This study focused on the efficacy of employing a pulsed fiber laser in the curved cutting of thin, non-oriented electrical steel sheets. Experiments were conducted in paraffinic oil by adjusting the input process parameters, including laser power, pulse frequency, cutting speed, and curvature radius. The multiple output quality metrics included kerf width, inner and outer heat-affected zones, and re-welded portions. Analyses of the Random Forest Method and Response Surface Method indicated that laser pulse frequency was the most important variable affecting the cut quality, followed by laser power, curvature radius, and cutting speed. To improve cut quality, an innovative artificial intelligence (AI) approach incorporating a deep neural network (DNN) model and a modified equilibrium optimizer (M-EO) was proposed. Initially, the DNN model established correlations between input parameters and cut quality aspects, followed by M-EO pinpointing optimal cut qualities. Such an approach successfully identified an optimal set of laser process parameters, even beyond the specified process window from the initial experiments on curved cuts, resulting in significant enhancements confirmed by validation experiments. A comparative analysis showcased the developed models’ superior performance over prior studies. Notably, while the models were initially developed based on the results from curved cuts, they proved adaptable and capable of yielding comparable outcomes for straight cuts as well.

Accurate estimation of the rotor flux angle remains a significant challenge when conventional direct or indirect field-oriented control (FOC) strategies are applied to induction motor drives. This paper proposes a novel method for determining the rotor flux angle under steady-state conditions using only stator voltage and current measurements. An adjustable steady-state detection mechanism is introduced and integrated into a phase-locked loop (PLL)-based indirect field-oriented framework to enable a smooth injection of the actual rotor flux angle into the control system. Both simulation and experimental results validate the effectiveness of the proposed method, demonstrating a significant reduction in stator current compared to conventional FOC approaches under identical load torque conditions.

This paper proposes a sensorless field-oriented control (FOC) strategy for permanent magnet synchronous motors (PMSMs), focusing on rotor flux position estimation based on back-electromotive force (back-EMF) signals. The limitations of conventional phase-locked loop (PLL) techniques for rotor flux position estimation along the motor shaft are analyzed, and an enhanced PLL structure is developed to address these deficiencies.In electric vehicle traction applications, precise flux position estimation alone is insufficient; accurate generation of d–q-axis current commands is equally critical. To address this need, a zero-pole-free PI regulator is designed within the PLL module, enabling more accurate flux estimation. Additionally, a gradient-based self-tuning algorithm is employed to identify system parameters, particularly the stator inductance, enabling the controller to optimize current command generation.Comprehensive system-level simulations have been conducted to validate the effectiveness of the proposed sensorless control scheme. Comparative studies demonstrate that the proposed method significantly improves feasibility and robustness for practical PMSM drive applications.