• Journal of Korean Institute of Industrial Engineers
• /
• v.36 no.3
• /
• pp.154-163
• /
• 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 Society of Industrial and Systems Engineering
• /
• v.33 no.1
• /
• pp.33-40
• /
• 2010

Because sampling interval for data collection tends to be short compared with the overall processing time, in chemical process, instrumental process related tanks or furnace collected data have a significant autocorrelation. Insufficient control technique and frequent control actions cause unstable condition of the process. Traditional control charts which were developed based on iid (independently and identically distributed) among data cannot be applied on the existence of autocorrelation. Also unstable process is difficult to identity or diagnose. Because large-scale process has a lot of measurable variables and multi-step-structures among data, it is difficult to find relation between measurable variables and nonconformity. In this paper, we suggested an appicable model to diagnose the process and to find relation between measurable variables (CTQ) and nonconformity in the process having autocorrelation, unstable condition frequently, a lot of measurable variables, and multi-step-structure. And we applied this model to real process, to verify that the process engineers could easily and effectively diagnose the process and control the nonconformity.

• Journal of Korean Society for Quality Management
• /
• v.32 no.3
• /
• pp.109-125
• /
• 2004

This paper deals with an optimal process control problem in production of hull structural steel plate with high defective rate. The main quality characteristic(dependent variable) is the internal quality(defect) of plates and is dependent on process parameters(independent variables). The dependent variable(quality characteristics) has three categorical ordinal data and there are 35 independent variables(29 continuous variables and 6 categorical variables). In this paper, we determine the main factors and to develop the mathematical model between internal quality predicted probabilities and the main factors. Secondly, we find out the optimal process condition of main factors through analysis of variance(ANOVA) using simulation. We consider three models to obtain the main factors and the optimal process condition: linear, quadratic, error models.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 1999.03a
• /
• pp.22-25
• /
• 1999

'There are a lot of process variables, exerted influence on the formability of products, in deep drawing process. Particularly, it is important that the punch and die shape radius of the process variables. Though researches have been performed on the deep drawing of sheet metal forming, like this study, but it is insufficient the actual circumstances that researches for process variables of the non-axisymmetric deep drawing products. In this study, An effect on thickness distribution is grasped as alteration of the punch and die shape radius in the process of non-axisyrnmetric deep drawing products, and then the optimal punch and die shape radius were presented, they were verified by the numerical analysis method (FEM).

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.11a
• /
• pp.633-636
• /
• 2006

In order to determine new settings of key process variables optimally, a new rule induction method through a historical data is proposed without using an explicit functional model between process and quality variables. First, a partial least square is used to reduce the dimensionality of the process variables. Then new process settings that yield the best quality variable are identified by sequentially partitioning the reduced latent variable space using a patient rule induction method. The proposed method is illustrated with a case study obtained from steel-making processes. We also show, through simulation, that the proposed method gives more stable results than estimating an explicit function even when the form of the function is known in advance.

• Journal of Korean Society for Quality Management
• /
• v.51 no.1
• /
• pp.55-65
• /
• 2023

Purpose: The purpose of this study is to propose an optimization process to improve product yield in the process using process data. Recently, research for low-cost and high-efficiency production in the manufacturing process using machine learning or deep learning has continued. Therefore, this study derives major variables that affect product defects in the manufacturing process using eXplainable Artificial Intelligence(XAI) method. After that, the optimal range of the variables is presented to propose a methodology for improving product yield. Methods: This study is conducted using the injection molding machine AI dataset released on the Korea AI Manufacturing Platform(KAMP) organized by KAIST. Using the XAI-based SHAP method, major variables affecting product defects are extracted from each process data. XGBoost and LightGBM were used as learning algorithms, 5-6 variables are extracted as the main process variables for the injection process. Subsequently, the optimal control range of each process variable is presented using the ICE method. Finally, the product yield improvement methodology of this study is proposed through a validation process using Test Data. Results: The results of this study are as follows. In the injection process data, it was confirmed that XGBoost had an improvement defect rate of 0.21% and LightGBM had an improvement defect rate of 0.29%, which were improved by 0.79%p and 0.71%p, respectively, compared to the existing defect rate of 1.00%. Conclusion: This study is a case study. A research methodology was proposed in the injection process, and it was confirmed that the product yield was improved through verification.

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

• Korean Management Science Review
• /
• v.12 no.2
• /
• pp.165-188
• /
• 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 Society for Quality Management
• /
• v.33 no.3
• /
• pp.135-148
• /
• 2005

This paper considers a six sigma project for reducing the project costs through project risk management. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A risk management process map is used to identify process input and output variables. Seven key process input variables are selected by using C&E diagram and X-Y matrix and finally four vital few input variables are selected by the related statistical analysis. The optimum alternatives of the vital few input variables are obtained by the method of PUGH matrix. The process is running on control plan and we obtained substantial project cost reductions in early stage of the control phase.

• Proceedings of the KWS Conference
• /
• 1997.10a
• /
• pp.205-209
• /
• 1997

One of the major important tasks in the robotic $CO_2$ arc welding process is to understand how process variables affected bead geometry and to subsequently develop the mathematical models to predict the desired bead dimensions. Experiment results are compared to outputs obtained using a set of published formulae relating input variables to output parameters and also investigated process variables on bead geometry for robotic $CO_2$ arc welding process The university of results obtained using empirical equations taken from existing models provided to be limited in predicting experimental bead shapes.