Geisler, Tobias
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Publication Access status: Metadata only , Layer thickness measurement of technically anodised aluminium surfaces by using goniometric scattered light(2021); In recent years, scattered light measurement technology has developed into a common method for measuring roughness, form and waviness on precision machined surfaces.Meanwhile, the application for the material structure evaluation of electrolytically anodized surfaces has also been considered.In this context, we present a novel approach to layer thickness measurement of naturally anodised aluminium surfaces.Our approach is based on the reflection intensity of the light beam, which penetrates the oxide layer and is reflected back from the surface as well as from the layer base.In the approach, a model for estimating reflection intensity I from the absorption coefficient is employed.The methodology is tested by comparing results to a layer thickness evaluation using metallographic preparation.Based on the proposed approach, we are able to measure intervals of layer thicknesses on naturally anodized aluminium surfaces without contact.Source Type:Article - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , PATTERN RECOGNITION OF ROUGH SURFACES BY USING GONIOMETRIC SCATTERED LIGHT(2018); Nowadays, most techniques for evaluating rough metal surfaces are based on tactile or confocal measurement procedures.However, these technologies have disadvantages in respect to measuring speeds, resistance to vibration, impact and dust.In this paper we present a novel surface measurement approach, which uses the scattering light technology.Our approach enhances the state-of-the-art scattering light-based surface measurement methodology in both the detector setup and evaluation of the raw intensity values acquired by the scattered light device.The main goal in optimizing the measurement setup is to capture scattering parameters for rough surfaces in a range greater than 10 µm based on an enlarged detector array.Regarding the evaluation, we propose a pattern recognition approach which maps the reflection intensity I back to material structures and the tenpoint mean roughness Rz, the golden standard in tactile roughness characterization.Based on this approach, we are able to classify rough surface deviations like stripes using a simple but robust thresholding.In order to demonstrate the generality of our approach, we evaluate our approach using two rather different materials, i.e. brushed stainless steel and anodized aluminium.Source Type:ArticleDOI:10.24425/1181601
