The Optimal Parameters Design of Multiple Quality Characteristics for Welding of Copper and Aluminium Heterogeneous Metals

• Main Authors: LIN,JHIH-SIN, 林志昕
• Other Authors: JHANG, JHY-PING
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/3yyh3s

碩士 === 華梵大學 === 工業工程與經營資訊學系碩士班 === 106 === Copper and aluminum are both inexpensive materials that feature outstanding ductility and electric and thermal conductivity, making them indispensable materials in electronics and manufacturing industries. Considering the need for electric conductivity, the welding of both materials becomes very important. Argon welding is a convenient and highly accessible joining technique. The difference in melting point is approximately 400℃ between copper and aluminum and it often leads to brittle compound, making it even more challenging for welding. The industrial society frequently relies on the experience of seasoned welders but that often results in poor welding quality and the experience of these master welders is hard to pass on, not to mention multiple quality characteristics. For this, this study is an attempt to develop an analysis process to solve the multiple quality characteristics in the heterogeneous welding between copper and aluminum alloy for greater welding strength. Al alloy 5052 and copper alloy C1100 were selected for this study. The Taguchi method was employed to develop an arc welding experiment using inert gas and tungsten electrodes. Non-destructive test (for thickness and width of the joints and Rockwell hardness) and destructive test (tension test) were selected through literature review and discussion with experts to determine the quality of welding. The controllable factors for soldering were welding current and welding rate. The fixed factors were the distance between tungsten bar and plate, elongation of tungsten bar and the flow rate of masking gas and the noise factor is different intermediate layers. Two experiments were repeated using the L9 (34) orthogonal array. The measurements were analyzed using S/N ratio analysis, TOPSIS analysis, back propagation neural network and genetic algorithm to identify the best-fit parameter combination for the welding of copper and aluminum alloys.