Welding deformation and residual stress have negative influence on assembly accuracy and service performance. Thermal elastic plastic (TEP) and inherent strain finite element analysis (FEA) methods were used to study this challenge. Basic principle of these two methods was first introduced. The influence of welding process, constraints, solid phase transformation and multi-pass welding on deformation and residual stress was discussed, and computation accuracy and efficiency were summarised. Loading method of inherent strain in inherent strain FEA was analysed, interface element was introduced to simulate effects of the gap on deformation in assembly welding especially for large structures. The future work, including accurately multiscale TEP model, efficiently transient prediction method of large structures, and flexible evaluation software, was planned.

Four-wheel redundantly-actuated mobile robot (FRMR) offers high controllability and maneuverability for automation applications. However, the robot dynamics and actuator failures are normally omitted in the existing kinematic control schemes, which may lead to steering vibration and degraded robustness. To deal with these problems, a fault-tolerant dynamic control method is developed for precise trajectory tracking of the FRMR, which utilizes a two-level structure to cover the wheel-ground interaction and possible actuator failures. In the high level, with a novel fractional-order sliding mode control, this method offers an effective way to regulate the steering angle and eliminate the rotation chattering simultaneously. For the redundantly-actuated issue, a robust allocation solution is then presented in the lower level to straightly determine optimal driving torques with full considerations of actuator failures and optimization efficiency. The convergence and stability of the achieved FRMR system are guaranteed theoretically. The experimental comparative results verify that higher tracking precision and enhanced robustness can be obtained using our proposed method.

That a mobile welding robot with an adaptive seam tracking ability can greatly improve welding efficiency and quality has been extensively studied. To further improve automation in multiple-station welding, we developed a novel intelligent mobile welding robot consisting of a four-wheeled mobile platform and a collaborative manipulator. With the support of simultaneous localization and mapping (SLAM) technology, the robot is capable of automatically navigating to different stations to perform welding operations. To automatically detect the welding seam, a composite sensor system including an RGB-D camera and a laser vision sensor is creatively applied. Based on the sensor system, a multi-layer sensing strategy is performed to ensure that the welding seam can be detected and tracked with high precision. By applying a hybrid filter to the RGB-D camera measurement, the initial welding seam could be effectively extracted. Next, a novel welding start point detection method is proposed. Meanwhile, to guarantee the tracking quality, a robust welding seam tracking algorithm based on laser vision sensor is presented, to eliminate the tracking discrepancy caused by the platform parking error, through which the tracking trajectory can be corrected in real-time. The experimental results show that the robot can autonomously detect and track the welding seam effectively at different welding stations. Also, multiple-station welding efficiency can be improved and quality can also be guaranteed.