• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.18 no.5
• /
• pp.483-490
• /
• 2009

The objective of this development project is to localize an automatic molten metal supplier that has been distributed by WESTOMAT, Germany, throughout the world. To achieve this purpose, an energy-saving pressurized dosing furnace and molten metal differential pressure control system that Is able to automatically supply a determined quantity of aluminum molten metal were developed. The localized equipment was installed in a site. Also, the results of the test operation of this equipment can be summarized as follows: It was able to improve the productivity because there were small decreases in supplying speeds and small losses in wastes compared to the existing mechanical molten metal supplier. Also, it was able to minimize the cost in maintenances due to the direct application of high temperature molten metals to molds. In addition, there were small energy losses due to the use of high thermal insulators compared to the existing reverberating furnace and able to prolong the life-time of furnaces and produce good quality nonferrous metals because it represented small carbon refractories and alumina in applied molten metals. Furthermore, it demonstrated no particular differences by objectively comparing it with the product by WESTOMAT.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.9
• /
• pp.90-96
• /
• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.17 no.6
• /
• pp.98-104
• /
• 2018

Deflected 4-way panels of ceiling air conditioners produced by injection molding process have caused dew condensation at the edge of products. In order to prevent this drawback with reducing weight and deformation, this study proposed renovated process adopting microcellular foaming. According to results from 2-sample t-test and analysis of variance(ANOVA), the critical factors affecting weight were melt temperature and injection speed. In addition, the vital effects on deformation were structure at the edge, mold temperature and cooling time. Optimal conditions of these parameters were derived by regressive analysis with CAE and response surface method(RSM), and then applied to an actual design and process stage to analyze performance. As a results, it clearly showed that new process improved process capability as well as reduced both weight and deformation by 18.8% and 71.9% respectively compared to the conventional method.

• Design & Manufacturing
• /
• v.15 no.3
• /
• pp.1-6
• /
• 2021

An artificial neural network model based on a deep learning algorithm is known to be more accurate than humans in image classification, but there is still a limit in the sense that there needs to be a lot of training data that can be called big data. Therefore, various techniques are being studied to build an artificial neural network model with high precision, even with small data. The transfer learning technique is assessed as an excellent alternative. As a result, the purpose of this study is to develop an artificial neural network system that can classify burr images of light guide plate products with 99% accuracy using transfer learning technique. Specifically, for the light guide plate product, 150 images of the normal product and the burr were taken at various angles, heights, positions, etc., respectively. Then, after the preprocessing of images such as thresholding and image augmentation, for a total of 3,300 images were generated. 2,970 images were separated for training, while the remaining 330 images were separated for model accuracy testing. For the transfer learning, a base model was developed using the NASNet-Large model that pre-trained 14 million ImageNet data. According to the final model accuracy test, the 99% accuracy in the image classification for training and test images was confirmed. Consequently, based on the results of this study, it is expected to help develop an integrated AI production management system by training not only the burr but also various defective images.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.2
• /
• pp.86-92
• /
• 2021

Usually, the die-cast components used in small mobile devices require finishing processes, such as computer numerically controlled coating. In such cases, porosity is the most important defect. The shape of the molten aluminum that passes through the runner and gate in a mold is the one of the factors that influences gas porosity. To define the spurt index, which numerically indicates the shape of molten aluminum after the gate, Reynolds number and Ohnesorge number are used. Before die fabrication, computer-aided engineering analysis is performed to optimize the filling pattern. Finally, X-ray and surface inspection are performed after casting and machining to evaluate how the spurt index affects porosity and other product parameters. Based on the results obtained herein, a new gating system design process is suggested.

• Design & Manufacturing
• /
• v.15 no.4
• /
• pp.57-64
• /
• 2021

In recent materials industry, researches on the technology to manufacture super hydrophobic surface by effectively controlling the wettability of solid surface are expanding. Research on the fabrication of super hydrophobic surface has been studied not only for basic research but also for self-cleaning, anti-icing, anti-friction, flow resistance reduction in construction, textile, communication, military and aviation fields. A super hydrophobic surface is defined as a surface having a water droplet contact angle of 150 ° or more. The contact angle is determined by the surface energy and is influenced not only by the chemical properties of the surface but also by the rough structure. In this paper, maskless lithography using DMD, electro etching, anodizing and hot embossing are used to make the polymer resin PMMA surface super hydrophobic. In the fabrication of microstructure, DMDs are limited by the spacing of microstructure due to the structural limitations of the mirrors. In order to overcome this, maskless lithography using a transfer mechanism was used in this paper. In this paper, a super hydrophobic surface with micro and nano composite structure was fabricated. And the wettability characteristics of the micro pattern surface were analyzed.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.2
• /
• pp.74-84
• /
• 2022

As the semiconductor industry develops, the difficulty of newly required process technology becomes difficult, and the importance of production yield and product reliability increases. As an effort to minimize yield loss in the manufacturing process, interests in the process defect process for facility diagnosis and defect identification are continuously increasing. This research observed the plasma condition changes in the multi oxide/nitride layer deposition (MOLD) process, which is one of the 3D-NAND manufacturing processes through optical emission spectroscopy (OES) and monitored the result of whether the change in plasma characteristics generated in repeated deposition of oxide film and nitride film could directly affect the film. Based on these results, it was confirmed that if a change over a certain period occurs, a change in the plasma characteristics was detected. The change may affect the quality of oxide film, such as the film thickness as well as the interfacial surface roughness when the oxide and nitride thin film deposited by plasma enhenced chemical vapor deposition (PECVD) method.

• Design & Manufacturing
• /
• v.16 no.3
• /
• pp.22-27
• /
• 2022

The demand for new processing technology that can improve productivity is increasing in industries that require large-scale and various products. In response to this demand, a robot machining system with flexibility is required. Because of the low rigidity of the robot, the robot machining system has a large error during machining and is vulnerable to vibration generated during machining. Vibration generated during machining deteriorates machining quality and reduces the durability of the machine. To solve this problem, a stage for fixing the spindle during machining is required. In order to compensate for the robot's low rigidity, a system combining a piezoelectric actuator for generating a large force and a guide mechanism to actuate with a desired direction is required. Since the rigidity of flexible hinges varies depending on the structure, it is important to optimal design the flexible hinge and high-rigidity system. The purpose of this research is to make analytic model and optimize a flexible hinge and to design a high rigidity stage. In this research, to design a flexible hinge stage, a concept design of system for high rigidity and flexure hinge modeling is carried out. Based on analytic modeling, the optimal design for the purpose of high rigidity is finished and the optimal design results is used to check the error between the modeling and actual simulation results.

• Smart Media Journal
• /
• v.11 no.9
• /
• pp.47-55
• /
• 2022

Quality control in the smart factory manufacturing process is an important factor. Currently, quality inspection of home appliance manufacturing parts produced by the mold process is mostly performed with the naked eye of the operator, resulting in a high error rate of inspection. In order to improve the quality competition, an automatic defect detection system was designed and implemented. The proposed system acquires an image by photographing an object with a high-performance scan camera at a specific location, and reads defective products due to scratches, dents, and foreign substances according to the vision inspection algorithm. In this study, the depth-based branch decision algorithm (DBD) was developed to increase the recognition rate of defects due to scratches, and the accuracy was improved.

• Design & Manufacturing
• /
• v.17 no.2
• /
• pp.1-8
• /
• 2023

This study aims to enhance the price and quality competitiveness of imported tiles by developing a robotic tile production automation line. The development process involved several steps, such as requirement analysis, derivation of technical specifications, conceptual design, engineering feasibility review, detailed design, and production. Emphasis was placed on the transfer process of the tiles' molds, and technological advancements were achieved through engineering interpretation, feasibility review, and performance evaluation. The developed automation system incorporates key specifications to ensure a transfer success rate of over 90%, thereby ensuring stable transportation of the tiles and minimizing defect rates during production. The maximum weight capacity for tile pick-up was set above 6 kg, allowing effective handling of tiles weighing 6 kg or less in automated tasks. Furthermore, the system enables safe and precise movement of the tiles to the desired location, with a transfer distance of at least 1.3 m and a transfer speed exceeding 0.2 m/sec, thereby increasing production efficiency.