Neural Networks Applied for Predictive Parameters Analysis of the Refill Friction Stir Spot Welding Process of 6061-T6 Aluminum Alloy Plates

Abstract

Refill friction stir spot welding (RFSSW) technology is a solid-state joint that can replace conventional welding or riveting processes in aerospace applications. The quality of the new welding process is directly influenced by the welding parameters selected. A finite element analysis was performed to understand the complexity of the thermomechanical phenomena during this welding process, validated by controlled experiments. An optimization model using neural networks was developed based on 98 parameter sets resulting from changing 3 welding parameters, namely pin penetration depth, pin rotation speed, and retention time. Ten parameter sets were used to verify the learning results of the optimization model. The 10 results were drawn to correspond to a uniform distribution over the training domain, with the aim of avoiding areas that might have contained distortions. The maximum temperature and normal stress reached at the end of the welding process were considered output data.

Research on the Surface-State Parameterization of a Refill Friction Stir Spot Welding Joint Made of Aluminum Alloy and Its Connection to the Fracture Mode

“…The RFSSW specimens were prepared by an HT-FSW500MT stir friction spot welding machine (Shanghai Aerospace Equipment Manufacturing Plant Aerospace Engineering Equipment (Suzhou) Co., Ltd., Shanghai, China), and the tool system consisted of an 18 mm clamping ring, 9 mm sleeve, and 5 mm pin. The RFSSW process is demonstrated in Figure 2 [11]. The experimental welding parameters selected in this paper are shown in Table 2.…”

“…Santana et al [10] applied the response surface method to establish a regression equation of stirring speed and plunge depth to predict the mechanical properties of joints. Birsan et al [11] utilized Neural Networks based on plunge depth, stirring speed, and residence time to predict the maximum temperature and stress values of joints. However, considering only the influence of welding process parameters, the influence of the objective environment on welding quality is often overlooked, such as tool wear, mechanical assembly errors, and other factors.…”

Microstructure and Mechanical Properties of Refill Friction Stir Spot-Welded Joints of 2A12Al and 7B04Al: Effects of Tool Size and Welding Parameters

“…Refill friction stir spot welding (RFSSW) is a green, energy-saving, environmentally friendly, and efficient spot-welding technology developed based on friction stir welding [3][4][5]. The technology is similar to friction stir welding, in which a solid joint is formed while the workpiece is kept below the melting temperature under the action of frictional heat and mechanical forces [6,7].…”