Purpose -The main aim of this paper is description of new apparatus and approach for contact less evaluation of surface roughness. For characterization of surface roughness, the procedures based on classical and non-classical (complexity) parameters are proposed. Design/methodology/approach -For obtaining the roughness profile in the selected direction (on the line transect of the surface), the special arrangements of textile bend around sharp edge is used. The image analysis is used for extraction of surface profile. The system of controlled movement allows one to obtain surface roughness profile in two dimensions. Findings -By using aggregation (cut length principle), the roughness resolution is decreased and roughness profile is created without local roughness variation. After application of cut length principle, the direct combination of slices leads to the creation of roughness surface. Research limitations/implications -There exists plenty of roughness characteristics based on standard statistics or analysis of spatial processes. For evaluation of suitability of these characteristics, it will be necessary to compare results from sets of textile surfaces. Practical implications -The measurement of fabric roughness by an RCM device is useful as simple tool for description of roughness in individual slices and in the whole rough plane. This method replaces the traditional contact stylus profiling methods Originality/value -The reconstruction of surface roughness from individual slices. The utilization of aggregation principle for creation of micro and macro roughness. The evaluation of roughness parameters based on the geometrical characteristics, harmonic analysis and complexity indices.
The surface roughness is one of the main parts of hand prediction. Classical method of surface roughness measurements is based on the surface profile measurement. Characteristic of roughness is then variation coefficient of surface profile (surface height variation). The main aim of this work is to estimate the surface profile complexity by using variogram (structure function). The surface profile variation is classified to the group according to short-and long-range dependence. The concept of fractal dimension is proposed especially for long-term correlation cases. The applicability of the proposed approach is demonstrated on the typical heat protective clothing fabrics and compared with the results of surface roughness evaluated by the KES system.
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