Framework for Capturing and Editing of Anisotropic Effect Coatings

Abstract

Coatings are used today for products, ranging from automotive production to electronics and everyday use items. Product design is taking on an increasingly important role, where effect pigments come to the fore, offering a coated surface extra optical characteristics. Individual effect pigments have strong anisotropic, azimuthaly‐dependent behaviour, typically suppressed by a coating application process, randomly orienting pigment particles resulting in isotropic appearance. One exception is a pigment that allows control of the azimuthal orientation of flakes using a magnetic field. We investigate visual texture effects due to such an orientation in a framework allowing efficient capturing, modelling and editing of its appearance. We captured spatially‐varying BRDFs of four coatings containing magnetic effect pigments. As per‐pixel non‐linear fitting cannot preserve coating sparkle effects, we suggest a novel method of anisotropy modelling based on images shifting in an angular domain. The model can be utilized for a fast transfer of desired anisotropy to any isotropic effect coating, while preserving important spatially‐varying visual features of the original coating. The anisotropic behaviour was fitted by a parametric model allowing for editing of coating appearance. This framework allows exploration of anisotropic effect coatings and their appearance transfer to standard effect coatings in a virtual environment.

Design Optimization for the Coating of Machine Tools Based on Eye-Tracking Experiments and Virtual Reality Technology

“…Aydin et al [11] determined the optimum CNC machining parameters for the wood surface quality through the optimization and adjustment of CNC machining parameters by ANN (artificial neural network). Filip et al [12] proposed a new method that can be used to capture and edit the anisotropic behavior of effect coatings, allowing users to quickly explore and evaluate the visual effects of anisotropy on effect coatings in a virtual environment. Katırcı et al [13] developed an artificial intelligence method to automatically classify coated parts, which has shown great potential in controlling the plating process parameters.…”