In general, rotor inertia has an inversely proportional relationship with proportional gain and bandwidth in a turret speed control system of machine tools; thus, this system has a disadvantage, such as weak disturbance caused by a decrease in the damping ratio and an increase in bandwidth due to low rotor inertia. This paper proposes a damping compensator that is resistance to disturbances in order to improve the above problems. The proposed damping compensator reduces the residual vibration induced in the transient state by using a digital high-pass filter. The experimental results showed that the overshoot was reduced by about 5.5% in the speed response and by about 20% in the torque response in the no-load condition. Under the load condition of 4.8 N.m, the torque response showed that the undershoot was reduced by about 26%.

Damage to the units related to driving and running of the railway vehicle may cause an inevitable accident due to defects and malfunctions in operation. In order to prevent such an accident, a non-destructive diagnostic technology that detects the damage is required. Previous researchers have researched and developed a monitoring system of the infrared thermography method to diagnose the condition of the railway vehicle driving and driving units. A system for monitoring running of the railway vehicle and temperature condition of the drive unit at a vehicle speed of 30 to 100 km/h was constructed, and a study on its applicability was conducted. In this study, a system for diagnosing an abnormal condition of the driving and running units while the vehicle is running with an infrared thermography diagnostic system was installed in the depot and operation route, and evaluation of the abnormal condition of the driving and running units was performed. The results show that the diagnosis system using infrared thermography can be used to identify abnormal conditions in the driving and running units of a railway vehicle. The diagnosis system can effectively inspect the normal and abnormal conditions in operation of a railway vehicle.

Germanium, an optical material, has high transmittance and refractive index and low light scattering in the infrared region, and research is being conducted to utilize it in various industrial fields. Various forms of optical lenses can be subjected to ultra-precision machining with high quality surface roughness, and they form accuracy through single point diamond turning (SPDT). In particular, the diamond tool with a negative rake angle and the u-LAM process that applies a 1,064 nm laser to the material have been studied to fabricate brittle materials into optical lenses. In this study, the effects of process parameters, such as laser power (W), spindle speed (RPM), feed rate (mm/min), and depth of cut (μm), on the surface roughness of a sub-nanometer scale and the occurrence of defects during the machining process were analyzed for Germanium materials. The process of removing these defects was also analyzed.

Advances in cell culture technology have improved the understanding of the physiological principles of cells. Recently, the development of microfluidic chips has made it possible to observe single cells in a massively parallelized and accurate manner. However, in order to maximize the availability of the microfluidic cell chip, it is essential to use an incubator that can isolate the cell culture chip from the outside while minimizing contamination and maintaining the temperature and humidity required for cell culture for a long time period. Here, we developed a thermo-hygrostat incubator consisting of an Arduino-based feedback control module for controlling a temperature and humidity complex sensor, a humidifier, and a heater. The temperature and humidity of the incubator could be actively changed according to the needs and application by simple editing control variables of Arduino coding. To demonstrate the efficiency of the device, we conducted an experiment comparing the growth of bacterial cells and obtained optimal conditions necessary for culture. In conclusion, it is expected that the newly developed thermo-hygrostat incubator can be used for a variety of purposes that require active control of temperature and humidity, as well as for long-term cultivation of bacterial cells inside a microfluidic chip.

As the market for minimally invasive procedures developed rapidly, there was an increase in the demand for high-precision, high-performance catheter fabrication technology. Sheath and dilator tubes are essential intervention devices for procedures, in which catheters are used and require precise dimensional accuracy, and uniform roundness and surface roughness. Polyethylene is used in sheath and dilator limitation for processability, which causes low melt flow index and side effects. Therefore, in the extrusion process using polyethylene, it is important to study the manufacturing of tubes with improved roundness and surface roughness. In this study, we proposed a calibrator for precise production with an aim to manufacture 5Fr micro-puncture tubes, and studied the changes in the roundness and surface roughness of tubes by changing the cooling water temperature and water disk thickness. As a result, it was found that the cooling water temperature and wafer disk thickness had an effect on the roundness and surface roughness, and the roundness had an effect on the formation of the wall thickness. Therefore, these experimental results were used as a study for the production of improved Sheath and Dilator tubes.

Deep reinforcement learning (RL) has attracted research interest in the manufacturing area in recent years, but real implemented applications are rarely found. This is because agents have to explore the given environments many times until they learn how to maximize the rewards for actions, which they provide to the environments. While training, random actions or exploration from agents may be disastrous in many real-world applications, and thus, people usually use computer generated simulation environments to train agents. In this paper, we present a RL experiment applied to temperature control of a chamber for ultra-precision machines. The RL agent was built in Python and PyTorch framework using a Deep Q-Network (DQN) algorithm and its action commands were sent to National Instruments (NI) hardware, which ran C codes with a sampling rate of 1 Hz. For communication between the agent and the NI data acquisition unit, a data pipeline was constructed from the subprocess module and Popen class. The agent was forced to learn temperature control while reducing the energy consumption through a reward function, which considers both temperature bounds and energy savings. Effectiveness of the RL approach to a multi-objective temperature control problem was demonstrated in this research.

A clean room is used for adjusting the concentration of suspended particles using an air-conditioner. It has a fan-filter unit combining a centrifugal fan and a high-efficiency particulate air filter that purifies the outside air and directly affects its cleanliness. Defects in these systems are typically detected using special sensors for each fault, which can be costly. Therefore, this paper proposes a system for diagnosing defects in the fan-filter unit using a single differential sensor and deep learning. The fan-filter unit is part of the air-conditioning system, and it is usually defective in bearings, filters, and motors. These faults include ball wear, internal bearing contamination, filter contamination, and motor speed changes. Each defect was artificially induced in experiments, and the differential pressure data of each defect was learned using a long short-term memory (LSTM) deep learning algorithm. The results of deep learning experiments generated by randomly mixing data five times were presented using a confusion matrix, and the results showed an accuracy of 87.2±2.60%. Therefore, the possibility of diagnosing defects in the fan-filter unit using a single sensor was confirmed.

Hybrid additive manufacturing (AM) refers to a combination of two metal AM techniques: material deposition by powder bed fusion (PBF) and additional building by directed energy deposition (DED). This study focused on different characteristics in accordance with relative deposition directions of PBF and DED during hybrid AM production. Characteristics of the sample fabricated by hybrid AM (i.e., hybrid sample) were compared with those of the sample fabricated by PBF or DED. Ferrite was dominant in the microstructure of PBF deposits with very fine retained austenite observed locally. In contrast, lath martensite and retained austenite were formed uniformly in the microstructure of DED deposits. Different microstructures in the two processes were attributed to differences of cooling rate. In DED deposits, microhardness was significantly decreased owing to a high retained austenite fraction. However, in the hybrid sample, microhardness was rapidly increased in the HAZ owing to aging heat treatment for long-term deposition. Principal wear mechanisms of PBF and DED samples were oxidative wear and plastic deformation, respectively.

Improving battery performance is crucial for increasing drone flight time. However, developing individual parts can also enhance mission performance and extend operating time. By attaching a drone to a wall instead of hovering in the air, the operating time and range of task performance can be extended. This study focuses on the adhesion force of a modular permanent magnet wheel leg for wall climbing drones. The wheel leg comprised several spokes without a rim. It could climb obstacles higher than wheel radius and provide a large adhesion area. An equation for the adhesion force of the wheel leg was derived, considering mechanical factors such as drone size, inclination of the ferromagnetic wall, and drone posture. A simple experimental model was created to verify the validity of the adhesive force equation. The effectiveness of the derived equation was confirmed by experimentally measuring the angle of the ferromagnetic wall that losT adhesion according to mechanical factors and comparing it with the derived adhesion force.

This paper presents a vision system for machined hole quality inspection of mechanical parts in an automobile. Automobile parts have various shapes and holes created by press punches. However, if the press punch pin is broken, a hole is not created on the mechanical parts. This problem causes serious part quality defects. To solve this problem, we proposed a vision system that could easily and cheaply inspect the quality of holes in automotive machining parts. A software development environment was created to build an economical vision inspection system. Images were gathered using the Near-real-time method to overcome the low frame-per-second of inexpensive Complementary Metal Oxide Semiconductor (CMOS) webcams. Status of the hole was determined using template matching and distance between holes as a feature. The hardware required for vision inspection was designed so that it could be directly applied to the automotive part manufacturing process. When the proposed vision inspection system was tested by installing it in an automobile parts factory for 3 months, the system showed an inspection accuracy of at least 97.9%. This demonstrates the effectiveness of the proposed method with accuracy and speed of hole defect inspection of machined parts.