• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.117-124
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• 2017

In this study, we investigated the optimization of process conditions by using the Six Sigma process, design of experiment (DOE) method and response surface method (RSM) to resolve dimensional deviation and appearance problems arising from the shortened process time of the mobile phone rear cover. The analysis of the trivial many was performed by 2-sample T-test and cooling time, and mold temperature and packing pressure were selected as the vital fews affecting the overall width of the product. The optimal conditions of the process were then studied using the DOE and the RSM. We analyzed the improvement effects by applying the selected optimal conditions to the production process and the results showed that the difference between the mean value and target value of the overall width stood at 0.01 mm, an improvement of 88.89% compared to current process that fell within the range of standard dimension. The short-term process capability stood at $4.77{\sigma}$, which implied an excellent technology level despite a decrease by $0.22{\sigma}$ compared to the current process. The difference in process capability decreased by $2.44{\sigma}$ to $0.41{\sigma}$, showing a significant improvement in management capability. Ultimately, the process time of the product was shortened from 18.3 seconds in the current process to 13.65 seconds, resulting in a 34.07% improvement in production yield.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.100-107
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• 2015

Recently as the manufacturers want competitiveness in dynamically changing environment, they are trying a lot of efforts to be efficient with their production systems, which may be achieved by diminishing unplanned operation stops. The operation stops and maintenance cost are known to be significantly decreased by adopting proper maintenance strategy. Therefore, the manufacturers were more getting interested in scheduling of exact maintenance scheduling to keep smooth operation and prevent unexpected stops. In this paper, we proposedan integrated maintenance approach in injection molding manufacturing line. It consists of predictive and preventive maintenance approach. The predictive maintenance uses the statistical process control technique with the real-time data and the preventive maintenance is based on the checking period of machine components or equipment. For the predictive maintenance approach, firstly, we identified components or equipment that are required maintenance, and then machine parameters that are related with the identified components or equipment. Second, we performed regression analysis to select the machine parameters that affect the quality of the manufactured products and are significant to the quality of the products. By this analysis, we can exclude the insignificant parameters from monitoring parameters and focus on the significant parameters. Third, we developed the statistical prediction models for the selected machine parameters. Current models include regression, exponential smoothing and so on. We used these models to decide abnormal patternand to schedule maintenance. Finally, for other components or equipment which is not covered by predictive approach, we adoptedpreventive maintenance approach. To show feasibility we developed an integrated maintenance support system in LabView Watchdog Agent and SQL Server environment and validated our proposed methodology with experimental data.

• Polymer(Korea)
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• v.36 no.4
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• pp.461-469
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• 2012

The surface modification and characterization of Ar-plasma treated polypropylene (PP) blend are investigated using x-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and contact angle measurement. An increase in Ar-plasma treatment time leads to an increase in wettability, oxygen containing polar functional groups, the amount of talc, and surface roughness on the PP blend surface. A careful observation using SEM indicates that there exists a skin layer consisting of only PP component. The difference in viscosity between PP and rubber particles facilities the formation of skin layer. However, it is found that an increase in Ar-plasma treatment time helps to decrease the thickness of skin layer. Additional methodologies for the elimination of skin layer during injection molding are also discussed. The surface modification and morphological alteration induced by Ar-plasma treatment provides a hydrophilic state, followed by the improvement in wettability, on the PP blend surface.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.483-490
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• 2016

Automobile door latch is a fine design and assembly techniques are required in order to produce them in a small component assembly shape such as a spring, injection products, a small-sized motor. The door latch is fixed to not open the door of the car plays an important role it has a direct impact on the driver's safety. In this study, during trimming of the terminals of the connector main components of the car door latch, reduce rollover and conducted a research to find a suitable effective shear surface. Using the Taguchi method with orthogonal array of Finite Element Analysis and optimal Design of Experiments were set up parameters for the shear surface quality of the car door latch connector terminals. The design parameters used in the analysis is the clearance, the radius, and the blank holding force, the material of the connector terminal is a C2600. Trimming process optimum conditions suggested by the analysis has been verified by experiments, the shear surface shape and dimensions of a final product in good agreement with forming analysis results.Taguchi method from the above results in the optimization for the final rollover and effective shear surface improved for a vehicle door latch to the connector terminal can be seen that the applicable and useful for a variety of metal forming processes other than the trimming process is determined to be applicable.

• IE interfaces
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• v.25 no.1
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• pp.40-51
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• 2012

Today, many manufacturing companies realize that collaboration is crucial for their survival. Especially, in the perspective of quality, the importance of collaboration is emphasized because economic loss increases exponentially while defective parts go through the process in supply chain. However, the manufacturing companies are facing two main difficulties in implementing collaborative relationships with their suppliers. First, it is difficult for the suppliers to produce reliable products due to their obsolete facilities. The problem gets worse for second- or third-tire vendors. Second, the companies experience the lack of universally understandable set of terminology and effective methodologies for knowledge representation. Ontology is one of the best approaches to expressing and processing a domain knowledge. In this paper, we propose the manufacturing ontology-based quality prediction framework to represent and share the knowledge of industrial environment and to predict product quality in manufacturing processes. In addition, we develop the ontology-based quality prediction system based on the proposed framework. We carried out a series of experiments for an injection molding process at an automotive part supplier. The experimental results demonstrated that the proposed framework and system can be successfully applicable in manufacturing industry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1045-1050
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• 2011

This study proposed the new pressing method under heat for the plastic automotive interior part in order to make embossing on the skin of the raw material of the part. The raw material is laid on the lower mold and it is pressed by the upper one with embossing shape. The air is suctioned from the inside of both molds for producing tension and making embossing shapes on the skin of the part without its breakage. The corresponding molds and test machines are made and the proposed manufacturing process is validated.

• Journal of Powder Materials
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• v.10 no.1
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• pp.6-14
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• 2003

The purpose of the present study is to investigate the method decreasing debinding time as well as lowering operation condition than pure supercritical $CO_2$ debinding by using cosolvent or binary mixture of propane + $CO_2$. First method is to add cosolvent, such as n-hexane, DCM, methanol, 1-butanol, in supercritical $CO_2$. In case of adding cosolvent, we were found the addition of non-polar cosolvent (n-hexane) improves dramatically the binder removal rate (more than 2 times) compared with pure supercritical $CO_2$ debinding, second method is to use mixture of supercritical propane + $CO_2$, as solvent. In case of using mixture of supercritical propane + $CO_2$, the rate of debinding speeded up with increasing of pressure and concentration of propane at 348.15 K. It was found that addition of cosolvent (e.g., n-hexane, DCM) and binary mixture propane + $CO_2$ for supercritical solvent remarkably improved binder removal rate for the paraffin wax-based binder system, in comparison with using pure supercritical $CO_2$.

• Design & Manufacturing
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• v.14 no.4
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• pp.25-39
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• 2020

In this study, an edge device that monitors the injection molding process by measuring the mold vibration(acceleration) signal and the mold surface temperature was developed and evaluated its performance. During injection molding, signals of the injection start, V/P switchover, and packing end sections were obtained through the measurement of the mold vibration and the injection time and packing time were calculated by using the difference between the times of the sections. Then, the mold closed and mold open signals were obtained using a magnetic hall sensor, and cycle time was calculated by using the time difference between the mold closed time each process. As a result of evaluating the performance by comparing the process data monitored by the edge device with the shot data recorded on the injection molding machine, the cycle time, injection time, and packing time showed very small error of 0.70±0.38%, 1.40±1.17%, and 0.69±0.82%, respectively, and the values close to the actual were monitored and the accuracy and reliability of the edge device were confirmed. In addition, it was confirmed that the mold surface temperature measured by the edge device was similar to the actual mold surface temperature.

• Design & Manufacturing
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• v.15 no.4
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• pp.24-31
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• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.4
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• pp.465-472
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• 2017

In this study, tuna sawdust was added to extruded meat analog in order to develop a meat analog with high quality. Addition of tuna sawdust has merit for utilizing a byproduct from poultry processing. Physicochemical characteristics were examined through the extrusion cooking process. The basic mixture of sample mixed with 65% deffated soy flour 25% isolated soy protein, and 10% corn starch was setup as the raw material. Three kinds of samples were made in total by addition of 15% and 30% tuna sawdust to this mixture. The extrusion process had a screw speed of 250 rpm, die temperature of $140^{\circ}C$, and moisture content of 50%. As addition of tuna sawdust increased, breaking strength and density decreased, specific length increased, and integrity and water holding capacity decreased. Likewise, nitrogen solubility index and protein digestibility decreased as addition of tuna sawdust increased. DPPH radical scavenging activity increased as addition of tuna sawdust addition, whereas it decreased as storage period increased to 30 or 60 days. The value of rancidity decreased as addition of tuna sawdust increased. However, 60 days later, radical scavenging activity increased more or less, and a significant difference was detected 150 days later. In conclusion, addition of tuna sawdust increased soft texture, and nutrition of the basic mixture sample. The process promoting functionality such as improvement of antioxidant function was confirmed through this study.