Optimization of the Welding Parameters of High-Quality Aluminum/Copper FSSW Joints Using Taguchi Method Combined with Back Propagation Neural Network and Genetic Algorithm
Source abstract
Abstract
Abstract Due to the different superior properties of lightweight and high-strength aluminum and high-conductivity copper metals, the joining of the two is very common and important in today’s industrial applications. Generally, there is no formula to follow for the setting of welding parameters, and the setting is completely based on the past knowledge and experience of experts. Once the range of expert experience is exceeded, the optimal parameters cannot be effectively set, which may easily lead to poor welding quality. This research aims to develop an economical and effective Taguchi experimental design method for achieving the highest shear strength value for aluminum/copper friction stir spot welded joints. Three independent welding process variables were considered including the pin rotation speed, dwell time, and downward pressure. Different optimization techniques such as Taguchi, TOPSIS, artificial neural network, genetic algorithm, and their combinations were utilized for obtaining the best ranges of input welding parameters to achieve the maximum shear strength values. The optimal combination of process parameters was found at the rotation speed of 1800 r/min, the dwell time of 15 s, and the downward pressure of 0.2 mm. The results showed that the integration of the TOPSIS method, neural network, and genetic algorithm provides the best combination of parameter values for the verification of shear strength experiments. According to the performed analyses, the degree of influence of the independent variables on the shear strength of bi-material joints can be ranked as: dwell time > pin rotation speed > downward pressure.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Welding Techniques Analysis · Welding Techniques and Residual Stresses · Electronic Packaging and Soldering Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
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