• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.591-594
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• 2000

This paper describes a research work of developing a computer-aided design of product with bending and piercing for progressive working. An approach to the system for progressive working is based on the knowledge-based rules. Knowledge for the system is formulated from plasticity theories, experimental results and the empirical knowledge of field experts. The system has been written in AutoLISP on the AutoCAD with a personal computer and is composed of four main modules, which are input and shape treatment, flat pattern layout, strip layout and die layout module. Based on knowledge-based rules, the system is designed by considering several factors such as radius and angle of bend, material and thickness of product, complexities of blank geometry and punch profile, bending sequence, and availability of press. Strip layout drawing generated by the piercing processes with punch profiles divided into for external area is simulated in 3-D graphic forms, including bending sequences for the product with piercing and bending. Results obtained using the modules enable the manufacturer for progressive working of electronic products to be more efficient in this field.

• Design & Manufacturing
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• 제16권4호
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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.