• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.467-477
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• 1994

The objective of this research is to develop a manufacturability measurement model for process and material screening. The process and material screening is the key requirement for implementing the Design for Manufacturability (Concurrent Engineering). A computerized system realizing this model then is developed to aid designers. Identification of the key factors which influence technical manufacturability, decision variables and their characteristics, conceptual framework for implementing the model are suggested. Manufacturability measure for quantifying the consistency of between the product requirements and the manufacturing capability is important contribution of this research. The focus is on net shape manufacturing process such as diecasting, forging, metal forming and injection molding.

• Design & Manufacturing
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• v.15 no.1
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• pp.48-56
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• 2021

The quality of products produced by injection molding process is greatly influenced by the process variables set on the injection molding machine during manufacturing. It is very difficult to predict the quality of injection molded product considering the stochastic nature of manufacturing process, because the process variables complexly affect the quality of the injection molded product. In the present study we predicted the quality of injection molded product using Artificial Neural Network (ANN) method specifically from Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) perspectives. In order to train the ANN model a systematic plan was prepared based on a combination of orthogonal sampling and random sampling methods to represent various and robust patterns with small number of experiments. According to the plan the injection molding experiments were conducted to generate data that was separated into training, validation and test data groups to optimize the parameters of the ANN model and evaluate predicting performance of 4 structures (MISO1-2, MIMO1-2). Based on the predicting performance test, it was confirmed that as the number of output variables were decreased, the predicting performance was improved. The results indicated that it is effective to use single output model when we need to predict the quality of injection molded product with high accuracy.

• Composites Research
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• v.16 no.2
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• pp.54-61
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• 2003

In this study, manufacturing process for microcapsules with the self-healing agent was introduced and the characteristics of microcapsules manufactured by varying with various manufacturing process variables were evaluated through a particle size analyzer, an optical microscope, and a TGA. Urea-formaldehyde resin was used for the thin wall of microcapsules and DCPD (dicyclopentadiene) was used for the self-healing agent. The various manufacturing process variables, such as (1) 24hr, 40hr, 48hr, 60hr of the solution time of the EMA copolymer, (2) pH3.5, pH4.0, pH4.5 of the hydrogen ion concentration of the emulsified solution, (3) 400rpm, 500rpm, 600rpm, 1000rpm of the agitation speed of the emulsified solution, (4) $50^{\circ}$, $55^{\circ}$, $60^{\circ}$ of the reaction temperature of the emulsified solution, were considered. According to the results, the particle size distribution of microcapsules was affected on the agitation speed, and the thermal stability of microcapsules was influenced by the solution time of the EMA copolymer, the hydrogen ion concentration, and the reaction temperature of the emulsified solution. Therefore, suitable manufacturing process variables should be applied to obtain thermally stable microcapsules capable of containing the healing agent capable until the thin wall of microcapsules were to be burned.

• Korean Management Science Review
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• v.13 no.1
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• pp.135-156
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• 1996

The purpose of this paper is to study the links between manufacturing strategy and change programs of manufacturing firms in Korea. The nature of our analysis is more descriptive than normative or confirmative. First, we investigate the linkage between manufacturing strategy, manufacturing capability, change programs and performance measurement systems. Secondly, we intend to explore an empirical typology of manufacturing strategy and change programs. The initial findings of the study are as follows: Linkage between manufacturing strategy and change programs of manufacturing firms was not apparent. Flexibility as a strategic priority is positively related to the innovative change programs. There are two distinctive strategies of manufacturing firms in Korea, namely, quality strategy and balanced strategy. We identified three types of change programs, which are incremental change, administrative innovation, and process innovation. Incremental change group has represented low factor score in the change program dimensions. The firms in administrative innovation group mainly depend on information systems and business reengineering. Korean manufacturing firms seem to pursue process improvement by trying simultaneously incremental change in the process, managerial process improvement, and a little bit of innovative change. Performance variable utilization of the firms are not significantly different along with manufacturing strategies and change programs. We found, however, learning and improvement performance dimension is significantly related to flexibility variables. Process innovation group marked high score in usage of learning and improvement indices. The findings of this study seem to have various implications on realigning the manufacturing strategy, change programs, and performance systems in Korean firms.

• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.121-130
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• 2009

This paper considers a six sigma project for reducing the cost copper of the cable materials in a electric wire company. The project follows a disciplined process of five macro phases: define, measure, analyze, improve, and control (DMAIC). A process map is used to identify process input variables. Three key process input variables are selected by using an input variables are selected by using an input variable evaluation table: large cable, plating, and a twisted pair. DOE is utilized for finding the optimal process conditions of the three key process input variables. The implementing result of this six sigma project is enable for reducing of the 2.8% copper materials.

• IE interfaces
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• v.18 no.4
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• pp.465-476
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• 2005

Evolutionary operation is a useful tool for improving full-scale manufacturing process by systematically changing the levels of the process variables without jeopardizing the product. This paper presents a system design for the evolutionary operation software called 'evolutionary optimizer'. Evolutionary optimizer consists of four modules: factorial design, many variables, mixture, and mean/dispersion. Context diagram, data flow diagram and entity-relationship modelling are used to systematically design the evolutionary optimizer system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.1
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• pp.46-56
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• 2012

The purpose of virtual metrology (VM) in semiconductor manufacturing is to predict every wafer's metrological values based on its process equipment data without an actual metrology. In this paper, we propose novelty detection-based reliability estimation models for VM in order to support flexible utilization of VM results. Because the proposed model can not only estimate the reliability of VM, but also identify suspicious process variables lowering the reliability, quality control actions can be taken selectively based on the reliance level and its causes. Based on the preliminary experimental results with actual semiconductor manufacturing process data, our models can successfully give a high reliance level to the wafers with small prediction errors and a low reliance level to the wafers with large prediction errors. In addition, our proposed model can give more detailed information by identifying the critical process variables and their relative impacts on the low reliability.

• Journal of Powder Materials
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• v.30 no.3
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• pp.223-232
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• 2023

Metal additive manufacturing (AM) has transformed conventional manufacturing processes by offering unprecedented opportunities for design innovation, reduced lead times, and cost-effective production. Aluminum alloy, a material used in metal 3D printing, is a representative lightweight structural material known for its high specific strength and corrosion resistance. Consequently, there is an increasing demand for 3D printed aluminum alloy components across industries, including aerospace, transportation, and consumer goods. To meet this demand, research on alloys and process conditions that satisfy the specific requirement of each industry is necessary. However, 3D printing processes exhibit different behaviors of alloy elements owing to rapid thermal dynamics, making it challenging to predict the microstructure and properties. In this study, we gathered published data on the relationship between alloy composition, processing conditions, and properties. Furthermore, we conducted a sensitivity analysis on the effects of the process variables on the density and hardness of aluminum alloys used in additive manufacturing.