Journal of the Korean Society for Precision Engineering

Power Consumption Analysis and Optimal Operation Method of Wireless Multi-sensor Module: Hyun Sik Son, Duck-Keun Kim, Kwang Eun Ko, Seung-Hwan Yang; J. Korean Soc. Precis. Eng. 2025;42(10):843-849.
Published online October 1, 2025; DOI: https://doi.org/10.7736/JKSPE.025.023

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Smart farms and smart factories utilize various environmental measurement and task recognition sensors. For situations requiring simultaneous measurements, a multi-sensor module that combines several sensors into one unit is advantageous. This study focuses on integrating various sensors into a single module and proposing an optimal usage method to minimize the power consumption of a wireless multi-sensor module capable of remote measurements. Analysis of the power consumption of individual sensor components revealed that when the measurement interval exceeds one minute, power consumption can be reduced by over 50.3% by turning off sensors during idle periods. If real-time responsiveness is not essential, the most efficient approach is to keep the entire module in sleep mode during these idle periods. A practical experiment was conducted using a multi-sensor module equipped with temperature and humidity, illuminance, CO₂ concentration, and soil moisture sensors. When continuously powered, the module operated for 40 hours on a 3500 mAh Li-ion battery. However, by implementing sleep mode with a five-minute measurement interval, the operational duration extended to 562 hours.

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Development of Prognostics and Health Management System for Rotating Machine and Application to Rotary Table: Mingyu Kang, Chibum Lee; J. Korean Soc. Precis. Eng. 2022;39(5):337-343.
Published online May 1, 2022; DOI: https://doi.org/10.7736/JKSPE.022.021

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Recently, interest in Prognostics and Health management (PHM) has been increasing as an advanced technology of maintenance. PHM technology is a technology that allows equipment to check its condition and predict failures in advance. To realize PHM technology, it is important to implement artificial intelligence technology that diagnoses failures based on data. Vibration data is often used to diagnose the state of the rotating machine. Additionally, there have been many efforts to convert vibration data into 2D images to apply a convolutional neural network (CNN), which is emerging as a powerful algorithm in the image processing field, to vibration data. In this study, a series of PHM processes for acquiring data from a rotary machine and using it to check the condition of the machine were applied to the rotary table. Additionally, a study was conducted to introduce and compare two methodologies for converting vibration data into 2D images. Finally, a GUI program to implement the PHM process was developed.

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A Development of Data Interface Middleware for Building Smart Factory: Hong Jin Jeong, Ki Hyeong Song, Bo Hyun Kim; J. Korean Soc. Precis. Eng. 2021;38(12):935-942.
Published online December 1, 2021; DOI: https://doi.org/10.7736/JKSPE.021.058

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SMEs encounter many difficulties in integrating and operating various information systems introduced to build smart factories. The source of this difficulty comes from integrated management of data interface between information systems. This research proposes a data interface middleware that can operate and manage various data interfaces between information systems in an integrated manner. First, this study identifies the types of information systems and operational information needed to build smart factories and analyzes the ways of data interface and requirements suitable for the manufacturing environment of SMEs. Structure and detailed functions of the data interface middleware are designed based on the analysis results. The proposed data interface middleware consists of the function layer, engine layer, and DB layer. The function layer is a set of functions for operating the middleware, and the engine layer comprises core engines for executing the functions. The DB layer manages all information that gathers when the data interface is executed. We applied the proposed middleware to connect data between the existing ERP and newly introduced smart factory package software in SMEs. Application results show that the associated data types are consistent in the two systems, and accuracy of the data parsing process is reliable.

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A Review of Intelligent Machining Process in CNC Machine Tool Systems
Joo Sung Yoon, Il-ha Park, Dong Yoon Lee
International Journal of Precision Engineering and Manufacturing.2025; 26(9): 2243. CrossRef
Improvement of Manufacturing Industry Work Environment Using Signage: Root Industry
Kyungjin Oh, Nayoung Lee, Daekwon Chung, Jinho Woo, Haeyeon Shin, Hunseop Kim, Ho Seong Lee, San Kim, SangJun Moon, Won-Shik Chu
Academic Society for Appropriate Technology.2022; 8(3): 117. 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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A Study on the Development of Smart Factory Equipment Engineering System and Effects: Hyun Sik Sim; J. Korean Soc. Precis. Eng. 2019;36(2):191-197.
Published online February 1, 2019; DOI: https://doi.org/10.7736/KSPE.2019.36.2.191

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The Smart Factory Equipment Engineering System collects and monitors necessary information in real-time. While putting the product into the equipment, operation conditions are lowered through a Recipe Management System. The working conditions are set by Run-to-Run a system for real-time detection and control through Fault Detection Classification function. In this study, the smart factory equipment system associated with the entire system is proposed by defining and integrating the necessary equipment management functions from a smart factory’s point of view. To do this, detailed analysis and process improvement on products, processes, and production line equipment were conducted and implemented in the smart factory equipment engineering system. The models proposed in this paper have been implemented to the production site of BGA-PCB. It has been confirmed that the models have resulted in significant change, and have qualitative and quantitative impacts on the working methods of equipment. Typically, data collection time, data entry time, and manual writing sheets were greatly reduced.

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Analysis of Field Conditions and Requirements for Deploying Smart Factory: Hyunjeong Lee, Yong Jin Kim, Jeongil Yim, Yong-Woon Kim, Soo-Hyung Lee; J. Korean Soc. Precis. Eng. 2017;34(1):29-34.
Published online January 1, 2017; DOI: https://doi.org/10.7736/KSPE.2017.34.1.29

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The operating environments of factories and manufacturing units have changed dramatically due to globalization, population, and customization. The existing factories are converted into smart units using information and communications technology (ICT). These smart factories can produce, control, repair, and manage themselves. The manufacturing processes are efficiently optimized using the monitoring and analysis methods of ICT. In this experimental study, we carried out a survey on the system solution providers and consumer companies to determine the field conditions and requirements necessary for assembling a smart factory. Using the results of this survey, we effectively devised smart factory solutions and implemented them on the existing conditions in various factories.

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Development of a Find-On System for Material Warehouse Management Suitable for Use in Small and Medium-Sized Businesses
kim songmin, jung seng-hwa, jae jin Song
The Journal of Korean Institute of Information Technology.2025; 23(5): 63. CrossRef
A Study on the Level Diagnosis Model of Korean Smart Factory for Small-Medium Manufacturing Company : Focusing on the Firm of Plastic Injection parts
Man-Seok Lee
The Journal of Korean Institute of Information Technology.2024; 22(2): 165. CrossRef
Improvement of Manufacturing Industry Work Environment Using Signage: Root Industry
Kyungjin Oh, Nayoung Lee, Daekwon Chung, Jinho Woo, Haeyeon Shin, Hunseop Kim, Ho Seong Lee, San Kim, SangJun Moon, Won-Shik Chu
Academic Society for Appropriate Technology.2022; 8(3): 117. 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