• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.637-638
• /
• 2006

V-belt pulleys play a key role in driving cooling pump, oil pump, air-conditioner and so on by using an engine power. Precision deep drawing is one of the main processes for manufacturing the pulleys. Operation variables of the deep drawing equipment, called the setup parameter, must be re-determined whenever the specifications of pulley to be produced are changed. The defect rates during a setup of equipment and the working hours needed for the setup are almost dependent on workers' know-how. This study designs the program for easily determining setup conditions in pulley manufacturing process.

• Journal of the Korean Applied Science and Technology
• /
• v.40 no.6
• /
• pp.1363-1372
• /
• 2023

This study is concerned with the optimization of the manufacturing process of a hot water extract containing antioxidant activity from Lycium barbarum, traditionally known to have various physiological activities. For the establishment of the optimization process, the central composite design of response surface methodology(RSM) was used. Thirteen extraction processes were performed by encoding the independent variables, extraction temperature (65.9℃-94.1℃) and extraction time (2.59 hr-5.41 hr). As a result of the experiment, the optimal manufacturing conditions for the extract were 340.0 mg/100 g of GAE at an extraction temperature of 94.1℃ and an extraction time of 5 hr. The maximum yield of flavonoids was 22.44 mg/100 g of HES at an extraction temperature of 94.1℃ and an extraction time of 4 hr. The conditions for producing the extract with the maximum antioxidant capacity (DPPH 92.12%) were 90℃ and 4.5 hr extraction time. Therefore, the optimal manufacturing process conditions for extracts containing total phenol content, flavonoid content, and DPPH radical scavenging activity, which are dependent variables, were extraction temperature of 90-95℃ and extraction time of 4 hr, which were not significantly different from the actual values. Therefore, Lycium barbarum extract rich in total phenol and flavonoid content related to antioxidant function is expected to be used as a functional food and cosmetic material.

• Journal of Powder Materials
• /
• v.31 no.3
• /
• pp.213-219
• /
• 2024

Additive Manufacturing (AM) is a process that fabricates products by manufacturing materials according to a three-dimensional model. It has recently gained attention due to its environmental advantages, including reduced energy consumption and high material utilization rates. However, controlling defects such as melting issues and residual stress, which can occur during metal additive manufacturing, poses a challenge. The trial-and-error verification of these defects is both time-consuming and costly. Consequently, efforts have been made to develop phenomenological models that understand the influence of process variables on defects, and mechanical/ electrical/thermal properties of geometrically complex products. This paper introduces modeling techniques that can simulate the powder additive manufacturing process. The focus is on representative metal additive manufacturing processes such as Powder Bed Fusion (PBF), Direct Energy Deposition (DED), and Binder Jetting (BJ) method. To calculate thermal-stress history and the resulting deformations, modeling techniques based on Finite Element Method (FEM) are generally utilized. For simulating the movements and packing behavior of powders during powder classification, modeling techniques based on Discrete Element Method (DEM) are employed. Additionally, to simulate sintering and microstructural changes, techniques such as Monte Carlo (MC), Molecular Dynamics (MD), and Phase Field Modeling (PFM) are predominantly used.

• IE interfaces
• /
• v.13 no.1
• /
• pp.78-91
• /
• 2000

This paper presents a computerized quality forecasting system for glass manufacturing. In forecasting the molten glass quality, we are concerned with three major issues : (1) to find the reasonable time lags between a set of process conditions and the quality measurement of glass products, (2) to find the most significant process variables affecting the quality, and (3) to construct the appropriate causal forecasting models using genetic algorithms. The experimental results show the proposed model results in better forecasting than linear regression model. The suggested forecasting model was implemented successfully and is being currently used in a real manufacturing line.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.7
• /
• pp.97-104
• /
• 2021

LASOX is a cutting technology used to dismantle nuclear power plants. The core component of the laser-assisted oxygen hybrid cutting process is the supersonic nozzle. To design optimized supersonic nozzles, an experimental design was established and computational fluid dynamics was used to analyze the supersonic nozzles. The main factors affecting the supersonic nozzle performance were identified using Minitab. Further, the correlations and interactions between the main factors of the supersonic nozzle design were analyzed. The fluid analysis results were examined for the major factors and standardized response variables as well as main effects to ensure suitability of the supersonic nozzle design for the laser-assisted oxygen cutting process.

• Transactions of Materials Processing
• /
• v.19 no.5
• /
• pp.305-310
• /
• 2010

Recently industry of motor vehicle is making a gradual progress of automotive electric components. According to this step, wire harness equipped at motor vehicle is also increased. The most important component at the wire harness is electric connector. At the manufacturing process of electric connector, exactly at the crimping process, design variables, such as clamping-height, clamping-width and clamping die shape are critical parameters to assure satisfactory harness shape in clamping process of electric connector. In this study we have performed FEM simulation for clamping process and clarified the effect of design variables on compression rate of wire.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.18 no.7
• /
• pp.1-7
• /
• 2019

A chatter lobe analysis is frequently used to look at the chatter state. Even if there is a lot of research on chatter, chatter lobe characteristics are not well defined. In this study, the chatter lobe behavior according to several variables of vibration mode is verified for further clarity. The dynamic variables of the chatter model are defined and their behaviors on chatter lobe boundary are analyzed in detail. In this sense, the chatter model with 2-DOF (2-DOF) was used to analyze chatter stability characteristics. The discussed results are satisfying and these can be used for the prediction of chatter existence in machining processes of 2-DOF systems in several revolution range. These analyses indicate a better agreement for predicting an appropriate stability lobe over a wide detailed range of critical depths of cut in machining operation. The results allow an excellent prediction of chatter according to various static and dynamic variables in machining states. The behavior of chatter dynamic variables in machining were also discussed in detail. All these results can also be applied to other machining processes by establishing a chatter model in a 2-DOF system.

• Journal of Korean Institute of Industrial Engineers
• /
• v.41 no.1
• /
• pp.25-33
• /
• 2015

From the viewpoint of applications to manufacturing, data mining is a useful method to find the meaningful knowledge or information about states of processes. But the data from manufacturing processes usually have two characteristics which are multicollinearity and imbalance distribution of data. Two characteristics are main causes which make bias to classification rules and select wrong variables as important variables. In the paper, we propose a new data mining procedure to solve the problem. First, to determine candidate variables, we propose the multiple hypothesis test. Second, to make unbiased classification rules, we propose the decision tree learning method with different weights for each category of quality variable. The experimental result with a real PDP (Plasma display panel) manufacturing data shows that the proposed procedure can make better information than other data mining procedures.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.2
• /
• pp.401-409
• /
• 2007

This paper addresses a performance evaluation model for ERP system's implementation using Analytic Network Process (ANP) technique. In this study, the performance variables are identified as the perspectives of cost, business process, systems operation, and change management, respectively. The empirical study also investigated factors that affect the performance variables to find out the causal relationship between them using the ANP approach. The data for the empirical analysis were collected from manufacturing companies that have implemented ERP systems. The research findings indicate the proposed model is powerful in proposing that the indirect relationship between influencing factors and managerial effectiveness, mediated by employee satisfaction, is an important one.

• Design & Manufacturing
• /
• v.15 no.1
• /
• pp.41-47
• /
• 2021

The Fine Blanking process is an effective precision shearing process that can obtain a smooth cutting surface and high product precision through a single blanking process. It is widely used in various manufacturing fields. However, shearing through this fine blanking process is only intended to minimize burrs, die rolls and fracture surfaces and does not completely remove them. Therefore, it is necessary to study the minimization of burrs, die rolls and fracture surfaces in the fine blanking process. In this study, a study was conducted on the relationship between the fracture surface and process conditions that occurred during product production using the fine blanking process. For this purpose, the shape of the V-ring indenter, the distance to the punch, and the pressure force, clearance, shear rate, and physical properties of the material were selected as process and design variables, and the relationship with the fracture surface according to each process and design condition was tested. It was analyzed through the Experimental Design Method.