Comparison of two metrological approaches for the prediction of human haptic perception. (9th May 2016)

• Record Type: Journal Article
• Authors: Neumann, Annika; Frank, Daniel; Vondenhoff, Thomas; Schmitt, Robert
• Is Part Of: Surface topography. Volume 4:Number 2(2016:Apr./Jun.)
• Publication Date: 2016-05-09
• Subjects: material characterization -- soft metrology -- surface topography -- perceived quality -- haptic perception -- descriptive analysis; Surfaces (Physics) -- Periodicals; Surfaces (Physics) -- Measurement -- Periodicals; 530.417
• DOI: 10.1088/2051-672X/4/2/025004
• Languages: English
• ISSNs: 2051-672X

Abstract

Abstract: Haptic perception is regarded as a key component of customer appreciation and acceptance for various products. The prediction of customers' haptic perception is of interest both during product development and production phases. This paper presents the results of a multivariate analysis between perceived roughness and texture related surface measurements, to examine whether perceived roughness can be accurately predicted using technical measurements. Studies have shown that standardized measurement parameters, such as the roughness coefficients (e.g. Rz or Ra ), do not show a one-dimensional linear correlation with the human perception (of roughness). Thus, an alternative measurement method was compared to standard measurements of roughness, in regard to its capability of predicting perceived roughness through technical measurements. To estimate perceived roughness, an experimental study was conducted in which 102 subjects evaluated four sets of 12 different geometrical surface structures regarding their relative perceived roughness. The two different metrological procedures were examined in relation to their capability to predict the perceived roughness of the subjects stated within the study. The standardized measurements of the surface roughness were made using a structured light 3D-scanner. As an alternative method, surface induced vibrations were measured by a finger-like sensor during robot-controlled traverse over a surface. The presented findings provide a better understanding of the predictability of human haptic perception using technical measurements.