• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.149-151
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• 2005

In this paper, we propose the Optimal Polynomial Neural Networks(PNN) for nonlinear process. The PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. Medical Imaging System(MIS) data is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.422-428
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• 2013

In many cases, an $\bar{X}$ control chart which is based on the performance variable is used in industrial fields. However, if the performance variable is too costly or impossible to measure and a less expensive surrogate variable is available, the process may be more efficiently controlled using surrogate variables. In this paper, we propose a model for the economic design of a VSI (Variable Sampling Interval) $\bar{X}$ control chart using a surrogate variable that is linearly correlated with the performance variable. The total average profit model is constructed, which involves the profit per cycle time, the cost of sampling and testing, the cost of detecting and eliminating an assignable cause, and the cost associated with production during out-of-control state. The VSI $\bar{X}$ control charts using surrogate variables are expected to be superior to the Shewhart FSI (Fixed Sampling Interval) $\bar{X}$ control charts using surrogate variables with respect to the expected profit per unit cycle time from economic viewpoint.

• Fashion & Textile Research Journal
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• v.14 no.4
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• pp.597-606
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• 2012

To increase the productivity and product quality of customized baseball jerseys, this study developed a multi-variable system for a production process that efficiently controls diverse production management factors. The working time was measured through the establishment of a standard process where skilled workers and Chinese factory workers manufactured 5 sets of the same basic design jerseys. Based on the measured working time (1,136 seconds/per unit), the multi-variable process control system was developed, where hourly production management is possible according to the involved workers and equipment types. Each process was assigned accoding to the production management factors for a total of 28 standard processes. The processes were developed based on consideration of work characteristics according to the order of needlework of open-type set baseball jerseys with sleeves(the basic design of baseball jerseys)to result in a customized production system structure that could be set up with multi-variables. As a result, a total 12 types of systems were developed in consideration of the personnel involved and the number of equipments. The optimal production management system (with the highest efficiency compared to the number of workers)was A-2, B-1, C-1. D-2, E-2, F-1, and G-1. This system had extremely high efficiency and showed 99% assignment efficiency for the 7-person team. Though not optimal, possible process assignment for each working personnel is proposed as a reserve process in case work modification is inevitable due to malfunctions and the absence of equipments.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.185-199
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• 2024

Purpose: The improvement of yield and quality in product manufacturing is crucial from the perspective of process management. Controlling key variables within the process is essential for enhancing the quality of the produced items. In this study, we aim to identify key variables influencing product defects and facilitate quality enhancement in CNC machining process using SHAP(SHapley Additive exPlanations) Methods: Firstly, we conduct model training using boosting algorithm-based models such as AdaBoost, GBM, XGBoost, LightGBM, and CatBoost. The CNC machining process data is divided into training data and test data at a ratio 9:1 for model training and test experiments. Subsequently, we select a model with excellent Accuracy and F1-score performance and apply SHAP to extract variables influencing defects in the CNC machining process. Results: By comparing the performances of different models, the selected CatBoost model demonstrated an Accuracy of 97% and an F1-score of 95%. Using Shapley Value, we extract key variables that positively of negatively impact the dependent variable(good/defective product). We identify variables with relatively low importance, suggesting variables that should be prioritized for management. Conclusion: The extraction of key variables using SHAP provides explanatory power distinct from traditional machine learning techniques. This study holds significance in identifying key variables that should be prioritized for management in CNC machining process. It is expected to contribute to enhancing the production quality of the CNC machining process.

• Design & Manufacturing
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• v.12 no.3
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• pp.60-65
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• 2018

The production of the automotive parts with the ultra high strength steel usually involves large amount of springback as well as fracture during the cold stamping process. Variable blank holding force(VBHF) can be used as one of the effective process parameters to reduce the springback amount with achieving better condition of formability. In this paper, VBHF with respect to the punch stroke is applied to the stamping process of the front side rear lower member for reducing the springback amount. From the analyses with constant blank holding force(CBHF), 24 kinds of VBHF conditions are utilized to investigate the springback tendency. It is noted that springback can be effectively reduced when BHF is increased near the bottom dead center because VBHF provides the tensile force to the blank with an adequate level of deformation without fracture.

• Design & Manufacturing
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• v.10 no.1
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• pp.31-35
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• 2016

In sheet metal forming process, drawing is typical process. And the key factor of drawing is blank holding force (BHF) A low BHF can cause wrinkling, whereas a high BHF can cause fracture during a deep drawing process. Thus, formability can be influenced by application appropriate BHF. In this study, a variable blank holding force (VBHF) is applied to extend the forming limit by avoiding both wrinkling and fracture. To determine VBHF in drawing process, numerical simulations and statistical analysis are carried out using commercial FEM software.

• Journal of the Korea Safety Management & Science
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• v.9 no.2
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• pp.215-233
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• 2007

Even though the ad hoc Shewhart methods remain controversial due to various mathematical flaws, there is little disagreement among researchers and practitioners when a set of process data has a skewness distribution. In the context and language of process control, the error related to the process data shows that time to signal increases when a control parameter shifts to a skewness direction. In real-world industrial settings, however, quality practitioners often need to consider a skewness distribution. To address this situation, we developed an enhanced design method to utilize advantages of the traditional attribute control chart and to overcome its associated shortcomings. The proposed design method minimizes bias, i.e., an average time to signal for the shift of process from the target value (ATS) curve, as well as it applies a variable sampling interval (VSI) method to an attribute control chart for detecting a process shift efficiently. The results of the factorial experiment obtained by various parameter circumstances show that the VSI c control chart using nearly unbiased ATS design provides the smallest decreasing rate in ATS among other charts for all experimental cases.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.489-499
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• 2000

We propose a new instrumental variable estimator of the complex parameter of a class of univariate complex-valued diffusion processes defined by the possibly non-stationary and/or nonlinear stochastic differential equations. On the basis of the exact finite sample distribution of the pivotal quantity, we construct the exact confidence intervals and the exact tests for the parameter. Monte-Carlo simulation suggests that the new estimator seems to provide a viable alternative to the maximum likelihood estimator (MLE) for nonlinear and/or non-stationary processes.

• Journal of Korean Society for Quality Management
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• v.30 no.2
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• pp.152-159
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• 2002

An acceptance sampling plan for products manufactured from a production process with variable fraction defective is developed. We consider a situation where defective products have short lifetimes and non-defective ones never fail during the technological life of the products. An acceptance criterion which guarantee the out going quality of accepted products is derived using the prior information on the quality of products. Numerical examples are provided.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.453-457
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• 2004

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with first-order autoregressive disturbance.