• Journal of the Korean Society of Costume
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• v.59 no.2
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• pp.70-86
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• 2009

Today, digital technology is extending its influence to fashion design, which is closely related to people's lifestyle. In order for people to access information all the time, every place, people have to wear these devices all the time, every place. Intelligent wear allows people to communicate with their own body, other persons or surrounding real-time. The purposes of this study are to define 'Intelligent wear' by looking through the similar terms of intelligent wear on related fields, to analyze characteristics of intelligent wear and to provide not only theoretical data but also, practical data far product development on both functional and aesthetic sides. In this study, usefulness provided by intelligent wear were identified in the concept of instrumental & expressive function. For the aim of the study, literature and case study were considered at the same time. The conclusions are as the following. The characteristics of intelligent materials were found to be information, intelligence and protection, the characteristics of intelligent manufacture were combination and virtual reality. And The characteristics of intelligent products were multi-function, transformation, camouflage. Integration of operability function extends human ability and the area of human activity, entertainment, and communication, and provides convenience. Consequently, development of intelligent wear should promote through not only computer engineering but also, connection to other fields. Most of all, there is a need for active research in clothing design and the fashion design since intelligent wear is after all, clothing.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.455-456
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• 2002

Wear is one of the main factors causing breakdown and fault of machine, so ferrography technique analyzing wear particles can be an effective way for condition monitoring and fault diagnosis. On the base of the forward multilayer neural network, a nodes self-deleting neural network model is provided in this paper. This network can itself deletes the nodes to optimize its construction. On the basis of the nodes self-deleting neural network, an intelligent ferrography diagnosis expert system (IFDES) for wear particles recognition and wear diagnosis is described. This intelligent expert system can automatically slim lip knowledge by learning from samples and realize basically the entirely automatic processing from wear particles recognition to wear diagnosis.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.532-537
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• 2024

In this study, wear characteristics and microstructure changes due to changes in alloy composition of Ni-Cr-Mo-V and Ni-Cr-Mo low-alloy steels used in brake discs for transportation system such as aircraft and high-speed trains. As a result of the hardness test, the hardness of C-Mo-V steel was the highest at 39.4±0.9HRc, and the hardness of Ni-Cr-Mo steel was the lowest at 32.4±0.6HRc. The friction coefficient tended to decrease as the vertical load increased. At a vertical load of 1 N, the friction coefficient of Ni-Cr-Mo steel was the highest at 0.842, and at a vertical load of 5 N, Mn-Cr-V steel was the highest at 0.696. Ni-Cr-Mo showed the largest wear scar width, depth, and wear amount, with a width of 711 ㎛, a depth of 8.24 ㎛, and a wear amount of 11 mg under a vertical load of 1 N, and a width of 1,017 ㎛, a depth of 19.17 ㎛, and a wear amount of 17 mg under a vertical load of 5 N. As a result of wear mechanism analysis, ploughing, delamination, and adhesion in all specimens, with plastic deformation being more prominently observed in Ni-Cr-Mo.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.74-77
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• 2001

One of the most important technology in PA(Factory Automation) is to construct the diagnostic system for manufacturing process. To improve the productibility in the factory, the state of tools such as bite, drill, endmill should be monitored continuously. In this study, fuzzy logic was used to check the wear of drill in drilling process. The input variables to construct the fuzzy rules are cutting force and the rate of cutting force's change. The experiment was done with the fixed spindle speed and feed rate in cutting condition.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.163-171
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• 2020

The condition of tire tread is a key parameter closely related to the driving safety of a vehicle, which affects the contact force of the tire for braking, accelerating and cornering. The major factor influencing the contact force is tread wear, and the more tire tread wears out, the higher risk of losing control of a vehicle exits. The tire tread condition is generally checked by visual inspection that can be easily forgotten. In this paper, we propose the intelligent tire (iTire) system that consists of an acceleration sensor, a wireless signal transmission unit and a tread classifier. In addition, we also presents classification algorithm that transforms the acceleration signal into the frequency domain and extracts the features of several frequency bands as inputs to an artificial neural network. The artificial neural network for classifying tire wear was designed with an Multiple Layer Perceptron (MLP) model. Experiments showed that tread wear classification accuracy was over 80%.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.833-836
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• 2001

One of the most important technology in Factory Automation and Unmanned Automation is to construct the diagnostic system for manufacturing process. To improve the productivity in cutting process, the state of tools such as bite, drill, endmill should be monitored continuously. In this study, fuzzy logic was used to check the wear of drill in drilling process. The input variables to construct the fuzzy rules are cutting force and the rate of cutting force's change. The experiment was done with the fixed spindle speed and feed rate in cutting condition. The proposed algorithm is verified by comparing Fuzzy wear with real wear measured.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.61-66
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• 1994

Tool condition monitoring is one of the most important aspects to improve productivity and quality and to achieve intelligent machining system. The tool state is classified into three groups as chipping, wear and fracture. In this study, wear of a ceramic cutting tool for hardened die material (SKD11) was investigated. Flank wear was occured more dominant than crarer wear. Therefore, to predict flank wear, the modeling of cutting force has been performed. The modeling of cutting force by an assumption that act the stress distribution on the tool face obtained through a numerical analysis. The relationships between the cutting force and the tool wear can be constructed by machining paraneters with cutting conditions. Experiments were performed under the various cutting conditions to ensure the validity of force models. The theoretical predictions of the flank wear is approximately in good agreement with experimental result.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.4
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• pp.42-49
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• 1998

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surface from which the particles originated. The morphological identification of wear debris can therefore provide very early detection of a fault and can also often facilitate a diagnosis. The purpose of this study is to attempt the developement of intelligent system for moving condition diagnosis of the machine driving system. The four shape parameter(50% volumetric diameter, aspect, roundness and reflectivity) of war debris are used as inputs to the neural network and learned the moving condition of five values(material3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the moving condition and materials very well by neural network.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.27-27
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• 2004