• Design & Manufacturing
• /
• 제18권2호
• /
• pp.64-73
• /
• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.

• 소성∙가공
• /
• 제30권6호
• /
• pp.291-300
• /
• 2021

Currently, starting with electric vehicles, the application of ultra-high-strength steel sheets and light metals has expanded to improve mileage by reducing vehicle weight. At a time when internal combustion engine vehicles are rapidly changing to electric vehicles, the application of ultra-high-strength steel is expanding to satisfy both weight reductions and the performance safety of the chassis parts. There is an urgent need to improve the quality of parts without defects. It is particularly difficult to estimate the part formability through the finite element method (FEM) in the burring operation, so product design has been based on the hole expansion ratio (HER) and experience. In this study, design of experiment (DOE), analysis of variance (ANOVA), and regression analysis were combined to optimize the formability by adjusting the process variables affecting the burring formability of ultra-high-strength steel parts. The optimal variables were derived by analyzing the influence of variables and the correlation between the variables through FE analysis. Finally, the optimized process parameters were verified by comparing experiment with simulation. As for the main influence of each process variable, the initial hole diameter of the piercing process and the shape height of the preforming process had the greatest effects on burring formability, while the effect of a lower round of punching in the burring process was the least. Moreover, as the diameter of the initial hole increased, the thickness reduction rate in the burring part decreased, and the final burring height increased as the shape height during preforming increased.