• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.375-378
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• 1995

Although laser interferometer measurement system has the advantage of range and accuracy, the traditional error measurement methods for geometric errors(two straightness and three angular errors) of a machine tool measures error components one at a time. It may also create an optical path difference and affect the measurement accuracy. In order to identify and compensate for geometric error of a moving body, an on-line measurement system for simultaneous detection of the five error components of a moving axis is required. An on-line measurement system with 5 degrees of freedom was developed for geometric error detection. Performance verification of the system was performed on an error generating mechanism. Experimental results show the feasibility of this system for identifying geometric errors of a side of machine tool.

• The KIPS Transactions:PartA
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• v.18A no.2
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• pp.61-68
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• 2011

Memory error debugging is one of the most critical processes in improving software quality. However, due to the extensive time consumed to debug, the enhancement often leads to a huge bottle neck in the development process of mobile devices. Most of the existing memory error detection tools are based on static error detection; however, the tools cannot be used in mobile devices due to their use of large working memory. Therefore, it is challenging for mobile device vendors to deliver high quality mobile devices to the market in time. In this paper, we introduce "PinMemcheck", a pin-based memory error detection tool, which detects all potential memory errors within $1.5{\times}$ execution time overhead compared with that of a baseline configuration by applying the Pin's binary instrumentation process and a simple data structure.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.67-77
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• 1995

In modern industry the requirement of automation of manufacturing process increases so that unmanned system has been popular as an ultimate goal of modern manufacturing process. In unmanned manufacturing process the tool fracture is a very serious problem because it results in the damage of workpieces and can stop the operation of whole manufa- turing system. In this study, image processing technique is used to detect the fracture of insert tip of face milling using multi-point-tool. In order to acquire the image information of fracture shape of rotation insert tip. We set up the optical system using a light beam chopper. In this system we can reduce the image degradation generated from stopped image of rotating insert tip using image restoration technique. We calculated the mean square error to diagnose the condition of tool fracture, and determind the criteria of tool fracture using experimental and staticstical method. From the results of this study we've developed non- contact detection technique of tool fracture using image processing method and proposed the fracture direction of automation and unmanned system considering the optimal time of tool change milling.

• Journal of Information Technology Services
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• v.7 no.3
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• pp.271-286
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• 2008

The core of the semantic web is ontology, which supports interoperability among semantic web applications and enables developer to reuse and share domain knowledge. It used a variety of fields such as Information Retrieval, E-commerce, Software Engineering, Artificial Intelligence and Bio-informatics. However, the reality is that various errors might be included in conceptual hierarchy when developing ontologies. Therefore, methodologies and supporting tools are essential to help the developer construct suitable ontologies for the given purposes and to detect and analyze errors in order to verify the inconsistency in the ontologies. In this paper we propose a new approach for ontology error detection based on the Concept Lattices of Formal Concept Analysis. By using the tool that we developed in this research, we can extract core elements from the source code of Ontology and then detect some structural errors based on the concept lattices. The results of this research can be helpful for ontology engineers to support error detection and construction of "well-defined" and "good" ontologies.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.105-112
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• 1999

In this paper we deal with tool interference problem in the case of compound surface machining. A new tool interference detection and correction method based on the envelope of the tool path is suggested to identify and correct the tool interference - not only within the local path of tool movement, but also outside of the tool path. Therefore, the developed strategy can be used to check the possible interference in any region of the surface. In order to analyze quantitatively the milled surface error produced by the tool interference, improved surface prediction model is also suggested in cutting process by general cutters. The effectiveness of the proposed method is demonstrated through simulation study.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.291-294
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• 2000

Free-form surfaces arise in shipbuilding, automotive and aerospace industries. Specially compound free-form surfaces so do. Machining complicated products consist of compound surface, it is very important to avold and remove tool interferences. By the way, in compound surfaces the tool interference can occur not only in the tool path direction but also in the other direction. A new tool interference detection and correction using tool interference conditions is suggested to identify and correct the tool interference in compound surfaces.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.245-250
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• 2002

A successful on-line monitoring system for conventional machining operations has the potential to reduce cost, guarantee consistency of product quality, improve productivity and provide a safer environment for the operator. In fee-shape machining, typical signs of tool problems such as vibration, noise, chip flow characteristics and visual signs are almost unnoticeable without the use of special equipment. These characteristics increase the importance of automatic monitoring in fine-shape machining; however, sensing and interpretation of signals are more complex. In addition, the shafts of the micro-tools break before the typical extensive cutting edge of the tool gets damaged. In this study, the existence of a relationship between the characteristics of the cutting force and tool usage was investigated, and tool breakage detection algorithm was developed and the fellowing results are obtained. In data analysis, didn't use a relative error compare which mainly used in established experiment and investigated tool breakage detection algorithm in time domain which can detect AE and cutting force signals more effective and accurate.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.3
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• pp.24-31
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• 2004

A successful on-line monitoring system for conventional machining operations has the potential to reduce cost, guarantee consistency of product quality, improve productivity and provide a safer environment for the operator. In fine-shape machining, typical signs of tool problems such as vibration, noise, chip flow characteristics and visual signs are almost unnoticeable without the use of special equipment. These characteristics increase the importance of automatic monitoring in fine-shape machining, however, sensing and interpretation of signals ar more complex. In addition, the shafts of the mini-tools break before the typical extensive cutting edge of the tool gets damaged. In this study, the existence of a relationship between the characteristics of the cutting force and tool usage was investigated, and tool breakage detection algorithm by LabVIEW was developed and the following results are obtained. It was possible to use a relative error compare which mainly used in established experiment and investigated tool breakage detection algorithm in time domain which can detect AE and cutting force signals more effective and accurate.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.47-51
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• 1992

Automatic detection of tool breakage during NC machining is a key issue not only for improving productivity but to implement the unattended manufacturing system. In this paper, we develop a vibration sensor-based tool breakage detection system for NC milling processes. The system obtains the time-domain vibration signal from the sensor attached on the spindle bracket of our CNC machine and declares tool failures through the on-line monitoring schemes. For on-line detection, our approach is to use the PSC(statistical process control) methods being increasingly used for on-line process control. The main thrust of this paper is to propose and compare the performance of SPC methods including : a) X-bar control scheme, b) S control scheme, c)EWMA (exponentially weighted moving average) scheme, and d) AEWMA (adaptive exponentially weighted moving average) scheme. The performance of the control schemes are compared in terms of the type 1 and 2 error calculated from the experiment data.