Abstract

In manufacturing firms, welding technology plays a supreme role in joining purposes. The joining of dissimilar metals is complex in several conventional type welding. Friction stir spot welding (FSSW) is a solid-state welding technique and it is a derivative of conventional friction stir welding (FSW) to join the metals. The recent demands of advanced engineering materials such as aluminum alloys and stainless steel in the automobile sectors are forced to facilitate this research work, at the same time, the joining of such materials is quite complex because of its different welding characteristics. The main contribution of this work is to optimize and predict the microhardness and tensile failure properties of dissimilar metals by hybrid lightning search algorithm-simplex method (LSA-SM) and ANN algorithm. The dissimilarly joined metals namely, duplex stainless steel 32760 and aluminum 7075-T6 are utilized in this work and the experimentation is performed for six different test runs. Besides, the predicted values from the proposed algorithm are compared with the existing support vector machine (SVM) method. The predicted values of 314 Hv microhardness and 4967 N tensile shear failure load are optimized by the ANN-LSA-SM method at 1900 rpm. From the test results, it shows that the proposed model is efficient to predict the mechanical properties of the dissimilar welded materials than the experimental and SVM method.

摘要

在制造企业中，焊接技术在连接方面起着至高无上的作用。在几种传统类型的焊接中，异种金属的连接很复杂。搅拌摩擦点焊 (FSSW) 是一种固态焊接技术，它是传统的搅拌摩擦焊 (FSW) 的衍生产品，用于连接金属。汽车领域对铝合金和不锈钢等先进工程材料的需求迫使这项研究工作更加容易，同时由于其不同的焊接特性，这些材料的连接相当复杂。这项工作的主要贡献是通过混合闪电搜索算法-单纯形法（LSA-SM）和人工神经网络算法来优化和预测异种金属的显微硬度和拉伸失效特性。不同连接的金属即，在这项工作中使用了双相不锈钢 32760 和铝 7075-T6，并针对六种不同的测试运行进行了实验。此外，将所提出算法的预测值与现有的支持向量机（SVM）方法进行比较。314 Hv 显微硬度和 4967 N 拉伸剪切破坏载荷的预测值通过 ANN-LSA-SM 方法在 1900 rpm 下进行优化。试验结果表明，所提出的模型在预测异种焊接材料的力学性能方面比实验法和支持向量机法更有效。314 Hv 显微硬度和 4967 N 拉伸剪切破坏载荷的预测值通过 ANN-LSA-SM 方法在 1900 rpm 下进行优化。试验结果表明，所提出的模型在预测异种焊接材料的力学性能方面比实验法和支持向量机法更有效。314 Hv 显微硬度和 4967 N 拉伸剪切破坏载荷的预测值通过 ANN-LSA-SM 方法在 1900 rpm 下进行优化。试验结果表明，所提出的模型在预测异种焊接材料的力学性能方面比实验法和支持向量机法更有效。