A digital twin approach for weld penetration prediction of tig welding with dual ellipsoid heat source

Tungsten Inert Gas (TIG) welding is a manufacturing process that utilizes argon as a shielding gas and tungsten as an electrode to join metals at high temperatures. The weld penetration is the key to determine the quality of the weld. However, the lack of sensing technology makes weld penetration difficult to predict, which imposes a major challenge to process stability and weld quality. To address this challenge, a digital twin-driven method is proposed for characterizing the melt pool morphology and melt penetration prediction. To achieve this, an analytical model of the melt pool with time-varying welding speed under the action of a double ellipsoidal circular heat source is first derived. The analytical model is solved using the numerical integration method. The prediction of melt depth and melt width is achieved by extracting isotherms. Meanwhile, a digital reconstruction of the welding scene was achieved by implementing the Neural Radiance Fields (NeRF) method. The target rendering of the melt pool and welding scene is accomplished by constructing voxels and meshes. Furthermore, VR is utilized as the interface for human–computer interaction, and a digital twin model of the molten pool morphology and welding scene is generated. The prediction model's accuracy is verified through welding experiments using 304L steel on a robotic welding system. The results show that in the 0–4 s stage, the penetration error is controlled within 7%. In the stage of 4–16 s when the speed changes, the maximum error of penetration is 16.59%. In terms of welding scene reconstruction quality, PSNR is 33.98 and SSIM reaches 0.9032. The method allows real-life simulation of different welding conditions and parameter combinations prior to welding, assessing their impact on the welding results, in order to find the optimal configuration of process parameters. It can also be remotely realized to monitor and control the melt penetration in real-time during the welding process. This method provides a new solution and a theoretical guidance system to solve the welding penetration control problems and it plays an important role in promoting welding intelligence.