• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.04a
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• pp.169-172
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• 2003

Manufacturing process for autonomic microcapsules was introduced and autonomic microcapsules were manufactured by varying with various manufacturing process variables. Urea-formaldehyde resin was used for the wall of microcapsules and DCPD (dicyclopentadiene) was used for the self-healing agent. The characteristics of these microcapsules was evaluated through a particle size analyaer, an optical microscope, and a TGA. The various manufacturing process variables, such as pH and agitation speed of the emulsified solution, were considered to focus in this study. According to the results, the particle size distributions were affected on the agitation speed of the emulsified solution, and the thermal stability was influenced by pH of the emulsified solution.

• Korean Management Science Review
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• v.12 no.2
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• pp.165-188
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• 1995

The conversion processes from traditional job shops to cellular manufacturing systems can be viewed as an aggregation of cause-and-effect relationships among many strategic, managerial, and technical variables. Therefore, management needs to fully understand these interacting variables and possible relationships between the variables to successfully convert their plants to cellular manufacturing systems. The purpose of this study is to assist such management's needs in part. The objectives of this research are i) investigating contingency variables that may affect the conversion processes and outcomes to cellular manufacturing systems and ii) examining relationships between the variables and the conversion processes and outcomes. In this paper, particularly three categories of variables are examined: product, process routing, and process technology / facilities characteristics. Literature review and the mail survey method are used. The results are compared and synthesized with the findings of previous studies for useful discussions. Some previous arguments and propositions are empirically supported.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.3
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• pp.154-163
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• 2010

Process monitoring of output variables affecting final performance have been mainly executed in semiconductor manufacturing process. However, even earlier detection of causes of output variation cannot completely prevent yield loss because a number of wafers after detecting them must be re-processed or cast away. Semiconductor manufacturers have put more attention toward monitoring process inputs to prevent yield loss by early detecting change-point of the process. In the paper, we propose the method to efficiently monitor functional input variables in multi-phase semiconductor manufacturing process. Measured input variables in the multi-phase process tend to be of functional structured form. After data pre-processing for these functional input data, change-point analysis is practiced to the pre-processed data set. If process variation occurs, key variables affecting process variation are selected using contribution plot for monitoring efficiency. To evaluate the propriety of proposed monitoring method, we used real data set in semiconductor manufacturing process. The experiment shows that the proposed method has better performance than previous output monitoring method in terms of fault detection and process monitoring.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.579-587
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• 2015

Semiconductor manufacturing industry is a high density integration industry because it generates a vest number of data that takes about 300~400 processes that is supervised by numerous production parameters. It is asked of engineers to understand the correlation between different stages of the manufacturing process which is crucial in reducing production costs. With complex manufacturing processes, and defect processing time being the main cause. In the past, it was possible to grasp the corelation among manufacturing process stages through the engineer's domain knowledge. However, It is impossible to understand the corelation among manufacturing processes nowadays due to high density integration in current semiconductor manufacturing. in this paper we propose a canonical correlation analysis (CCA) using both wafer test voltage variables and fail bit counts variables. using the method we suggested, we can increase the semiconductor yield which is the result of the package test.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.166-171
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• 2005

With development of the database, there are too many data on process variables and the manufacturing process for the traditional statistical process control methods to identify the process variables related with assignable causes. Data mining is useful in this situation and provides variety of approaches for improving the process. In this paper, we applied control charts to monitor the process and if assignable causes are detected, then we applied the SVM technique and the sequence pattern analysis to find out the process variables suspected. These techniques made possible to predict the behavior of process variables. We illustrated our proposed methods with real manufacturing process data.

• Design & Manufacturing
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• v.16 no.3
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• pp.50-57
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• 2022

The qualities of the products produced by injection molding are strongly influenced by the process variables of the injection molding machine set by the engineer. It is very difficult to predict the qualities of the injection molded product considering the stochastic nature of the manufacturing process, since the processing conditions have a complex impact on the quality of the injection molded product. It is recognized that the artificial neural network(ANN) is capable of mapping the intricate relationship between the input and output variables very accurately, therefore, many studies are being conducted to predict the relationship between the results of the product and the process variables using ANN. However in the condition of a small number of data sets, the predicting performance and robustness of the ANN model could be reduced due to too many input variables. In the present study, the ANN model that predicts the length of the injection molded product for multiple combinations of process variables was developed. And the accuracy of each ANN model was compared for 8 process variables and 4 important process inputs that were determined by the feature selection. Based on the comparison, it was verified that the performance of the ANN model increased when only 4 important variables were applied.

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.243-260
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• 2008

To prevent low yields in the semiconductor industry is crucial to the success of that industry. However, to prevent low yields is difficult because of too many factors to affect yield variation and their complex relation in the semiconductor manufacturing process. This study presents a new efficient detection methodology for detecting abnormal yields including high and low yields, which can forecast the yield level of a production unit (namely a lot) based on yield-related feature variables' behaviors. In the methodology, we use C5.0 to identify the yield-related feature variables that are the combination of correlated process variables associated with yield, use SOM (Self-Organizing Map) neural networks to extract and classify significant patterns of past abnormal yield lots and finally use C5.0 to generate classification rules for detecting abnormal yield lot. We illustrate the effectiveness of our methodology using a semiconductor manufacturing company's field data.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.29-40
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• 2000

Process planning (selection and ordering of processes) and material selection for product manufacturing are two key things determined before taking full-scale manufacturing. Knowledge on product design. material characteristics, processes, time and cost all-together are mutually related and should be considered concurrently. Due to the complexity of problem, human experts have got only one of the feasilbe solutions with their field knowledge and experiences. We propose a hierarchical expert system framework of knowledge representation and reasoning in order to overcome the complexity. Manufacturing processes have inherently hierarchical relationships, from top level processes to bottom level operation processes. Process plan of one level is posted in process blackboard and used for lower level process planning. Process information on blackboard is also used to adjust the process plan in order to resolve the dead-end or inconsistency situation during reasoning. Decision variables for process, material, tool, time and cost are represented as object frames, and their relationships are represented as constraints and rules. Constraints are for relationship among variables such as compatibility, numerical inequality etc. Rules are for causal relationships among variables to reflect human expert\`s knowledge such as process precedence. CRSP(Constraint and Rule Satisfaction Problem) approach is adopted in order to obtain solution to satisfy both constraints and rules. The trade-off procedure gives user chances to see the impact of change of important variables such as material, cost, time and helps to determine the preferred solution. We developed the prototype system using visual C++ MFC, UNIK, and UNlK-CRSP on PC.

• Transactions on Electrical and Electronic Materials
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• v.13 no.3
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• pp.129-135
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• 2012

A number of surface mount technology (SMT) process variables including land design are considered for minimizing tombstone defect in flexible printed circuit assembly in high volume manufacturing. As SMT chip components have been reduced over the past years with their weights in milligrams, the torque that once helped self-centering of chips, gears to tombstone defects. In this paper, we have investigated the correlation of the assembly process variables with respect to the tombstone defect by employing statistically designed experiment. After the statistical analysis is performed, we have setup hypotheses for the root causes of tombstone defect and derived main effects and interactions of the process parameters affecting the hypothesis. Based on the designed experiments, statistical analysis was performed to investigate significant process variable for the purpose of process control in flexible printed circuit manufacturing area. Finally, we provide beneficial suggestions for find-pitch PCB design, screen printing process, chip-mounting process, and reflow process to minimize the tombstone defects.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.262-265
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• 2003

In the present study, subframe was developed using hydroforming technology. The manufacturing process for subframe consists of tube bending, pre-forming and hydroforming. The effects of bending process for manufacturing hydroformed subframe were researched. And the variables of bending process were studied by FEM simulation. The bending method is rotary draw bending that is the most popular, cost-effective bending method for thin walled tubes.