Development of a Visual Simulation Method for Quantifying Sensory Evaluation of Machined Surface

Abstract

Machined surfaces are judged to determine machining conditions. Two types of criteria are used: one is quantitative parameters including surface roughness and the other is sensory parameters of a tester, such as glossiness, iridescence and sense of discomfort. Sensory parameters and their representations are dependent on the experience and personality of the tester. Consequently, we cannot evaluate machined surfaces quantitatively considering human sensory parameters. In this research, we aim to classify sensory parameters, and evaluate machined surfaces quantitatively. To investigate the sensory parameters, it is necessary to simulate human visual information such as luminance and color. In this paper, we propose a visual simulation method to calculate visual information directly using shape data of a machined surface based on Beckmann’s theory on the scattering of electromagnetic waves. As the first case study, we applied the proposed method to a turned aluminum surface and simulated intensity distribution of reflected light agreed with microscope image. As the second case study, the proposed simulation was applied to an iridescent surface and its color was simulated. The results show that the simulated color transition by the observation direction agreed well with photographs.