Journal of the Korean Society for Precision Engineering

Tape-casting Process Electrochemical Characteristic Test for Fabrication of LST-GDC for Anode Supported SOFCs: Min Ji Kim, Chunghyun Kim, Young-Beom Kim; J. Korean Soc. Precis. Eng. 2025;42(11):937-942.
Published online November 1, 2025; DOI: https://doi.org/10.7736/JKSPE.025.073

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In this study, we developed a composite anode support composed of La-doped SrTiO₃ (LST) and Gd-doped CeO₂ (GDC) using a tape casting process for solid oxide fuel cells (SOFCs). By adjusting the pore former content in the slurry, we constructed a bilayered structure consisting of a porous anode support layer (ASL) and a dense anode functional layer (AFL) with the same material composition. The number of tape-cast sheets was controlled to tailor the overall thickness, and lamination followed by co-sintering at 1250^oC resulted in a mechanically robust bilayer. We characterized the microstructural evolution concerning sintering temperature and pore former content using SEM, while XRD confirmed the phase stability of LST and GDC. The measured electrical conductivity at 750^oC ensured sufficient electron transport. To enhance interfacial adhesion and suppress secondary phase formation, we introduced a GDC buffer layer and a pre-sintering treatment prior to electrolyte deposition. A full cell with a YSZ electrolyte and LSCF cathode achieved a stable open circuit voltage of approximately 0.7 V and demonstrated continuous operation at 750^oC. These findings highlight the suitability of LST-GDC composite anodes as thermochemically stable supports, potentially enabling direct hydrocarbon utilization in intermediate-temperature SOFCs.

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A Case Study on Productivity Innovation through Convergence of Sand 3D Printing Technology: Kuk-Hyun Han, Jin-Wook Baek, Sang Yun Park, Tae Wan Lim, Ju Min Park; J. Korean Soc. Precis. Eng. 2021;38(9):651-657.
Published online September 1, 2021; DOI: https://doi.org/10.7736/JKSPE.021.073

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Recently, competition in the manufacturing industry related to the preoccupation of new markets has drastically changed due to the increase in small quantity batch production products. Besides, business models utilizing 3D printing technology suitable for flexible manufacturing are gaining interest. As 3D printing technology is becoming more common, Design for Additive Manufacturing is also in the spotlight. However, the productivity of 3D printing technology is still insufficient in terms of mass production. In this study, the possibility of innovation in mass production process that combines 3D printing technology is presented through the case of innovation in manufacturing productivity of medium-speed engine cylinder head through the integration of sand 3D printing technology. It outlines how sand 3D printing technology is applied to cylinder head mass production processes, how the quality of cylinder head products can be improved compared to conventional pattern-based molding methods, and how productivity can be maximized by reducing process time and human error through hybrid production method with sand 3D printed integrated design cores. In conclusion, this paper presents the effectiveness of sand 3D printing technology which can secure product competitiveness by increasing the production capacity of mass production process, reducing production costs, improving quality, and reducing loss.

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Digital Transformation of Metal Casting Process Using Sand 3D Printing Technology with a Novel Methodology of Casting Design Inside a Core
Kuk-Hyun Han, Jin-Wook Baek, Tae Wan Lim, Ju Min Park
International Journal of Metalcasting.2023; 17(4): 2674. CrossRef

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Development of Intelligence Data Analytics System for Quality Enhancement of Die-Casting Process: Jun Kim, Hyoung Seok Kang, Ju Yeon Lee; J. Korean Soc. Precis. Eng. 2020;37(4):247-254.
Published online April 1, 2020; DOI: https://doi.org/10.7736/JKSPE.019.136

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The goal of this research is to develop intelligence data analytics system for quality enhancement of die-casting process. Targeting a die-casting factory in Korea, we first constructed an edge device-based infrastructure with wireless communication environment for data collection and a processing infrastructure to support the intelligence data analytics system. Using the real quality regarding data of the target factory, we developed two data analytics models for defect prediction and defect cause diagnosis using AdaBoostC2 algorithm. Accuracy of the developed data analytics model for defect prediction was verified as 86%. To use the developed data analytics model efficiently and produce a sequential process of data analytics model generation, execution, and update were conducted automatically. The edge device and integrated server-based dualized analysis system was proposed. The developed intelligence data analytics system was applied to the target factory, and the effectiveness was demonstrated.

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Development of AI-based Bearing Machining Process Defect Monitoring System
Dae-Youn Kim, Dongwoo Go, Seunghoon Lee
Journal of Society of Korea Industrial and Systems Engineering.2025; 48(3): 112. CrossRef
Development of a Quality Prediction Algorithm for an Injection Molding Process Considering Cavity Sensor and Vibration Data
Jun Kim, Ju Yeon Lee
International Journal of Precision Engineering and Manufacturing.2023; 24(6): 901. CrossRef
Data-analytics-based factory operation strategies for die-casting quality enhancement
Jun Kim, Ju Yeon Lee
The International Journal of Advanced Manufacturing Technology.2022; 119(5-6): 3865. CrossRef
Development of a cost analysis-based defect-prediction system with a type error-weighted deep neural network algorithm
Jun Kim, Ju Yeon Lee
Journal of Computational Design and Engineering.2022; 9(2): 380. CrossRef
Development of Prognostics and Health Management System for Rotating Machine and Application to Rotary Table
Mingyu Kang, Chibum Lee
Journal of the Korean Society for Precision Engineering.2022; 39(5): 337. CrossRef
Server-Edge dualized closed-loop data analytics system for cyber-physical system application
Jun Kim, Ju Yeon Lee
Robotics and Computer-Integrated Manufacturing.2021; 67: 102040. CrossRef
Die-Casting Defect Prediction and Diagnosis System using Process Condition Data
Ji Soo Kim, Jun Kim, Ju Yeon Lee
Procedia Manufacturing.2020; 51: 359. CrossRef
Development of Fault Diagnosis Models Based on Predicting Energy Consumption of a Machine Tool Spindle
Won Hwa Choi, Jun Kim, Ju Yeon Lee
Procedia Manufacturing.2020; 51: 353. CrossRef

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Development of Prediction Model for Root Industry Production Process Using Artificial Neural Network: Chanbeom Bak, Hungsun Son; J. Korean Soc. Precis. Eng. 2017;34(1):23-27.
Published online January 1, 2017; DOI: https://doi.org/10.7736/KSPE.2017.34.1.23

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This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed - forward back propagation and the Levenberg - Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process.

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A Study on 3D Printing Conditions Prediction Model of Bone Plates Using Machine Learning
Song Yeon Lee, Yong Jeong Huh
Journal of the Korean Society for Precision Engineering.2022; 39(4): 291. CrossRef
Quality prediction for aluminum diecasting process based on shallow neural network and data feature selection technique
Chanbeom Bak, Abhishek Ghosh Roy, Hungsun Son
CIRP Journal of Manufacturing Science and Technology.2021; 33: 327. CrossRef
Response Simulation, Data Cleansing and Restoration of Dynamic and Static Measurements Based on Deep Learning Algorithms
Seok-Jae Heo, Zhang Chunwei, Eunjong Yu
International Journal of Concrete Structures and Materials.2018;[Epub] CrossRef