[논문]사출 성형 공정에서의 변수 최적화 방법론

정영진; 강태호; 박정인; 조중연; 홍지수; 강성우

doi:10.7469/jksqm.2024.52.1.43

사출 성형 공정에서의 변수 최적화 방법론
Methodology for Variable Optimization in Injection Molding Process

品質經營學會誌 = Journal of Korean society for quality management, v.52 no.1, 2024년, pp.43 - 56

정영진 (인하대학교 산업경영공학과) , 강태호 (인하대학교 산업경영공학과) , 박정인 (인하대학교 산업경영공학과) , 조중연 (인하대학교 산업경영공학과) , 홍지수 (인하대학교 산업경영공학과) , 강성우 (인하대학교 산업경영공학과)

Abstract ▼ AI-Helper

Purpose: The injection molding process, crucial for plastic shaping, encounters difficulties in sustaining product quality when replacing injection machines. Variations in machine types and outputs between different production lines or factories increase the risk of quality deterioration. In response, the study aims to develop a system that optimally adjusts conditions during the replacement of injection machines linked to molds. Methods: Utilizing a dataset of 12 injection process variables and 52 corresponding sensor variables, a predictive model is crafted using Decision Tree, Random Forest, and XGBoost. Model evaluation is conducted using an 80% training data and a 20% test data split. The dependent variable, classified into five characteristics based on temperature and pressure, guides the prediction model. Bayesian optimization, integrated into the selected model, determines optimal values for process variables during the replacement of injection machines. The iterative convergence of sensor prediction values to the optimum range is visually confirmed, aligning them with the target range. Experimental results validate the proposed approach. Results: Post-experiment analysis indicates the superiority of the XGBoost model across all five characteristics, achieving a combined high performance of 0.81 and a Mean Absolute Error (MAE) of 0.77. The study introduces a method for optimizing initial conditions in the injection process during machine replacement, utilizing Bayesian optimization. This streamlined approach reduces both time and costs, thereby enhancing process efficiency. Conclusion: This research contributes practical insights to the optimization literature, offering valuable guidance for industries seeking streamlined and cost-effective methods for machine replacement in injection molding.

주제어

표/그림 (12)

그림 Figure 1. Principle of Injection Process
그림 Figure 2. Overview of Variable Optimization in Injection Molding Process
그림 Figure 3. Example of Mold’s Pressure Sensor, Temperature Sensor Position
표 Table 1. Independent Variables of Injection Molding Process
표 Table 2. Dependent Variables of Injection Molding Process (Temperature Sensor)
표 Table 3. Dependent Variables of Injection Molding Process (Pressure Sensor)
표 Table 4. Injection Molding Process Data Frame
그림 Figure 4. Comparison Between the Actual Values and Predicted Values of Sensors
표 Table 5. Results of Variables for Predictive Models
표 Table 6. Optimal Range of Dependent Variable 1
표 Table 6. Optimal Range of Dependent Variable 2
표 Table 7. Independent Variable with Bayesian Optimization

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