Thermal deformation can cause loads and moments that were not anticipated at the time of design to act on linear motion guides built inside the equipment, resulting in a reduction in their service life. To avoid such failures, it is important to predict life reduction due to thermal deformation in advance using analysis. In this paper, we have verified that the finite element modeling method for linear motion guides can be used to calculate the loads and moments acting on the linear motion guides when the equipment is thermally deformed. The results show that the loads and moments acting on the linear guide can be predicted with an analysis error of 10% to 20%. In addition, the effects of temperature change on the load distribution and theoretical life of the linear guide were analyzed. For a typical single-axis table, it was found that the smaller the temperature difference between the table and the base, the longer the life of the linear guide.

This paper proposes a method of avoiding singularities and obstacles by selecting the types of inverse kinematics solution of a manipulator by reinforcement learning. Deep Q-Network (DQN) selects from eight different types of solution that enable the manipulator to avoid singularities and obstacles throughout its motion path. This proposed method is applied to a 6-DOF collaborative robot. DQN, a type of reinforcement learning, is constructed with six joint angles as observation and eight types of solution as action. The motion path of the manipulator is divided into steps every 0.1s, and the type of solution at each step is selected by DQN. The agent is rewarded when the manipulator reaches the end of its motion path, and punished when it collides with the obstacle or itself, and according to the six joint angular velocities. As a result, DQN selects the types of solution that can avoid singularities and obstacles. The proposed method makes it possible to select which of the types of solution can realize the motion path of the robot hand without colliding with obstacles and which minimize the joint angular velocities.

The authors propose using microtubes as an alternative to porous structures for improving the accuracy of release agent penetration in a die-casting mold. They evaluated the effectiveness of this method by examining the characteristics of the release agent, creating microtubes of different sizes and angles using a commercial powder bed fusion machine, and evaluating the release resistance during casting. They found that a larger setting size is needed to permeate the release agent when designing microtubes using a layer-by-layer method, and that increasing the angle of the microtube design improves the accuracy of the microtube's internal shape and stabilizes the amount of release agent penetration. The authors also found that controlling the release agent penetration through a microtube is simpler than controlling it through a porous structure, and that the release agent penetration can be controlled by adjusting the output pressure, allowing for the production of high-quality castings.