• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.287-292
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• 2022

In this paper, the stochastic distribution of the springback amount is investigated for the stamping process of a U-channel shaped-product with ultra-high strength steel. Using the reliability-based design optimization technique (RBDO), stochastic distribution of process parameters is considered in the analysis including material properties and process variation. Quantification of the springback scatters is carried out with the statistical analysis method according to the material strength. It is found that the scattering amount of springback decreases while the amount of springback increases as the tensile strength of the blank material increases, which is investigated by analyzing the strain and stress distribution of the punch and die shoulder. It is noted that the proposed scheme is capable of predicting and responding to the unavoidable scattering of springback in the sheet metal forming process.

• Design & Manufacturing
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• v.16 no.4
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• pp.1-6
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• 2022

Fine blanking is a press processing technology that can process most of the product thickness into a smooth surface with a single stroke. In this fine blanking process, shear is an essential step. The punches and dies used in the shear are subjected to impacts of tens to hundreds of gravitational accelerations, depending on the type and thickness of the material. Therefore, among the components of the fine blanking mold (dies), punches and dies are the parts with the shortest lifespan. In the actual production site, various types of tool damage occur such as wear of the tool as well as sudden punch breakage. In this study, machine learning algorithms were used to predict these problems in advance. The dataset used in this paper consisted of the signal of the vibration sensor installed in the tool and the measured burr size (tool wear). Various features were extracted so that artificial intelligence can learn effectively from signals. It was trained with 5 features with excellent distinguishing performance, and the SVM algorithm performance was the best among 33 learning models. As a result of the research, the vibration signal at the time of imminent tool replacement was matched with an accuracy of more than 85%. It is expected that the results of this research will solve problems such as tool damage due to accidental punch breakage at the production site, and increase in maintenance costs due to prediction errors in punch exchange cycles due to wear.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.320-326
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• 2017

Multistage deep-drawing technology is used widely in the production of mobile phone battery cases to improve productivity and economy. To ensure adequate capacity and rigidity, such cases are fabricated as a rectangular cup with a high slender ratio. The multistage deep-drawing of a rectangular cup entails a high slender ratio, and the heights of the product sides may be non-uniform because of the complicated deformation mechanisms. This causes problems in product assembly that affects the surface quality of the case. This study examined a blank shape that minimizes the height variations of the product to resolve the aforementioned problems. Optimization design and analysis were performed to identify the shape that yields the least variation. The long and short sides of an oval blank were set as the design variables. The objective function was set to yield the lowest height difference, and the thickness reduction rate of the product was set to the target range. In addition, the height of the final shape was set as a constraint. The height difference was minimized successfully using the optimized design. The design process of the initial blank for all rectangular shapes can be automated in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.302-307
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• 2016

The technology of multistage deep drawing has been widely applied in the metal forming industry, in order to reduce both the manufacturing cost and time. A battery can used for mobile phone production is a well-known example of multistage deep drawing. It is very difficult to manufacture a battery can, however, because of its large thickness to height aspect ratio. Furthermore, the production of the final parts may result in assembly failure due to springback after multistage deep drawing. In industry, empirical methods such as over bending, corner setting and ironing have been used to reduce springback. In this study, a bottoming approach using the finite element method is proposed as a practical and scientific method of reducing springback. Bottoming induces compression stress in the deformed blank at the final stroke of the punch and, thus, has the effect of reducing springback. Different cases of the bottoming process are studied using the finite element program, DYNAFORM, to determine the optimal die design. The results of the springback simulation after bottoming were found to be in good agreement with the experimental results. In conclusion, the proposed bottoming method is expected to be widely used as a practical method of reducing springback in industry.