In the heating and drying system using microwaves, an optimal design method was presented to effectively shield microwaves leakage between the door and the cylindrical applicator. In order to protect the human body from leaking microwaves, it is necessary to keep the intensity of microwaves below 5 mW/cm². The door part adopts a choke structure and includes a number of design factors, such as, fin shape, slit shape, and a gap between the applicator and the door. The geometry was optimized by design of experiments, applying full factorial design and response surface method in a 4-factor, 2-level design. The results obtained by ANSYS HFSS analysis were applied to the intensity of microwave leakage according to the change of the design factors. The shape of the choke structure was optimized using Minitab, a statistical program. The microwave heating and drying system was manufactured based on optimal design value and the leakage of microwaves between the door and the applicator was measured. We confirmed that the experimental values were consistent with the simulation values.

As the digitization of the manufacturing process is accelerating, various data-driven approaches using machine learning are being developed in chemical mechanical polishing (CMP). For a more accurate prediction in contact-based CMP, it is necessary to consider the real-time changing pad surface roughness during polishing. Changes in pad surface roughness result in non-uniformity of the real contact pressure and friction applied to the wafer, which are the main causes of material removal rate variation. In this paper, we predicted the material removal rate based on pressure and surface roughness using a deep neural network (DNN). Reduced peak height (Rpk) and real contact area (RCA) were chosen as the key parameters indicative of the surface roughness of the pad, and 220 data were collected along with the process pressure. The collected data were normalized and separated in a 3 : 1 : 1 ratio to improve the predictive performance of the DNN model. The hyperparameters of the DNN model were optimized through random search techniques and 5 cross-validations. The optimized DNN model predicted the material removal rate with high accuracy in ex-situ CMP. This study is expected to be utilized in data-driven machine learning decision making for cyber-physical CMP systems in the future.

In response to the market’s need for luxurious automobile interiors, automotive parts makers are developing various types of crash pads to give drivers a sense of emotional luxury. In particular, a low-cost and high-quality crash pad manufacturing technology is being developed for mid- to low-priced vehicles, namely, the IMG-S (In Mold Grain-pre Stitch) technology. High defect rate of stitching is a critical problem during the manufacture of crash pad using the IMG-S technology. In order to solve this problem, this paper proposes a method of real-time machine vision inspection of stitches on the automotive crash pad. This paper presents the real-time machine vision inspection system configuration, proposes stitch and reference line detection methods, and method for calculating the distance between stitches and the reference line. According to the distance between the stitch and the reference line, the status of the stitch was judged as normal, warning, or erroneous, and the final result was displayed on the user interface. The applicability of the proposed real-time machine vision inspection method was verified by stitching the test line.

In this study, acoustic emission (AE) signals associated with the behavior of materials in the magnesium alloy (Mg AZ31B) tensile test were analyzed. The AE sensor was attached with the material to measure the AE signals. During the tensile experiment, the AE sensor measured the elastic waves generated inside the specimen. The AE parameters, such as, the signal energy, duration, and frequency centroid, were studied. We also analyzed the effect of the materials size and tensile speed on the AE signals. As a result, the lowest frequency centroid value occurred at the yield and fracture points. As the width and length of the specimen increased, the number of hit counts increased and the peak frequency occurred. Other AE parameters, such as, the duration and frequency centroid, were not affected. As the tensile speed increased, the hit decreased and the frequency centroid decreased in the elastic region. It was found that in the detection of the yield and fracture deformation, the number of counts, and frequency centroid were appropriate.

The subtle feature is one of the characteristic lines and represents the most noticeable line in the automotive panel. In this study, we proposed a method to predict the radius of curvature of products according to the material, its thickness, its punch angle, and its punch radius. The radius of curvature was divided into three regions, namely, the non-linear, transition, and linear regions. In the non-linear region, the prediction model for the radius of curvature with different forming conditions was derived using the finite element analysis. In the linear region, the radius of curvature was assumed to be the sum of the punch radius and the thickness of the material. In the transition region, a model connecting two regions (Non-linear and linear region) was developed based on the continuity condition. The prediction model presented a very small RMSE with the value of 0.314 mm. Using the prediction model, the radius of curvature with various forming variables could be predicted and the required radius of punch, to obtain a certain value of the radius of curvature, could be precisely predicted.

Steering Stop parts constituting the suspension system of automobiles are located inside an automobile suspension. They are used to fix upper and lower suspension arm parts by welding. The purpose of this study was to develop Steering Stop parts for automobile suspension. Cost increase due to problem of existing tool life is a challenging issue. This study tries to solve the tool life problem and reduce the cost using a former cold forging complex forming technology. We developed a long-life complex forming technology between multistage former forging and cold forging for producing Steering Stop parts of automobile suspension.

This paper presents a distortion compensation algorithm for cable-driven master devices. Such device has four string pots at four corners of a frame. Four cables are tied from the four corners to the center holder. When the central holder, which is a haptic grip, moves, lengths of the four cables will change. From the four cable lengths, the spatial position of the haptic grip can be estimated using triangulation. In this case, distortion such as barrel image of the image field occurs when estimating a position with an offset parallel to the plane in which the four string pots are located. The closer to the corner, the smaller the position estimate value is than the true value. After distortion phenomenon is modeled by projecting onto the ellipsoid, the position in the vertical direction of the cable plane is compensated by the corresponding value and flattened. The mean error in the x-direction position was improved by 91% from 0.7833±0.8381 mm to -0.0709±0.4341 mm. This cable-driven master device can be used as a haptic device for operating a surgical robot.

In this study, thin-shell surface observation, storage capability test, and micro-compressive test were performed for self-healing microcapsules using a field emission scanning electron microscope (FE-SEM) and a micro-compressive testing machine. A microcapsule having a melamine-urea-formaldehyde thin-shell and a microcapsule having a melamine-urea-formaldehyde thin-shell reinforced with carbon nanotubes were used. Two carbon nanotube contents were considered: 0.17 wt% and 0.50 wt%. Thin-wall shell state was relatively smooth when microcapsules were not reinforced with carbon nanotubes. It was uneven when microcapsules were reinforced with carbon nanotubes. Prepared microcapsules showed little decreases of weights even when the exposure time was increased regardless of whether they were reinforced with carbon nanotubes. Thus, their storage capability was good. When carbon nanotube content was the same, the fracture load was almost constant without being affected by the diameter of the microcapsule. However, fracture displacement increased with increasing diameter of the microcapsule. When diameters of microcapsules were similar, fracture load and fracture displacement increased when carbon nanotube content increased. It was found that self-healing microcapsules had good storage capability and mechanical properties. Thus, they could be applied to repair damage to composite materials if thin-shell formation mechanism for adding carbon nanotubes is supplemented.

In this paper, the relationship between various physical and chemical dynamics included in a gas cutting process was analyzed and a mathematical model was presented. To express the gas cutting process in a formula that could reflect the physics and chemical reaction dynamics, the entire process was classified into three stages: flame spurt, metal oxidation, and metal oxide melting. Flame spurt is caused by combustion of fuel gas and oxygen. It was modeled through fluid dynamics, chemical species transport, and reaction kinetics. Metal oxidation was modeled as a chemical reaction of surface oxidation and oxide growth based on temperature and concentration of species of the metal surface obtained through flame and cutting oxygen spurt results. Finally, the melting of metal oxide was expressed as a rate equation based on melting conditions, heat flux obtained in the previous two stages, and changed properties of the metal. The presented mathematical model could analyze dynamic relationships for each stage of a gas cutting process and connect them into one process. Results of this study can be used as basic data for future finite element analysis and simulations.

Estimation and compensation of geometric errors for rotary axes are among methods to improve machining accuracy of five-axis machine tools. Studies have been conducted on various methodologies for estimating geometric errors for rotary axes, which are essential for improving machining accuracies of five-axis CNC machine tools. This paper presents a method for estimating geometric errors of a rotating/tilting table using a cross-shaped calibration artifact with a touch trigger probe. The proposed method includes rotary axes error estimation equations for angles of each rotary and tilt axis based on locations of probing points. Computer simulations were performed based on a MATLAB/Simulink and ADAMS cosimulation system using the probing cycle process to verify the proposed method. Computer simulation results confirmed the usefulness of the proposed method in terms of volumetric errors.