The collaboration of robots and humans sharing workspace, can increase productivity and reduce production costs. However, occupational accidents resulting in injuries can increase, by removing the physical safety around the robot, and allowing the human to enter the workspace of the robot. In preventing occupational accidents, studies on recognizing humans, by installing various sensors around the robot and responding to humans, have been proposed. Using the LiDAR (Light Detection and Ranging) sensor, a wider range can be measured simultaneously, which has advantages in that the LiDAR sensor is less impacted by the brightness of light, and so on. This paper proposes a simple and fast method to recognize humans, and estimate the path of humans using a single stationary 360° LiDAR sensor. The moving object is extracted from background using the occupied grid map method, from the data measured by the sensor. From the extracted data, a human recognition model is created using CNN machine learning method, and the hyper-parameters of the model are set, using a grid search method to increase accuracy. The path of recognized human is estimated and tracked by the extended Kalman filter.

Recently, porous structures of nano/microfibers are receiving great attention because of their excellent mechanical properties, surface area to volume ratio, and permeability. In this study, thick microfiber mats were fabricated using a melt-electrospinning process in a controlled manner. A melt-electrospinning equipment including a three-axis precision motion control with pneumatic dispensing was constructed. The diameter and deposition pattern of melt-electrospun microfibers with respect to the barrel temperature and pressure were investigated. Based on identified effects of process conditions on microfiber geometry, thick microfiber mats with various properties were successfully fabricated using melt-electrospinning with snake scanning and iterative layering. Their mechanical properties and porosities were then compared and analyzed.

Recently, carbon fiber-reinforced plastic (CFRP) has been attracting much attention in various industries because of its beneficial properties such as excellent strength, modulus per unit density, and anti-corrosion properties. However, there are several issues in its application to various fields. Severe tool wear issues in its machining have been noted as one of the most serious problems because it induces various serious machining failures such as delamination and splintering. In this regard, timely tool replacement is essential for reducing the influence of tool wear. In this study, tool wear, especially flank wear, in the CFRP drilling was investigated and monitored. First, the reproducibility of tool wear under the same machining condition was experimentally evaluated. And it is demonstrated that tool wear may remarkably differ even though the same machining condition is applied to the tools. Then, tool wear monitoring based on the feed motor torque was applied to the detection of tool life ending in the CFRP drilling process. Consequently, it was demonstrated that the average and maximum detection error of the tool life end were less than 7 and 14%, respectively.