• Journal of Practical Engineering Education
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• v.6 no.1
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• pp.37-42
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• 2014

The automated visual inspection system (machine vision system) for quality assurance is important factory automation equipment in the manufacturing industries, such as display, semiconductor, etc. There is a lot of demand for the machine vision engineers. However, there are no technical training courses for machine vision technologies in vocational schools, colleges and universities. In this paper, we present the implementation of line scan camera based equipment for technical training of the automated visual inspection system. The training system consists of the X-Y stage which is widely used in machine vision industries and its variable image resolution are set to $10-30{\mu}m$. Additionally, this training system can attach the industrial illumination, either the direct illuminator or coaxial illuminator, for verifying the effect of illuminations. This means that the trainee can have a practical training in various equipment conditions and the training system is similar to the automated visual inspection system in industries.

• 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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.10
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• pp.537-544
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• 2009

On account of the technological development, intelligent building is on increasing where the artificial regulation on indoor environment is possible, thence the concern about those facilities such as water-supply facility, water-heater and drainage facility has becomes higher. However, due to diversification and complicated system of the facility-equipments, the noise generating from such facility equipment is gradually becoming a problem, and since especially equipment noises arising at the machine room frequently infringe into the resident's pleasant living environment with the complex types of an air-borne sound and a structure-borne sound, it is becoming the civil complaint. On such viewpoint, this Study ever observed the characteristics of noise generating from various facility-equipments in the building, and intended to evaluate the facility-noises by use of the valuation index such as PSIL, N, NC, NR. As result of, the facilities noise which happens in the machine room makes normal conversation very difficult due to high sound pressure level. Based on such data, this study is willing to present it as an essential material for establishment an efficient measure against the facility-noises arising at machine room hereafter.

• Journal of the Korea Management Engineers Society
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• v.23 no.4
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• pp.87-103
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• 2018

This paper introduces the method to develop two main types of the fuzzy OEE (Overall Equipment Effectiveness) models via triangular membership function for measuring uncertainty. The fuzzy OEE includes model type 1 and model type 2. The model type 1 is used when the theoretical machine speed only reflects the time loss whereas model type 2 is used when the actual machine speed reflects both time and speed loss. Model type 2 has shown to perform a lower availability rate and a higher performance rate compared to model type 1. In addition, the fuzzy UPH (Unit Per Hour) which is derived from using the fuzzy OEE is presented to satisfy demand uncertainty. The fuzzy UPH can easily measure the fuzzy tact time and cycle time by reciprocating itself. Finally, this study demonstrates the fuzzy OEE models using IVIFS (Interval-Valued Intuitionistic Fuzzy Set) based on the characterization via membership function, non-membership function and hesitant function. For the purpose of analyzing the fuzzy system OEE, the OEE for each machine of plant structure is considered triangular interval-valued intuitionistic fuzzy number. Regardless of plant structure, the validity degree of fuzzy membership function of system OEE decreases when the number of machine with worst value of the validity degree increases. Corresponding examples are presented in this paper for practitioner to understand the applicability and practicability of the proposed fuzzy OEE methods.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.70-78
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• 2021

Naval surface ships and submarines could be exposed to non-contact underwater explosion(UNDEX) environment. Equipment installed on the ships and submarines could be damaged by shock load generated by UNDEX environment. Therefore, shock survivability of equipment generally evaluated by shock tests. Ground based shock test machine such as Light weight shock test machine(LVSM) is developed to simulate shock load caused by UNDEX environment. In this study, linear dynamic model of LVSM is proposed and evaluated to improve shock test design procedure. Parameters of the model are decided by optimizing time domain response compared to zero payload experiment. Proposed model is verified by comparing simulation results and test results of maximum payload experiment. Finally, shock test design using the model is described for various test equipment weight.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.62-67
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• 2001

The textiles supply rolling equipment is a part of inspection machine which inspect finished textiles and it check up textiles through rolling hydraulic equipment. This study suggests a method to select the capacity and initial gas pressure of accumulator to control reducing speed of the hydraulic motor to a desired degree. An accumulator in hydraulic systems is hydraulic machinery which stores kinetic energy of inertia body during braking. A series of computer simulations were done for the brake action and the selection method was based upon a trial and error approach. The results of the simulation work were compared with those of experiments and these results show that the proposed method can be applied effectively to control reducing speed of the hydraulic motor when braking action in textiles rolling system.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.38 no.12
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• pp.100-107
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• 2009

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.177-177
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• 2018

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.118-121
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• 2003

Human support systems, such as computers and robots, are required to be changed to a machine equipment independently operates and communicate with human, rather than non-sensitivity and obedient machine equipment Therefore, we notice nonverbal language that human recognizes naturally. In addition, we show the validity and constitution of mechanism that recognizes an intention of human using those several information to judge independently.