There is a strong correlation between the geometry of the weld pool surface and the degree of penetration in arc welding. To measure the geometry of the weld pool surface robustly, many structured light laser line based monitoring systems have been proposed in recent years. The geometry of the specular weld pool could be computed from the reflected laser lines based on different principles. The prerequisite of accurate computation of the weld pool surface is to segment the reflected laser lines robustly and efficiently. To find the most effective segmentation solutions for the images captured with different welding parameters, different image processing algorithms are combined to form eight approaches and these approaches are compared both qualitatively and quantitatively in this paper. In particular, the gradient detection filter, the difference method and the GLCM (grey level co-occurrence matrix) are used to remove the uneven background. The spline fitting enhancement method is used to remove the fuzziness. The slope difference distribution-based threshold selection method is used to segment the laser lines from the background. Both qualitative and quantitative experiments are conducted to evaluate the accuracy and the efficiency of the proposed approaches extensively.

This paper proposes a high-performance indirect control scheme for torque ripple minimization in the switched reluctance motor (SRM) drive system. Firstly, based on the nonlinear torque-angle characteristic of SRM, a novel torque sharing function is developed to obtain the optimal current profiles such that the torque ripple is minimized with reduced copper losses. Secondly, in order to track current accurately and indirectly achieve high-performance torque control, a robust current controller is derived through the Lyapunov stability theory. The proposed robust current controller not only considers the motor parameter modeling errors but also realizes the fixed frequency current control by introducing the pulse width modulation method. Further, a disturbance-observer-based speed controller is derived to regulate the motor speed accurately, and the load torque is considered an unknown disturbance. The simulations and experiments on a 1.5 kW SRM prototype are carried out to demonstrate the effectiveness of the proposed high-performance indirect torque control strategy. Results verify the superiority of the proposed strategy with respect to the torque ripple suppression, system efficiency, and antidisturbance.

With the development of semiconductor switches in recent years, inductive energy storage has shown a good application potential in pulsed-power supplies. In our previous studies, a repetitive inductive pulsed-power supply (IPPS) circuit topology was proposed to generate continuous current pulses. An insulated-gate bipolar transistor (IGBT) is used as the main switch to cut off the charging current in this topology. Limited by the current turning off ability of the main switch, it may encounter difficulties in expanding to higher charging currents. In this article, with the concept of inverse current commutation with semiconductor devices (ICCOS) switch, which is proposed by the German-French Institute of Saint Louis (ISL), an improved repetitive IPPS circuit topology is proposed by using a bridge current commutation unit as ICCOS. The thyristor acts as the main switch in the improved circuit and the charging current is turned off by a precharged capacitor. To turn off the charging current for the next cycle, the capacitor can be self-charged by the leakage flux of the pulse transformer and the back electromotive force from the load. Other than that, when the load current pulse reaches the required width, the residual energy of the system can be recovered by switching the inductor to the charging state. Simulation and comparative study show that the improved circuit is suitable for the generation of repetitive current pulses in high-energy systems.

Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents’ dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.

A hybrid drive wind turbine equipped with a speed regulating differential mechanism can generate electricity at the grid frequency by an electrically excited synchronous generator without requiring fully or partially rated converters. This mechanism has extensively been studied in recent years. To enhance the transient operation performance and low-voltage ride-through capacity of the proposed hybrid drive wind turbine, we aim to synthesize an advanced control scheme for the flexible regulation of synchronous generator excitation based on fractional-order sliding mode theory. Moreover, an extended state observer is constructed to cooperate with the designed controller and jointly compensate for parametric uncertainties and external disturbances. A dedicated simulation model of a 1.5 MW hybrid drive wind turbine is established and verified through an experimental platform. The results show satisfactory model performance with the maximum and average speed errors of 1.67% and 1.05%, respectively. Moreover, comparative case studies are carried out considering parametric uncertainties and different wind conditions and grid faults, by which the superiority of the proposed controller for improving system on-grid operation performance is verified.