• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.42-49
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• 2009

This study proposes a web-based remote monitoring system for evaluating degradation of machine tools using ART2(Adaptive Resonance Theory 2) neural network. A number of studies on the monitoring of machine tools using neural networks have been reported. However, when normal condition is changed due to factors such as maintenance, tool change etc., or a new failure signal is generated, such algorithms need to be entirely retrained in order to accommodate the new signals. To cope with such problems, this study develops a remote monitoring system using ART2 in which new signals when required are simply added to the classes previously trained. This system can monitor degradation as well as failure of machine tools. To show the effectiveness of the proposed approach, the system is experimentally applied to monitoring a simulator similar to the main spindle of a machine tool, and the results show that the proposed system can be extended to monitoring of real industrial machine tools and equipment.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.3
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• pp.1-7
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• 2016

The cutting processes used for monitoring engineering includes analysis and feedback about strange conditions, tools collision and tools wear in real time, for improving the working ratio of equipment and productivity. In this study, we proposed monitoring using profibus to increase the reliability as the most important factor for cutting monitoring. The profibus can increase the reliability of cutting monitoring for cutting torque of a main spindle motor and a feed motors through PLC-based interface.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.71-75
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• 1993

The application of artificial neural network forcondition monitoring and diagnosis of milling tools was introduced. To detect the conditions of milling tools, the monitoring wywtem consists of three phases: "preparation phase", "learning phase", "production phase". The conditions of milling tools were categorized into the three states. "normal", "warning", "abnormal". The dectection of tool condition, in this paper, could be successfully performed by monitoring the variation of power spectrum on Y dirctional cutting force.

• Educational Technology International
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• v.20 no.2
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• pp.223-255
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• 2019

The purpose of this study is to draw implications for designing online tools to support monitoring in collaborative learning. For this purpose, eighteen research papers that explored learner dashboards and group awareness tools were analyzed. The driving questions for this analysis related to the information and outcomes that must be monitored, whose performance they represent, and who monitors the extent of learning. The analytical frameworks used for this study included the following: three modes of co-regulation in terms of who regulates whose learning (self-regulation in collaborative learning, other regulation, and socially shared regulation) and four categories of dashboard information to determine which information is monitored (information about preparation, participation, interaction, and achievements). As a result, five design implications for learner dashboards that support monitoring were posited: a) Monitoring tools for collaborative learning should support multiple targets: the individual learner, peers, and the entire group; b) When supporting personal monitoring, information about the individual and peers should be displayed simultaneously to allow direct comparison; c) Information on collaborative learning achievements should be provided in terms of the content of knowledge acquired rather than test scores; d) In addition to information related to interaction between learners, the interaction between learners and learning materials can also be provided; and e) Presentation of the same information to individuals or groups should be variable.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.181-182
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• 2007

• Journal of the Korean Society for Precision Engineering
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• v.30 no.5
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• pp.480-485
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• 2013

Enhancing energy efficiency of machine tools causes substantial impacts on the manufacturing industries, to cope with the competitive introduction of the total energy management strategies. Real-time energy monitoring is essential to identify energy consumption patterns of the machine tools and correlate them with the energy management strategy. Integrated analysis of machine tool's operation status and the corresponding energy usage is most important to accurately evaluate the energy efficiency under the various machining process environments. This paper proposes a system architecture to realize the online energy monitoring system and the embedded energy monitoring approach interconnected with the CNC kernel. The shop-floor operation management system is presented to integrate the proposed online energy monitoring approach.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.4
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• pp.134-140
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• 2016

Main Spindle System has effect on performance of machine tools and working quality as well as is required of high reliability. Especially, it takes great importance in producing automobiles which includes a large number of working processes. However, main spindle unit in Machine tools are often cases where damage occurs do not meet the design life due to driving in harsh environments. This is when excessive maintenance and repair of machine tools or for damage stability has resulted in huge economic losses. Therefore, this studying propose a method of accelerated life test for diagnosing and prognosis the state of life assessment main spindle system. Time status monitoring of diagnostic data - through the analysis of the frequency band signals were carried out inside the main spindle bearing condition monitoring and fault diagnosis.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.613-618
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• 2006

As the embedded software spreads in various areas, many development tools have been made to minimize the developing time. But these tools cannot be applicable to all the environment because they have been created for the specific platform. As such, this paper proposes an Embedded Monitoring System, which supports the various communication environment and removes the limitation of adaptability to the various platforms. Using the Code Inline technique, this system can perform the monitoring process. However, we should consider the optimization for the monitoring process and monitoring sensors because the technique has the monitoring sensor overhead. As such, this paper proposes an approach for initializing the monitoring process and a bit-masking technique for optimizing the monitoring sensor. The Embedded Monitoring System will be applicable to all the areas using embedded systems.

• Publications of The Korean Astronomical Society
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• v.15 no.spc2
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• pp.27-35
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• 2000

The investigation of the space environment requires the use of experimental and theoretical tools and resources in order to perform the research task. Understanding of these research tools is imperative for proper interpretation of the results. In this paper, we discuss on research tools that are widely used in the field of aeronomy; Fabry-Perot interferometer and Michelson interferometer. These instruments have been used extensively as passive optical devices, spectrally monitoring the natural atmospheric emissions (airglow). This function has made both instruments valuable tools in upper atmospheric studies since they provide the ability to determine the dynamic and thermodynamic properties of the upper atmosphere by monitoring naturally-occuring emission.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.161-167
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• 2020

In the process of cutting large aircraft parts, the tool may be abnormally worn or damaged due to various factors such as mechanical vibration, disturbances such as chips, and physical properties of the workpiece, which may result in deterioration of the surface quality of the workpiece. Because workpieces used for large aircrafts parts are expensive and require strict processing quality, a maintenance plan is required to minimize the deterioration of the workpiece quality that can be caused by unexpected abnormalities of the tool and take maintenance measures at an earlier stage that does not adversely affect the machining. In this paper, we propose a method to indirectly monitor the tool condition that can affect the machining quality of large aircraft parts through real-time monitoring of the current signal applied to the spindle motor during machining by comparing whether the monitored current shows an abnormal pattern during actual machining by using this as a reference pattern. First, 30 types of tools are used for machining large aircraft parts, and three tools with relatively frequent breakages among these tools were selected as monitoring targets by reflecting the opinions of processing experts in the field. Second, when creating the CNC machining program, the M code, which is a CNC auxiliary function, is inserted at the starting and ending positions of the tool to be monitored using the editing tool, so that monitoring start and end times can be notified. Third, the monitoring program was run with the M code signal notified from the CNC controller by using the DAQ (Data Acquisition) device, and the machine learning algorithms for detecting abnormality of the current signal received in real time could be used to determine whether there was an abnormality. Fourth, through the implementation of the prototype system, the feasibility of the method proposed in this paper was shown and verified through an actual example.