• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.6
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• pp.590-597
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• 2009

The final quality of the workpiece is affected by the grinding process that has been conducted in final manufacturing stage. However the quality-satisfaction of ground workpiece depends on the skill of an expert in this process. Therefore, the process models of grinding have been developed to predict the states according to grinding process. In this paper, in order to find the optimized grinding condition to reduce the manufacturing expense and to meet requirements of ground workpiece optimization algorithm using E.S.(Evolutionary Strategy) is proposed. The proposed algorithm has been employed to find the optimal grinding and dressing condition using the grinding process models and nonlinear grinding constraints. The optimized results also presents the guide line of grinding process. The effectiveness of the proposed algorithm is verified through the experimental results.

• Journal of the Korea Safety Management & Science
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• v.6 no.3
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• pp.237-247
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• 2004

The condition of the manufacturing process in a factory should be diagnosed and maintained efficiently because any unexpected disorder in the process will be reason to decrease the efficiency of the overall system. However, if an expert experienced in this system leaves, there will be a problem for the efficient process diagnosis and maintenance, because disorder diagnosis within the process is normally dependent on the expert's experience. This paper suggests a process diagnosis using data mining based on the collected data from the coil-spring manufacturing process. The rules are generated for the relations between the attributes of the process and the output class of the product using a decision tree after selecting the effective attributes. Using the generated rules from decision tree, the condition of the current process is diagnosed and the possible maintenance actions are identified to correct any abnormal condition. Then, the appropriate maintenance action is recommended using the decision network.

• Journal of the Semiconductor & Display Technology
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• v.15 no.1
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• pp.17-21
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• 2016

EUV (Extreme Ultraviolet) pellicle which protects a mask from contamination became a critical issue for the application of EUV lithography to high-volume manufacturing. However, researches of EUV pellicle are still delayed due to no typical manufacturing methods for large-scale EUV pellicle. In this study, EUV pellicle membrane manufacturing method using not only KOH (potassium hydroxide) wet etching process but also a water bath was suggested for uniform etchant temperature distribution. KOH wet etching rates according to KOH solution concentration and solution temperature were confirmed and proper etch condition was selected. After KOH wet etching condition was set, $5cm{\times}5cm$ SiNx (silicon nitride) pellicle membrane with 80% EUV transmittance was successfully manufactured. Transmittance results showed the feasibility of wet etching method with water bath as a large-scale EUV pellicle manufacturing method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.3-8
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• 2004

When most manufacturing company produce grinding product operators decide grinding condition by experience and subjective judgment. The study on grinding manufacture have been developed to get the grinding condition with the same result when non-experienced or experienced worker work. The objective of this study is to develope the grinding condition and predict the result of grinding by neural network. Several discussions were made in following areas as; getting MRR with image processing, the architecture optimization of neural network with experiment design, analysis of the input neurons using sensitivity approach. The results showed that the developed approach was the best method in predicting grinding condition with respect to surface finish quality.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.4
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• pp.37-43
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• 2008

Working condition is one of the most important factors in precision working. In this study, we optimized the vibration acceleration level(VAL) of Al 2024 alloy to select optimum working condition of side wall end-milling using RSM(Response Surface Methodology). RSM was well adapted to make analytic model for minimizing vibration acceleration, created the objective function and saved a great deal of computational time. Therefore, it is expected that the proposed optimization procedure using RSM can be easily utilized to solve the optimization problem of working condition. The experimental results of the surface roughness and VAL showed the validity of the proposed working condition of side wall end-milling as it can be observed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.5
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• pp.92-97
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• 1998

The morpholgies of the wear particles are directly indicative of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the machine driving system. The four parameters(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different patter characteristic and recognized the friction condition and materials very well by artificial neural network. We discussed how the network determines differencee in wear debris feature, and this approach can be applied to foreseeability and decisio for moving condition of the Machine driving system.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.6
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• pp.127-132
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• 2000

Morphological analysis of wear particles is one of useful methods for machine condition monitoring because it is well reflected in machine driving state. This paper was undertaken to apply to the condition monitoring of hydraulic piston motor. The lubricating wear test was performed under different experimental conditions using the wear test device and wear specimens of the pin on disk type was rubbed in paraffinic base oil by three kinds of lubricating materials, varying applied load, sliding distance. The four shape parameters(50% volumetric diameter, aspect, roundness and reflectivity) are used for morphological analysis of wear particles. The results showed that the four shape parameters of wear particles depend on a kind of the lubricating materials. It was capable of calculating presumed wear volume for three kinds of materials on driving time to foresee as damage condition of lubricating materials.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.1
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• pp.19-22
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• 2017

This paper describes the efficient equipment maintenance that can offer the exact time for repair and change of component in root industry. A conventional method offered the fixed time for repair and change of component because the method is based on early guarantee specification of the component. However the operating condition of manufacturing field is often under worse condition than early guarantee condition for high productivity. So, most components can't use until early guarantee time due to the operation of various different condition. Therefore we suggest the useful method for efficient equipment-maintenance by manufacturing-field analysis and feedback database. For this, the classification of root industry and related equipment is performed and then the detail classification of the process and component for equipment maintenance. And the monitoring module is also designed to gather data for feedback process and the environment is basically implemented for aging and reliability test.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.6
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• pp.517-521
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• 2016

General industrial robots are difficult to use for precision assembly because they are operated based on position control. Their position accuracy is also usually higher than the assembly clearance (several tens of ${\mu}m$). In previous researches, force control was suggested as a robotic assembly solution. However, this method is difficult to apply in reality because of speed and cost problems. The RCC provides high speed, but applications are limited because the compliance is fixed, and it cannot detect an assembly condition during a task. A variable passive compliance device (VPCD) was developed herein. The VPCD can detect the assembly condition during tasks. This device can provide proper compliance for successful assembly tasks. The pneumatic system and the Stewart platform with an LVDT sensor were applied for measuring the displacement and variable compliance, respectively. The concept design and analysis were conducted to prove the effectiveness of the developed VPCD.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.103-104
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• 2017

Objectives: The manufacturing industry was the one with the most repeated occurrence industrial disasters, and it is important to study the safety environment known by workers in the industry in order to prevent industrial accidents. Methods: Research objects were 1,123 male among members of employees who responded to 2015 PSWCI panel report. Research objects underwent affected subjective health and then categorized subjects into some groups according to changes in employment condition during one year. Statistical analyses were performed using the SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Results: According to results of analysis, manufacturing industry had differences in economic activity and health conditions based on employees conditions. Dynamic change aspects of manufacturing industry condition had differences between sex and cases where going back and forth between temporary condition and unemployment status had higher percentage in male. Conclusions: The results of this study may serve as the refer to taking a bill to improve the level of safety of the work environment felt by employees in the manufacturing industry.