Numerous research efforts have been reported in the literature on the positioning accuracy of a six-axis robot, but very few research have studied its orientation accuracy. Especially in robot machining, the end effector’s orientation accuracy plays a major role in the surface finish of the work piece. Typically, the surface of the work piece machined by a robot can be significantly worse than that the CNC milled surface, and it is to some extent attributable to the robot’s orientation errors.This research first measures the orientation accuracy of a six-axis robot by the following two different measurement schemes. The static and dynamic orientation error over a single line is measured using a laser interferometer. Then, the orientation error over the entire workspace is measured using an electronic level. The results obtained are compared with that of the simulated model based on the model developed by the authors’ group, which contains the angular positioning deviations of rotary axes as error sources, in addition to the Denavit-Hartenberg parameters in the conventional model. The performance of the present model in predicting the end effector orientation errors is validated by experiment.

Thermal displacement affects the machining accuracy of machine tools, and various countermeasures are being considered. For example, there are methods to suppress the temperature rise of the machine by cooling the heat source. The most common method for cooling heat sources is to circulate cooling water at a constant temperature, but this involves an increase in cost and energy consumption. This study devised a way to stabilize machining accuracy by utilizing the environmental characteristics of a factory and devising a machine tool bed structure that utilizes air with a relatively stable ambient temperature.

Gear skiving is a technique for high-precision machining for internal gears of machinery. In order to estimate the timing of tool replacement accurately, quickly evaluation for the tool wear amount on the machine. In this study, we developed an on-machine system that can easily estimate the amount of tool wear by observing images using a camera. The system configuration and image processing method for extracting tool geometry are described, and basic experiments to detect the amount of wear with micron-order accuracy are reported.