Quantitative Evaluation of Machined-Surface Gloss Using Visual Simulation and its Application to Sensory Test

Abstract

In the machining field, the quality of a machined surface is characterized using both quantitative and sensory parameters. It is important to quantitatively evaluate sensory parameters to automate the evaluation of machined surfaces and determine the machining conditions. In this study, we quantitatively evaluate the gloss degree, which is a sensory parameter, via visual simulation. The gloss degree is evaluated based on an angular luminance distribution for machined surfaces cut using different tools. Using the quantitative evaluation result, observation is conducted to predict the appearance of the machined surface, and a sensory test is performed. The result shows that the quantitative evaluation results are consistent with the sensory test results.