• Journal of Korean Society for Quality Management
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• v.51 no.1
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• pp.55-65
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• 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 the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.147-153
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• 1997

Fault diagnosis technique in machining system which is one of engineering techniques absolutely necessary to automation of manufacturing system has been proposed. As a whole, diagnosis process is explained by two steps: sensor data acquisition and reasoning current state of system with the given sensor data. Flexible disk grinding process implemented in milling machine was employed in order to obtain empirical manufacturing process information. Resistance force data during machining were acquired using tool dynamometer known as sensor which is comparably accurate and reliable in operation. Tool status during the process was analyzed using influnece diagram assigning probability from the statistical analysis procedure.

• IE interfaces
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• v.21 no.4
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• pp.435-443
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• 2008

RFID (Radio Frequence Identification) is emerging technology and it is used for many fields. The research about RFID has been focused on hardware such as increasing hitting ratio. But recently the research about how and where RFID can apply is going on. Especially people are doing research about connecting RFID with existing information system. In this paper, when BPM manage RFID based manufacturing operation process, we show components which is needed and how we can obtain the components. And we apply this to straw manufacturing process on experiment. The claim-handling process and order-handling process which need improvement in straw manufacturing are chosen and we improve those processes. For executing the improved process, we define components which are database, data acquisition workflow and real-time event processing system and then we make prototype system.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.3 s.147
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• pp.392-398
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• 2006

A prototype of MES(Manufacturing Execution System) applied to panel assembly shop is implemented by using PDA(Personal Digital Assistant) and wireless database web server. The system is developed based on the Dot Net framework. The prototype can exchange the manufacturing execution data between production managers in the control room and workers in the factory through wireless internet communication. Manufacturing model of the panel shop is designed by using IDEF0 and UML method to understand the characteristics of the information and the data entities from the PPR-S view. Several issues in the shop were revealed from the manufacturing model analysis. The most typical problem was the lack of information sharing between the managing workers and the assembly workers. The problem prevents the workers and labors from sharing the process information and continuous workpiece flow In interactive way. To increase the information and data flow, a wireless internet based system is implemented and PDAs are linked together to exchange the process planning data and in-process data between the workers. It is anticipated that PDA and the implemented system can enable the process control at each process stages to obtain the well-organized operation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.132-139
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• 2009

Yield is a very important measure that can expresses simply for productivity and performance of company. So, yield is used widely in many industries nowadays. With the development of the information technology and online based real-time process monitoring technology, many industries operate the production lines that are developed into automation system. In these production lines, the product structures are very complexity and variety. So, there are many multi-variate processes that need to be monitored with many quality characteristics and associated process variables at the same time. These situations have made it possible to obtain super-large manufacturing process data sets. However, there are many difficulties with finding the cause of process variation or useful information in the high capacity database. In order to solve this problem, neural networks technique is a favorite technique that predicts the yield of process for process control. This paper uses a neural networks technique for improvement and maintenance of yield in manufacturing process. The purpose of this paper is to model the prediction of a sub process that has much effect to improve yields in total manufacturing process and the prediction of adjustment values of this sub process. These informations feedback into the process and the process is adjusted. Also, we show that the proposed model is useful to the manufacturing process through the case study.

• Journal of Powder Materials
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• v.27 no.2
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• pp.111-118
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• 2020

The convergence of artificial intelligence with smart factories or smart mechanical systems has been actively studied to maximize the efficiency and safety. Despite the high improvement of artificial neural networks, their application in the manufacturing industry has been difficult due to limitations in obtaining meaningful data from factories or mechanical systems. Accordingly, there have been active studies on manufacturing components with sensor integration allowing them to generate important data from themselves. Additive manufacturing enables the fabrication of a net shaped product with various materials including plastic, metal, or ceramic parts. With the principle of layer-by-layer adhesion of material, there has been active research to utilize this multi-step manufacturing process, such as changing the material at a certain step of adhesion or adding sensor components in the middle of the additive manufacturing process. Particularly for smart parts manufacturing, researchers have attempted to embed sensors or integrated circuit boards within a three-dimensional component during the additive manufacturing process. While most of the sensor embedding additive manufacturing was based on polymer material, there have also been studies on sensor integration within metal or ceramic materials. This study reviews the additive manufacturing technology for sensor integration into plastic, ceramic, and metal materials.

• The Journal of Information Systems
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• v.20 no.3
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• pp.1-24
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• 2011

Recently, six sigma has been widely adopted in a variety of industries as a disciplined, data-driven problem solving approach or methodology supported by a handful of powerful statistical tools in order to reduce variation through continuous process improvement. Also, data mining has been widely used to discover unknown knowledge from a large volume of data using various modeling techniques such as neural network, decision tree, regression analysis, etc. This paper proposes a six sigma methodology based on data mining for effectively and efficiently processing massive data in driving six sigma projects. The proposed methodology is applied in the hot stove system which is a major energy-consuming process in a "P" steel company for improvement of heat efficiency through reduction of energy consumption. The results show optimal operation conditions and reduction of the hot stove energy cost by 15%.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.67-76
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• 2017

In industrial site in various fields it will be generated in combination with large amounts of data have a correlation. It is able to collect a variety of data in types of industry process, but they are unable to integrate each other's association between each process. For the data of the existing industry, the set values of the molding condition table are input by the operator as an arbitrary value When a problem occurs in the work process. In this paper, design the fusion and analysis processing model of data collected for each industrial type, Prediction Case(Automobile Connect), a through for corporate earnings improvement and process manufacturing industries such as master data through standard molding condition table and the production history file comparison collected during the manufacturing process and reduced failure rate with a new molding condition table digitized by arbitrary value for worker, a new pattern analysis and reinterpreted for various malfunction factors and exceptions, increased productivity, process improvement, the cost savings. It can be designed in a variety of data analysis and model validation. In addition, to secure manufacturing process of objectivity, consistency and optimization by standard set values analyzed and verified and may be optimized to support the industry type, fits optimization(standard setting) techniques through various pattern types.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.43-51
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• 2014

Statistical process control (SPC) is an important technique for monitoring and managing the manufacturing process. In spite of its easiness and effectiveness, some problematic sides of application exist such that the SPC techniques are hardly reflect the changes of the process conditions. Especially, update of control limits at the right time plays an important role in acquiring a reasonable performance of control charts. Therefore, we propose the control chart performance evaluation index (CPEI) based on count data model to monitor and manage the performance of control charts. The CPEI could indicate the degree of control chart performance and be helpful to detect the proper update cycle of control limits in real time. Experiments using real manufacturing data show that the proper update intervals are made by proposed method.

• Journal of Korean Society for Quality Management
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• v.30 no.3
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• pp.111-119
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• 2002

Gears are essential elements for mechanical power transmission. Geometric precision is the main factor for characterizing gear grade and qualify. Gear pitch is one of the crucial measurements, which is defined as a distance between two adjacent gear teeth. It is well-known that variability in gear pitches may causes wear-out and vibration noise. Therefore maintaining pitch errors at a low level plays a key role in assuring the gear quality to customers. This paper is concerned with a case study, which presents a computerized system for Inspecting pitch errors in a gear machining process. This system consists of a PC and window-based programs. Although the start and stop is manually accomplished, the process of measuring and analyzing pitch data is automatically conducted in this system. Our purpose lies in reducing inspection cost and time as well as Increasing test reliability. Its operation is briefly illustrated by example. Sometimes a strong autocorrelation is observed from pitch data. We also discuss a process monitoring scheme taking account of autocorrelations.