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 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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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

Abstract
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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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