Fine blanking is a press processing technology that can process most of the product thickness into a smooth surface with a single stroke. In this fine blanking process, shear is an essential step. The punches and dies used in the shear are subjected to impacts of tens to hundreds of gravitational accelerations, depending on the type and thickness of the material. Therefore, among the components of the fine blanking mold (dies), punches and dies are the parts with the shortest lifespan. In the actual production site, various types of tool damage occur such as wear of the tool as well as sudden punch breakage. In this study, machine learning algorithms were used to predict these problems in advance. The dataset used in this paper consisted of the signal of the vibration sensor installed in the tool and the measured burr size (tool wear). Various features were extracted so that artificial intelligence can learn effectively from signals. It was trained with 5 features with excellent distinguishing performance, and the SVM algorithm performance was the best among 33 learning models. As a result of the research, the vibration signal at the time of imminent tool replacement was matched with an accuracy of more than 85%. It is expected that the results of this research will solve problems such as tool damage due to accidental punch breakage at the production site, and increase in maintenance costs due to prediction errors in punch exchange cycles due to wear.

A helmet is essential for safety when operating personal mobility. However, user's actual helmet wear rate is low due to the inconvenience of wearing and poor ventilation. In this study, a new helmet structure with improved ventilation for personal mobility devices was designed. To design a new structure with improved breathability compared to the existing helmet while satisfying the safety regulations for the helmet, a generative design method was applied to the shock-absorbing liner of the helmet. In addition, other materials were applied to create a structure with improved ventilation while maintaining safety. The generated design result was verified for shock absorption through simulation. As a result of the study, EPS, the current material was replaced with CFRP and Kevlar, and the structure was changed. This design was judged to satisfy safety regulations against impact. The new helmet structure is expected to improve the helmet usability for personal mobility and increase the helmet wear rate of users.

Recently, several studies are being conducted to achieve a curvature of 180° or more for the edge of the display glass. The thermocompression molding process is applied to the manufacture of curved glass, and high hardness G5 graphite is used as the mold material to withstand the impact applied to the mold. G5 graphite has high hardness and strong brittleness, which makes tool wear and surface damage easy during machining. Therefore, the demand for diamond-coated tools with good mechanical properties is increasing in the G5 machining field. In this study, the optimal cutting conditions and machinability of a nanodiamond (NCD) coated ball end mill being developed by a tool manufacturer were analyzed and evaluated. For this purpose, the same test was performed on the microdiamond (MCD) coated ball end mill and compared together. In summary, the machinability of MCD and NCD coated tools showed better cutting performance at a cutting speed of 282 m/min, a feed rate of 1,400 mm/min, and a radial depth of cut of 0.08 to 0.1 mm.

Fiber-reinforced plastics(FRPs) have excellent specific stiffness and strength, so they are usually used as automotive parts that require high rigidity and lightweight instead of metal. However, it is difficult to predict the mechanical properties of injection molded parts due to the fiber orientation and breakage of FRPs. In this paper, the fiber orientation characteristics and mechanical properties of injection molded specimens were evaluated in order to fabricate automotive transmission side covers with FRPs and design a rib structure for improvement of their rigidity. The test molds were designed and manufactured to confirm the fiber orientation characteristics of each position of the injection molded standard plate-shaped specimens, and the tensile properties of the specimens were evaluated according to the injection molding conditions and directions of specimens. A gusset-rib structure was designed to improve the additional structural rigidity of the target products, and a proper rib structure was selected through the flexural tests of the rib-structured specimens. Based on the evaluation of fiber orientation and mechanical characteristics, the optimization analyses of gate location were performed to minimize the warpage of target products. Also, the deformation analyses against the internal pressure of target product were performed to confirm the rigidity improvement by gusset-rib structure. As a result, it could be confirmed that the deformation was reduced by 27~37% compared to the previous model, when the gusset-rib structure was applied to the joining part of the target products.

Sustained economic development around the world is accelerating resource depletion. Research and development of secondary batteries that can replace them is also being actively conducted. Secondary batteries are emerging as a key technology for carbon neutrality. The core of an electric vehicle is the battery (secondary battery). Therefore, in this study, the temperature change by the heat source of the hammer and the rotational speed (rpm) of the abrasive disc of the Classifier Separator Mill (CSM) was repeatedly calculated and analyzed using the heat flow simulation STAR-CCM+. As the rotational speed (rpm) of the abrasive disk increases, the convergence condition of the iteration increases. Under the condition that the inlet speed of the Classifier Separator Mill (CSM) and the heat source value of the disc hammer are the same, the disc rotation speed (rpm) and the hammer temperature are inversely proportional. As the rotational speed (rpm) of the disc increases, the hammer temperature decreases. However, since the wear rate of the secondary battery material increases due to the strong impact of the crushing rotational force, it is determined that an appropriate rpm setting is necessary. In CSM (Classifier Separator Mill), it is judged that the flow rate difference is not significantly different in the direction of the pressure outlet (Outlet 1) right above the classifier wheel with the fastest flow rate. Because the disc and hammer attachment technology is adhesive, the attachment point may deform when the temperature of the hammer rises. Therefore, it is considered necessary to develop high-performance adhesives and other adhesive technologies.

The EV electric vehicle market is growing rapidly worldwide. An electric vehicle means a vehicle that uses energy charged through an electricity source as power. The precision of the press is important to mass-produce the drive motor, which is a key component of the electric vehicle. The size of the driving motor is increasing, and The size of the mold is also growing. In this study, the precision of large high-speed presses for mass production of driving motors was measured. A study was conducted on the measurement method of press and the analysis of measurement data. A drive motor is a component that transmits power by converting electrical energy into kinetic energy. EV driven motors have key material properties to improve efficiency. The material properties are the thickness of the material. As a method for improving performance, use a 0.2mm thin steel sheet. Mold is also becoming larger. As the mold grows, the size of the high-speed press for mass production of the driving motor is also increasing. Also, the precision of the press is the most important because it uses a thin iron plate material. So the importance of large press precision is being emphasized. In this study, the effect of large high-speed press structure on precision was verified

The piercing process of creating holes in sheet metals for mechanical fastening generates high shear force. Real-time monitoring technology could predict tool damage and product defects due to this severe condition, but there are few applications for piercing high-strength aluminum. In this study, we analyzed the load signal to predict the punch's wear level during the process with a piezoelectric sensor installed piercing tool. Experiments were conducted on Al6061 T6 with a thickness of 3.0 mm using piercing punches whose edge angle was controlled by reflecting the wear level. The piercing load increases proportionally with the level of tool wear. For example, the maximum piercing load of the wear-shaped punch with the tip angle controlled at 6 degrees increased by 14% compared to the normal-shaped punch under the typical clearance of 6.7% of the aluminum piercing tool. In addition, the tool wear level increased compression during the down-stroke, which is caused by lateral force due to the decrease in the diameter of pierced holes. Our study showed the predictability of the wear level of punches through the recognition of changes in characteristic elements of the load signal during the piercing process.

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

Recently, with the development of industry and the improvement of living standards, various wastes are generated along with the production of various products. Most of these wastes are used as containers for products, and plastic or aluminum is used. Various attempts are being made to automate the classification of these wastes due to the high labor cost, but most of them are solved by manpower due to the geometrical shape change due to the nature of the waste. In this study, in order to automate the waste sorting task, Deep Learning technology is applied to a robot system for waste sorting and a vision system for waste sorting to effectively perform sorting tasks according to the shape of waste. As a result of the experiment, a Deep Learning parameter suitable for waste sorting was selected. In addition, through various experiments, it was confirmed that 99% of wastes could be selected in individual & group image learning. It is expected that this will enable automation of the waste sorting operation.

Injection molding is a cyclic process comprising of cooling phase as the largest part of this cycle. Providing efficient cooling in lesser cycle times is of significant importance in the molding industry. Recently, lots of researches have been done for rapid cooling of a hot-spot area using CO2 in injection molding. The CO2 flows under high pressure through small, flexible capillary tubes to the point of use, where it expands to create a snow and gas mixture at a temperature of -79℃. The gaseous CO2 removes heat from the mold and releases it into the atmosphere. In this paper, a CO2 cooling module was applied to an injection mold in order to cool a large area cavity uniformly and quickly, and the cooling performance of the injection mold was investigated. The product was a high-curvature molded part with a molding area of 300x100mm. Heat cartridges were installed in a stationary mold, and CO2 cooling module was inserted inside a movable mold. Through structural analysis, it was confirmed that the maximum deformation of mold with CO2 cooling module was 0.09mm. A CO2 feed system with a heat exchanger was used for cooling experiments. The CO2 was injected into the holes on both sides of the supply pipe of the cooling module and discharged through hexagon blocks to cool the mold. It took 5.8 seconds to cool the mold from an average temperature of 140℃ to 70℃. Through the experiment using CO2 cooling module, it was found that a cooling rate of up to 12.98℃/s and an average of 10.18℃/s could be achieved.