• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.11 no.3
• /
• pp.39-44
• /
• 2002

As laser cutting process is widely used in industry, an automated on-line process control system has become very important. In this paper, development of a laser cutting monitoring system, which is regarded as the fundamental step toward automation of the process, is presented. Acoustic emission and an artificial neural network were used for the monitoring system. With given process Parameters including laser power and scanning speed the system can predict the suitability of laser beam for the cutting or a stainless steel (STS304) plate.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.200-205
• /
• 1996

Recently, in order to achieve high flexibility of manufacture, monitoring and control strategies cf a new type have been developed. Since the generation of built-up edge on the cutting tool damages the surface finish of the workpiece, the monitoring system of built-up edge is an important process monitoring. In this study, the analyzing methods of cutting force signal to detect the built-up edge during cutting process are described. The cutting force signals are analyzed using the mean, standard deviation and mean to standard deviation of this cutting signals. We can obtain the guide to detect the built-up edge during turning process.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2014.10a
• /
• pp.739-742
• /
• 2014

In the process of MLCC manufacturing, MLCC stacking process is the key process of making high quality MLCC. Since MLCC is small components, the entire process of MLCC stacking process is minute and sensitive to micro errors. To prevent micro error, we suggest condition-based monitoring which quantifies error based on feature extraction and quantifying error method. As results, it has been shown that the suggested algorithm has effectiveness of condition based monitoring of MLCC stacker.

• Journal of Welding and Joining
• /
• v.28 no.4
• /
• pp.81-86
• /
• 2010

In resistance spot welding, regardless of the optimal condition, bad weld quality was still produced due to complicated manufacturing processes such as electrode wear, misalignment between the electrode and workpiece, poor part fit-up, and etc.. Therefore, the goal of this study was to measure the process signal which contains weld quality information, and to develop the process fault monitoring system. Welding force signal obtained through variety experimental conditions was analyzed and divided into three categories: good, shunt, and poor fit-up group. And then a monitoring algorithm made up of an artificial neural network that could estimate the process fault of each different category based on pattern was developed.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.7
• /
• pp.67-72
• /
• 1997

Recently, in order to achieve high flexibility of manufacture, monitoring and control strategies of a new type have been developed. Since the generation of built-up edge on the cutting tool damages the surface finish of the workpiece, the monitoring system of built-up edge is an important process monitoring. In this study, the analyzing methods of cutting force signal to detect the built-up edge during cutting process are described. The cutting force signals are analyzed using the mean, standard deviation and mean to standard deviation of this cutting signals. We can obtain the guide to detect the built-up edge during turning process.

• Journal of the Korea Safety Management & Science
• /
• v.2 no.4
• /
• pp.19-32
• /
• 2000

Assignable causes producing temporary deviation from the underlying system can influence on process adjustment and process monitoring in dynamic feedback control system. In this paper, the impact of assignable causes on EWMA forecasts and process adjustment which is based on the EWMA forecasts are derived for optimum control methods.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.11-21
• /
• 1992

Modern manufacturing process requires machine intelligence to meet the demands for high technology products as well as intelligence-based operating skills to lessen human worker's intervene. To meet this trend there has been wide spread interest in applying artificial neural network(ANN) to the areas of manufacturing process monitoring and control. This paper addresses application problems in such processes as welding, assembly, hydroforming process and inspection of solder joints.

• Journal of the International Relations & Interdisciplinary Education
• /
• v.4 no.2
• /
• pp.21-41
• /
• 2024

The The purpose of this study is to find the legal regulations of the National Education process monitoring team and challenges.The research method was used for literature research, and method of counseling, and methods.literature study used for national education process monitoring terminals related laws and status analysis, and legal interpretation was used for national education process monitoring related to the system and logic organize the system and logic of national education process monitoring.The interview was used directly and indirectly used to reduce errors in the development of law experts from the development of law and educational experts.The main contents of research was examined based on the need of legal nature and system based on the need of legal characteristics and system.Next, it was evaluated on the operation of the national education process monitoring team, and tasks based on these evaluation results. The results of discussion was presented separately divided into the issue of the Enforcement Decree of the Act, task, and the problem of the Enforcement Decree. The problem of the Enforcement Decree of the Act was discussed on the obligation of the purpose of the purpose of the purpose of the establishment, monitoring unit, monitoring unit and role of the compliance organization.The problem and tasks were discussed with negative synthesis of monitoring and monitoring complex definition of the qualification criteria for representativeity, monitoring range of monitoring and monitoring range of monitoring.As a result of these discussion, the reorganization and operation of the organization was requested to establish a special profit group based on expertise, there were room for special profit groups based on expertise.

• Journal of Power System Engineering
• /
• v.4 no.2
• /
• pp.25-30
• /
• 2000

The grinding process is very complex and relates many parameters to control the process. As this reason, a theoretical analysis and a quantitative estimation of the grinding process has not been well established. In this study, the in-process monitoring system was suggested by applying the neural network for monitoring and shooting the malfunction of cylindrical plunge grinding process. This system used the power signals from the electric power meter. This neural network was composed of processing elements [4-(5-5)-3] with 4 identified power parameters. Because sensitivity is blunted some minute vibration components, the simulation result of this system has appeared about 10% erroneous recognition in the uncertain pattern and the average success rate of the trouble recognition was about 90%. Consequently, the developed system, which applied to the power signals, can be recognize enough to monitor the grinding process as in-process.

• Journal of Biosystems Engineering
• /
• v.23 no.4
• /
• pp.373-380
• /
• 1998

The contribution of this paper is to provide the methods for the production automation of potato microtuber using the vision process in growth monitoring. The first method deals with computation for the growth density in the primary growth process. The second method addresses cognition process to identify the number and the volume of potato microtuber in secondary growth process. The third is to decide whether potato microtubers are infected by a virus or bacteria in growth process. The computation for the growth density in the primary growth process uses the method of Labeling. The second and third methods use template matching based on color patterns. With the developed method using vision process, this experiment is capable of discriminating weekly growth-rate in primary growth process, 85% cognition rate in secondary process and identifying whether there are infections. Therefore, we conclude that our experimental results are capable of growth monitoring for mass production of potato microtubers.