论文部分内容阅读
This paper presents an experimental design approach to process parameter optimization for the laser lap welding of SUS301L austenitic stainless steels using Nd:YAG laser in order to reduce the welding deformations of the back of the welding seams while ensuring mechanical properties of welding joints.To determine the optimal laser-welding parameters,a set of mathematical models were developed relating welding parameters to each of the weld characteristics.These were validated both statistically and experimentally.In order to reduce experimental error and the number of specimens,the part of orthogonal experiments were used in this study.The quality criteria set for the weld to determine optimal parameters were the maximization of weld width and the minimization of weld depth.Laser power,welding speed,the laser irradiation angle,the focused distance and shield gas of 3.5kW,7.0m/min,70°,0mm and N2 with the flow of 30L/min,respectively,with a fiber diameter of 600μm were identified as the optimal set of process parameters.
This paper presents an experimental design approach to process parameter optimization for laser lap welding of SUS301L austenitic stainless steels using Nd: YAG laser in order to reduce the welding deformations of the back of the welding seams while ensuring mechanical properties of welding joints. the optimal laser-welding parameters, a set of mathematical models were developed relating welding parameters to each of the welding characteristics. These were validated both statistically and.In a to experimental to reduce experimental error and the number of specimens, the part of orthogonal experiments were used in this study. the quality criteria set for the weld to determine optimal parameters were the maximization of weld width and the minimization of weld depth. Laser power, welding speed, the laser irradiation angle, the focused distance and shield gas of 3.5 kW, 7.0 m / min, 70 °, 0 mm and N2 with the flow of 30 L / min, respectively, with a fiber diameter of 600 μm were identified as th e optimal set of process parameters.