The Surface roughness is one of the important factors related to the appearance and performance of products, and the interest is further increased in various kinds of manufacturing. This paper describes a novel method for measuring surface roughness by laser speckle method. In the proposed method, instead of directly fitting with surface roughness and speckle feature amount, indirect parameters are obtained by converting the functional system. The polished metal pieces are used as the sample set. The prediction formula was obtained based on the reference value measured by the conventional method, and the prediction error of the prediction formula was verified by applying the sample of unknown surface roughness. In addition, we confirmed that the prediction accuracy improves by limiting the range for constructing the prediction formula. This method enables us to quickly detect the surface roughness by using a very simple configuration and a simple analysis method. A system that uses the method can be constructed calibration curve of surface roughness.
A cleanliness evaluation system was developed that detects contamination remaining on surgical instruments by using near-infrared spectroscopy. Measurement attachments were developed that can easily measure the absorption spectrum of surgical instruments. A method for creating a discrimination database capable of highly accurate cleanliness evaluation was verified. Since the change in baseline of the absorption spectrum varies greatly depending on the shape and size of the surgical instrument, the discrimination database for cleanliness evaluation should not be created for absorption spectra that have been measured for various surgical instruments. However, after a database for the absorption spectra of one type of instrument with a nearly uniform baseline was created, it was possible to evaluate cleanliness with regard to the absorption peak of the remaining blood stains. Therefore, it is possible to develop a cleanliness evaluation system of surgical instruments by constructing respective discrimination databases for surgical instruments of different shapes and sizes.