• Journal of the Korean Society of Industry Convergence
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• v.23 no.3
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• pp.403-410
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• 2020

Continuous industrial development has led to a better quality of life for everyone, even further accelerating industrial growth. Industrial development, however, has also caused environmental degradation, which is posing a serious threat to humanity. It has also encouraged the indiscriminate use of limited resources, causing resource depletion. Efficient resource management based on resource circulation is critical to saving resources. Resource circulation methods are as follows: reducing the use of resources in the manufacturing process, recycling used or reprocessed products and reusing used resources without being reprocessed, remanufacturing with end-of-life products with disassembled parts. Furthermore, remanufacturing process including cleaning, inspection, repairing, and reassembling facilitate performance level as well as new typical products. It is noteworthy that the remanufacturing of machine tools can significantly save resources because their structural parts are substantially large in size. Machine tools have served as a foundation for the manufacturing industry, which has driven Korea's industrial development. Nevertheless, a few research has been reported for remanufacturing technology with used machine tools. Relevant research of developing a remanufacturing process chart and method is prerequisite for saving the resource and environments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.152-161
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• 2011

The operation of vending machine system presents a decision-making problem which consists of determining the product allocation to vending-machine storage compartments, replenishment intervals of vending machines, and vehicle routes, all of which have critical effects on system profit. Especially, it becomes more difficult to determine the operation variables optimally when demand for a product that is out-of-stock spills over to another product or is lost. In this paper, we propose a heuristic for solving the operation problem of the vending machine system and evaluate it by comparing with Yang's algorithm on various test problems with respect to system profit via a computer simulation. The results of computational experiments show a substantial profit increase of the proposed heuristic over Yang's algorithm. Sensitivity analysis indicates that some input variables impact the profit increase significantly.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.904-908
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• 2008

As the various industrial production machinery has come into being by development of industrial technology, the productivity of the basic industrial production machinery has improved and the international competitiveness of the one of Korea has strengthened. However, at the same time, noise from various industrial production machinery disturbs the quiet environment. There are 35 kinds of the noise emission machinery defined in the noise and vibration control act according to the horse power and the number of machinery. These were classified in 1992 through investigation from 1990 to 1991, and the characteristic of the noise emission machinery may be different from the past one. So we need to investigate the characteristics of the noise emitted by machinery to control it rightly. Also we need to investigate the new noise emission machinery which has come into being recently. In this survey, we measured sound intensity of 32 noise emission machinery to calculate the sound power levels of those and investigated the characteristic of the sound power level of those according to the frequency. From the survey, we found that the forging machine, concrete pipe and pile making machine, sawing machine, etc. are the noisy machinery. And the automatic packing machine, sewing machine, centrifuge, etc. are the silent machinery. Also the generator, the concrete pipe and pile making machine, the printing machine, etc. emit the low frequency noise, and the molding machine, the stone cutter, the metal cutter, etc. emit the high frequency noise. Lastly, we intented to propose the proper guide line of classifying noise emission machinery.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.9 no.13
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• pp.1-11
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• 1986

The mechanization of production facilities being made rapid progress, and its function being diversified and complicated for industrialized period, the relation between machine and Its operator brings about many problems which are concerned with accident. In these circumstance, the purpose of industrial safety can not be properly achieved as considered by only one side of machine or man. Therefore, it is necessary to study how to cope with the safety of man-machine system. It has to be considered in the above mentioned contents that safety management can not be attained through only technique of numerical control. The cause of accident being studied scientifically, the service of safety problems has to be systematized and operated in rational safety organization. The purpose of this thesis is to consider preventing and decreasing industrial accident from production system field by means of the improvement of worker's own safety consciousness and introducing the function of safety management to the duties of labour union.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.100-107
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• 2015

Recently as the manufacturers want competitiveness in dynamically changing environment, they are trying a lot of efforts to be efficient with their production systems, which may be achieved by diminishing unplanned operation stops. The operation stops and maintenance cost are known to be significantly decreased by adopting proper maintenance strategy. Therefore, the manufacturers were more getting interested in scheduling of exact maintenance scheduling to keep smooth operation and prevent unexpected stops. In this paper, we proposedan integrated maintenance approach in injection molding manufacturing line. It consists of predictive and preventive maintenance approach. The predictive maintenance uses the statistical process control technique with the real-time data and the preventive maintenance is based on the checking period of machine components or equipment. For the predictive maintenance approach, firstly, we identified components or equipment that are required maintenance, and then machine parameters that are related with the identified components or equipment. Second, we performed regression analysis to select the machine parameters that affect the quality of the manufactured products and are significant to the quality of the products. By this analysis, we can exclude the insignificant parameters from monitoring parameters and focus on the significant parameters. Third, we developed the statistical prediction models for the selected machine parameters. Current models include regression, exponential smoothing and so on. We used these models to decide abnormal patternand to schedule maintenance. Finally, for other components or equipment which is not covered by predictive approach, we adoptedpreventive maintenance approach. To show feasibility we developed an integrated maintenance support system in LabView Watchdog Agent and SQL Server environment and validated our proposed methodology with experimental data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.2 no.1
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• pp.13-17
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• 2001

The configuration design of kinematic mechanisms of industrial sewing machine has been studied using a functional approach. The configuration design methodology has been applied to shorten the development cycle time of mechanisms and to manage design data efficiently Expert system has been used to embody the decomposition of functional requirements. It has been interfaced with a CAD system through the API program to show the assembly and parts of the mechanism. Constraints also can be handled by the expert system through the rule induction and the case based reasoning process. The configuration design system includes the kinematical analysis and optimization of the mechanisms of an industrial sewing machine by the interface between the expert system and an analysis program by means of API Program supplied by expert system. The conceptual design of sewing machine mechanism can be Performed rapidly and efficiently.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.741-749
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• 2014

This paper proposes a machine vision module for PLCs (Programmable Logic Controllers). PLC is the industrial controller most widely used in factory automation system. However most of the machine vision systems are based on PC (Personal Computer). The machine vision system embedded in PLC is required to reduce the cost and improve the convenience of implementation. In this paper, we newly propose a machine vision module based on PLC. The image processing libraries are implemented and integrated with the PLC programming tool. In order to interface the libraries with ladder programming, the ladder instruction set was also designed for each vision library. By use of the developed system, PLC users can implement vision systems easily by ladder programming. The developed system was applied to sample inspection system to verify the performance. The experimental results show that the proposed system can reduce the cost of installing as well as increase the ease-of-implementation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.123-131
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• 2020

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

• Journal of Biosystems Engineering
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• v.36 no.6
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• pp.453-460
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• 2011

This study was carried out to test a red pepper tedding factors which is needed for design and development of an auto-tedding machine and a performance. According to this test, the results can be summarized as follows: The results of the tedding factors test according to shape of rotary blade, which is the 0.4 to 0.5 mm brush type, was found to be the most appropriate. As a result of tedding ratio which includes brush diameters, driving velocities and rotation velocities, there was generally no significance difference, but the red pepper tedding efficiency was found to be 39.7%. The moisture drying rate of red pepper was found to be 0.9 %w.b./h in prototype auto-tedding machine and 0.4 %w.b./h in traditional practice. The drying time was found to be 3 days in prototype auto-tedding machine and the traditional practice was 6 days. The average variable coefficient of the red pepper moisture content was found to be 16.8% in prototype auto-tedding machine in comparing with the traditional practice of 35.0%. This test showed a difference around two times, and this difference was evaluated to be the reason for irregular drying and different drying times.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.