As environmental and energy issues increase, energy efficiency is of great significance in the automobile industries. Drag torque of a wet clutch in an automatic transmission system is one of the causes of energy loss. In this work, the drag torque characteristics of a wet clutch as a function of rotational speed was experimentally investigated with respect to the test parameters such as automatic transmission fluid (ATF) temperature and flow rate, clearance between friction disk and separator, and the number of disks in the test system, using two different friction materials. Drag torque was found to decrease with increasing ATF temperature as a result of the decrease in viscosity. Also, drag torque decreased as the clearance between friction disk and separator increased. In addition, the drag torque increased along with the delay in the generation of maximum drag torque as the ATF flow rate increased. Furthermore, it was observed that drag torque increased with the increase in the number of disks in the test system. The drag torque characteristics as a function of rotational speed may be dependent on the friction materials. The results obtained from this work may aid in the design of wet clutch system to enhance performance.

3D scanning is a technique to measure the 3D shape information of the object. Shape information obtained by 3D scanning is expressed either as point cloud or as polygon mesh type data that can be widely used in various areas such as reverse engineering and quality inspection. 3D scanning should be performed as accurate as possible since the scanned data is highly required to detect the features on an object in order to scan the shape of the object more precisely. In this study, we propose the method on finding the location of feature more accurately, based on the extended Biplane SNAKE with global optimization. In each iteration, we project the feature lines obtained by the extended Biplane SNAKE into each image plane and move the feature lines to the features on each image. We have applied this approach to real models to verify the proposed optimization algorithm.