提出了一种基于模糊逻辑的5系铝与纯铜搅拌摩擦焊接力学性能建模算法

M. R. Maraki, H. Tagimalek, B. Pasoodeh
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引用次数: 1

摘要

铜/铝复合材料由于其重量轻,导热性和导电性最佳而非常重要和实用。高重量电阻比,连同其固有的性能,使其具有吸引力的新应用。在这方面,高机械性能复合材料的使用量显著增加。在本研究中,对5000系铝和纯铜试样进行了一、二、三、四道次的搅拌摩擦焊接(FSW),并将焊接试样的力学性能和冶金性能与原始试样进行了比较。为了进一步研究拉伸试验的结果,进行了金相和显微硬度试验。焊接试样的显微组织评价表明,试样的混合区是由铝层和铜层结合确定的。结果表明,与原型相比,焊接区的屈服强度有所提高,焊接区的硬度和极限强度也有所提高。与拉伸试样相比,距熔核焊缝距离越大,其力学性能和显微硬度的改善越小。通过改变参数和提高入口温度,使颗粒的混合和均匀分散更加合理,最终提高了抗拉强度。最后,在研究中,考虑到所提出的模型分别在R-Sq (pred)和R-Sq (adj)两个指标(96和99%)中得到,采用模糊逻辑方法对实验数据进行建模。实验数据与模型数据的对比表明,实验数据的误差是可以接受的。
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Provide a Modeling Algorithm for Mechanical Properties of Friction Stir Welding of 5 Series Aluminum and Pure-Copper Based on Fuzzy Logic
The copper/aluminum composite is very important and practical due to its light weight, optimal thermal and electrical conductivity. The high weight resistance ratio, along with its inherent properties, makes it attractive for new applications. In this regard, the use of composites with high mechanical properties has significantly increased. In this research, 5000 series aluminum and pure copper samples in 1st, 2nd, 3rd, and 4th passes have been subjected to friction stir welding (FSW) and then the mechanical and metallurgical properties of the welded samples have been compared with the original samples. In order to further study the results of tensile tests, metallography and microhardness tests have been performed. Microstructural evaluation of welded samples showed that the mixing zone of the samples was determined by combining aluminum and copper layers. The results showed an increase in yield strength in the welding zone and ultimately an improvement in hardness and ultimate strength in the weld zone compared to the prototype. Compared to stretched samples, the greater the distance from the nugget weld, the less the improvement in mechanical properties and microhardness. By changing the parameters and increasing the inlet temperature, the mixing and uniform dispersion of the particles is performed more appropriately and ultimately increases the tensile strength. Finally, in the research, experimental data were modeled using fuzzy logic method and considering that the presented model was obtained in two indices R-Sq (pred) and R-Sq (adj), 96 and 99%, respectively. The comparison between the experimental data and the model data indicated an acceptable error in the experimental data .
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