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Hybrid SPR 접합을 적용한 이종소재 인장전단에 관한 연구
유관종(Kwan-jong Yu),최두복(Du-bok Choi),김재열(Jae-yeol Kim) 한국기계가공학회 2020 한국기계가공학회지 Vol.19 No.9
Self-piercing rivets are often used in the automotive industry, among other industries, as mechanical components to join multiple materials such as aluminum alloys. Self-piercing rivets have a strong sealing property, although there is considerable scope for their performance improvement. In this study, to enhance the performance of self-piercing rivets, the hybrid self-piercing riveting (SPR) technique, using the existing SPR and structural adhesive, was proposed. Moreover, heterogeneous material specimens subjected to the hybrid SPR technique were manufactured and tested. The joint strength of the test pieces of different materials was evaluated through finite element analyses.
김일수(Ill-Soo Kim),유관종(Kwan-Jong Yu),서주환(Joo-Hwan Se),손준식(Joon-Sik Son),김학형(Hak-Hyoung Kim) 한국생산제조학회 2006 한국공작기계학회 춘계학술대회논문집 Vol.2006 No.-
The GMA welding process involves large number of interdependent variables which may affect product quality, productivity and cost effectiveness. The relationship between process parameters for a fillet welding and bead geometry is complex because a number of process parameters are involved. To make the automated GMA welding, it is essential that a mathematical model that predicts bead geometry and accomplishes the desired mechanical properties of the weldment is developed. In this paper, an attempt has been made to develop a neural network model and two regression equations(linear and curvilinear) to predict a bead height on fillet welding and to compare the neural network model and the two regression equations in order to select an optimal prediction model.
손준식(Joon-Sik Son),김일수(Ill-Soo Kim),유관종(Kwan-Jong Yu),전광석(Kwang-Suk Chon) 대한기계학회 2006 대한기계학회 춘추학술대회 Vol.2006 No.9
The GMA welding process involves large number of interdependent variables which may affect product quality, productivity and cost effectiveness. The relationship between process parameters for a fillet welding and bead geometry is complex because a number of process parameters are involved. To make the automated GMA welding, it is essential that a mathematical model that predicts bead geometry and accomplishes the desired mechanical properties of the weldment is developed. In this paper, an attempt has been made to develop a neural network model and two regression equations (linear and curvilinear) to predict a bead width on fillet welding and to compare the neural network model and the two regression equations in order to select an optimal prediction model.