can be caused by the mechanics' instability, considering any influences from the environment, but, in most cases, this type of error is the result of vibration [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

Podulka

2022

Sensors

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Errors that occur when surface topography is measured and analysed can be classified depending on the type of surface studied. Many types of surface topographies are considered when frequency-based errors are studied. However, turned surface topography is not comprehensively studied when data processing errors caused by false estimation (definition and suppression) of selected surface features (form or noise) are analysed. In the present work, the effects of the application of various methods (regular Gaussian regression, robust Gaussian regression, and spline and fast Fourier Transform filters) for the suppression of high-frequency measurement noise from the raw measured data of turned surface topography are presented and compared. The influence and usage of commonly used available commercial software, e.g., autocorrelation function, power spectral density, and texture direction, which function on the values of areal surface topography parameters from selected (ISO 25178) standards, are also introduced. Analysed surfaces were measured with a stylus or via non-contact (optical–white light interferometry) methods. It was found that the characterisation of surface topography, based on the analysis of selected features, can be crucial in reducing measurement and data analysis errors when various filters are applied. Moreover, the application of common functions can be advantageous when feature-based studies are proposed for both profile and areal data processing.

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Section: Selection Of Cut-off Value With a Feature-based Techniquementioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

Podulka

2022

Sensors

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“…Different conditions for the implementation of sheet metal forming processes and the variety of plastically processed materials mean that the following parameters should be taken into account when selecting a lubricant for a specific application [ 11 ]: processing temperature, value of maximum unit pressures, grade of processed material and grade of tool material, sliding speed, type of protective coating on the tool surface, and tool design. The surface topography of sheet metal has a decisive influence on friction, wear, and lubrication under mixed lubrication and dry friction conditions [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Friction Behaviour of 6082-T6 Aluminium Alloy Sheets in a Strip Draw Tribological Test

et al. 2023

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Aluminium alloy sheets cause many problems in sheet metal forming processes owing to their tendency to gall the surface of the tool. The paper presents a method for the determination of the kinematic friction coefficient of friction pairs. The determination of coefficient of friction (COF) in sheet metal forming requires specialised devices that ‘simulate’ friction conditions in specific areas of the formed sheet. In this article, the friction behaviour of aluminium alloy sheets was determined using the strip drawing test. The 1-mm-thick 6082 aluminium alloy sheets in T6 temper were used as test material. Different values for nominal pressures (4.38, 6.53, 8.13, 9.47, 10.63, and 11.69 MPa) and different sliding speeds (10 and 20 mm/min.) were considered. The change of friction conditions was also realised with several typical oils (hydraulic oil LHL 32, machine oil LAN 46 and engine oil SAE 5W-40 C3) commonly used in sheet metal forming operations. Friction tests were conducted at room temperature (24 °C). The main tribological mechanisms accompanying friction (adhesion, flattening, ploughing) were identified using a scanning electron microscope (SEM). The influence of the parameters of the friction process on the value of the COF was determined using artificial neural networks. The lowest value of the COF was recorded when lubricating the sheet metal surface with SAE 5W40 C3 engine oil, which is characterised as the most viscous of all tested lubricants. In dry friction conditions, a decreasing trend of the COF with increasing contact pressure was observed. In the whole range of applied contact pressures (4.38–11.69 MPa), the value of the COF during lubrication with SAE 5W40 C3 engine oil was between 0.14 and 0.17 for a sliding speed of 10 mm/min and between 0.13 and 0.16 for a sliding speed of 20 mm/min. The value of the COF during dry friction was between 0.23 and 0.28 for a sliding speed of 10 mm/min and between 0.22 and 0.26 for a sliding speed of 20 mm/min. SEM micrographs revealed that the main friction mechanism of 6082-T6 aluminium alloys sheet in contact with cold-work tool steel flattens surface asperities. The sensitivity analysis of the input parameters on the value of COF revealed that oil viscosity has the greatest impact on the value of the COF, followed by contact pressure and sliding speed.

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“…Precise studies of the LST surface topography measurement process can influence the tribological performance applications like friction [ 15 ], sealing [ 16 ], lubricant retention [ 17 ], wear [ 18 ] or wear resistance [ 19 ], corrosion [ 20 ], fatigue [ 21 ] or, generally, material contact [ 22 ] and material properties. In general, the surface topography analysis can be roughly divided into measurement and data analysis processes [ 23 ]. Each part of the surface topography study, simultaneously, can be fraught with many factors influencing the accuracy of the analysis.…”

Section: Introductionmentioning

confidence: 99%

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

Podulka

2022

Materials

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There are many factors influencing the accuracy of surface topography measurement results: one of them is the vibrations caused by the high-frequency noise occurrence. It is extremely difficult to extract results defined as noise from the real measured data, especially the application of various methods requiring skilled users and, additionally, the improper use of software may cause errors in the data processing. Accordingly, various thresholding methods for the minimization of errors in the raw surface topography data processing were proposed and compared with commonly used (available in the commercial software) techniques. Applied procedures were used for the minimization of errors in the surface topography parameters (from ISO 25178 standard) calculation after the removal and reduction, respectively, of the high-frequency noise (S-filter). Methods were applied for analysis of the laser-textured surfaces with a comparison of many regular methods, proposed previously in the commercial measuring equipment. It was found that the application of commonly used algorithms can be suitable for the processing of the measured data when selected procedures are provided. Moreover, errors in both the measurement process and the data processing can be reduced when thresholding methods support regular algorithms and procedures. From applied, commonly used methods (regular Gaussian regression filter, robust Gaussian regression filter, spline filter and fast Fourier transform filter), the most encouraging results were obtained for high-frequency noise reduction in laser-textured details when the fast Fourier transform filter was supported by a thresholding approach.

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

Cited by 26 publications

References 51 publications

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

Friction Behaviour of 6082-T6 Aluminium Alloy Sheets in a Strip Draw Tribological Test

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

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