Investigation of the Process Parameters in Rotary Friction Welded Dissimilar AA7075/AA5083 Aluminum Alloy Joints on Fatigue Initiation using FEA and ANN

Abstract

The rotary friction welding (RFW) method is one of the most widespread methods in the world for producing bimetallic components that require high mechanical strength. Simulations play a vital role in improving energy efficiency and reducing environmental impact, aligning with the sustainability goals of modern industry. A neural network (NN)-based incremental learning system was developed to predict crack growth and fatigue for AA5083 and AA7075 aluminum alloys. The results indicate the ability of this method to accommodate the input temperatures and the S-N curve and provide reliable predictions of expected fatigue. This method can reduce labor costs and time spent on crack propagation tests, enhancing the effectiveness of production processes and reducing process costs. This work also reveals the ability of neural . It maynetworks (NN) in monotonic function extrapolation like the S-N curve, which may pave the way for a wide variety of monotonic function-predicting problems. In future studies, a neural network (NN)-based increment learning scheme could be trained with random parts of individual S–N curves and applied to predict the rest. Additionally, the verification utilizing AISI 2205 and AISI 1020 steel has observed that neural networks may obtain S-N curve values for another metal with less than an 8% error rate. Friction pressure increases temperature, deformation, and stress in welding processes. Friction pressure 17 MPa increases temperature to 355 degrees Celsius, while Friction pressure 23 MPa increases deformation to 0.020 mm. A friction pressure of 29 MPa increases equivalent stress to 110 MPa. The indication of the S-N curve shows that increasing welding pressure increases Alternating Stress. Friction pressure also increases life, with minimum life cycles reaching 171040 cycles at 17 MPa, 195560 cycles at 23 MPa, and 283690 cycles at 29 MPa. Comparing research and simulation results, convergence is less than 8%, reducing error.