• Journal of the Korean Society for Precision Engineering
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• v.8 no.4
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• pp.55-63
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• 1991

This paper deals with development of a Computer Aided Process Planning System based on knowledge base in addition to database for injection mold as a part of Computer Integrated Manufacturing System for injection mold manufacture. The prposed system consists of four modules such as manufacturing feature code generation module machine tool selection and sequencing module, operation and cutting tool selection mudule and standard time estimation module. The system is programmed by using Turbo Pascal on the IBM-PC/AT. The performance of the system is evaluated by using real problems and the test results indicate that the proposed system is a practical and efficient system.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.312-318
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• 2000

In this study, an exclusive CAD/CAM system is developed for enhancing the effectiveness and productivity of CNC universal cylindrical grinding machines on which the external/facing/internal grinding cycles and the wheel dressing cycles are integratively carried out. The CAD/CAM system can manage the various processes such as geometry design, NC code generation, NC code verification, DNC operation, and so on. Especially, the feature-based modeling concept is introduced to improve the geometry design efficiency. And the NC code verification is realized by virtual manufacturing technique based on the real-time analysis of NC codes and the boolean operation between workpiece and wheel.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.7
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• pp.1604-1618
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• 1995

In the process of mechanical assembly design, assembly modeling systems have been used mainly for the design verification before manufacturing by enabling to check the interference and/ or the dynamic and kinematic performance. However, the conventional assembly modeling systems have a shortcoming that they can not be used in the initial design stage but can be used only after the design is fully completed. In other words conventional assembly modeling systems provide bottom-up modeling which means that the detailed modeling of components must precede the definition of relationships between them. To resolve this problem, an assembly modeling system is proposed to provide a top-down modeling environment in which components and assembly can be modeled simultaneously. To this end, an assembly data structure suitable for top-down assembly modeling has been established. Feature positioning Module(FPM) using geometric constraints has been also developed. The Sekective Solving Method proposed for FPM is based on the priority between the constraint equations and enables the designer's intent expressed by geometric constraints to be maintained throughout the whole modeling process. Finally, the feature based modeling technique using two-level features has been developed. Two-level features include an abstract model and a detailed model in a merged form in non-manifold data frame.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.415-424
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• 2002

Semiconductor manufacturing has been emerged as a highly competitive but profitable business. Accordingly it becomes very important for semiconductor manufacturing companies to meet customer demands at the right time, in order to keep the leading edge in the world market. However, due-date oriented production is very difficult task because of the complex job flows with highly resource conflicts in fabrication shop called FAB. Due to its cyclic manufacturing feature of products, to be completed, a semiconductor product is processed repeatedly as many times as the number of the product manufacturing cycles in FAB, and FAB processes of individual manufacturing cycles are composed with similar but not identical unit processes. In this paper, we propose a production scheduling and control scheme that is designed specifically for semiconductor scheduling environment (FAB). The proposed scheme consists of three modules: simulation module, cycle due-date estimation module, and dispatching module. The fundamental idea of the scheduler is to introduce the due-date for each cycle of job, with which the complex job flows in FAB can be controlled through a simple scheduling rule such as the minimum slack rule, such that the customer due-dates are maximally satisfied. Through detailed simulation, the performance of a cycle due-date based scheduler has been verified.

• Journal of the Korea Society for Simulation
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• v.21 no.2
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• pp.79-90
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• 2012

In this paper, a feature signal extraction method is proposed in order to enhance the low performance of fault detection caused by unbalanced data which denotes the situations when severe disparity exists between the numbers of class instances. Most of the cyclic signals gathered during the process are recognized as normal, while only a few signals are regarded as fault; the majorities of cyclic signals data are unbalanced data. SOM(Self-Organizing Map)-based feature signal extraction method is considered to fix the adverse effects caused by unbalanced data. The weight neurons, mapped to the every node of SOM grid, are extracted as the feature signals of both class data which are used as a reference data set for fault detection. kNN(k-Nearest Neighbor) and SVM(Support Vector Machine) are considered to make fault detection models with comparisons to Hotelling's $T^2$ Control Chart, the most widely used method for fault detection. Experiments are conducted by using simulated process signals which resembles the frequent cyclic signals in semiconductor manufacturing.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.115-125
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• 2011

Manufacturing companies usually manage the process to achieve high quality using various types of control chart in statistical process control. When an assignable cause occurs in a process, the data in the control chart changes with different patterns by the specific causes. It is important in process control to classify the CCP (Control Chart Pattern) recognition for fast decision making. In former research, gathered data from process used to apply as raw data, leads to degrade the performance of recognizer and to decrease the learning speed. Therefore, feature based recognizer, employing feature extraction method, has been studied to enhance the classification accuracy and to reduce the dimension of data. We propose the method to extract features that take the distances between CCP data and reference vector generated from BDK (Bi-Directional Kohonen Network). We utilize those features as the input vectors in ANN (Artificial Neural Network) and compare with raw data applied ANN to evaluate the performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.305-307
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• 2021

In this paper, we propose a study related to the development of a Digital-Twin platform using a smart factory based CPPS (Cyber Pysical Production System) using ICT (Information Communication Technology) technology. The platform developed through this study performs a 3D model simulation function in conjunction with P3R (Product, Process, Plant, Resource) including BOP (Bill of Process) management function from the preceding manufacturing process planning stage. In addition, we propose a digital twin platform that can predict production processes, equipment, layout, and production. The platform proposed through this paper proposes a feature that can manage the entire smart factory manufacturing process from the initial planning design stage to the manufacturing, production, operation, and maintenance stages.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.335-340
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• 2014

Crowd Sourcing-based Social App Manufacturing environment, a small app development project by a team of anonymous virtual performed without the constraints of time and space, and manage it for the app development process need to be automated method. Virtual teams with anonymity is a feature of the Social App Manufacturing, is an important factor that increases the uncertainty of whether the completion of the project or reduction in visibility of the progress of the project. In this study, as one of how to manage the project of Social App Manufacturing environment, the impact of risk that can be used to quantitatively measure the impact of the risk of delay in development has on the project also proposes a measurement model. Effects of risk and type of the impact of risks associated with delays in the work schedule also define the characteristic function, measurement model that has been proposed, suggest the degree of influence measurement equation of risk of the project in accordance with the progressive. The advantage of this model, the project manager is able to ensure the visibility of the progress of the project. In addition, identify the project risk of work delays, and to take precautions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.369-377
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• 2023

In the industrial manufacturing sector, quality control is pivotal for minimizing defect rates; inadequate management can result in additional costs and production delays. This study underscores the significance of detecting texture defects in manufactured goods and proposes a more precise defect detection technique. While the DFR(Deep Feature Reconstruction) model adopted an approach based on feature map amalgamation and reconstruction, it had inherent limitations. Consequently, we incorporated a new loss function using statistical methodologies, integrated a skip connection structure, and conducted parameter tuning to overcome constraints. When this enhanced model was applied to the texture category of the MVTec-AD dataset, it recorded a 2.3% higher Defect Segmentation AUC compared to previous methods, and the overall defect detection performance was improved. These findings attest to the significant contribution of the proposed method in defect detection through the reconstruction of feature map combinations.