<![CDATA[Three-phase induction motors dominate industrial applications due to their robustness, simple construction, and low maintenance requirements; however, torque ripple and efficiency degradation remain critical issues affecting vibration, acoustic noise, mechanical stress, and overall energy consumption. Excessive torque ripple may reduce operational stability and shorten motor lifespan, while low efficiency increases electricity costs in continuous industrial processes. Conventional optimization approaches often address torque ripple or efficiency separately and usually require extensive computational effort with a large number of simulation iterations. This study proposes an integrated optimization framework combining Finite Element Analysis (FEA) and the Taguchi method to simultaneously reduce torque ripple and improve efficiency through systematic stator design modification. Six control factors are evaluated, including slot geometry parameters (hs0, hs2, bs1, bs2), stator core material, and air-gap length, arranged using an L25 orthogonal array to minimize simulation runs while maintaining statistical reliability. Electromagnetic simulations are performed using ANSYS Maxwell to obtain torque characteristics, magnetic behavior, power loss, and efficiency under rated operating conditions. Statistical evaluation through Analysis of Means (ANOM) and Analysis of Variance (ANOVA) identifies dominant parameters and determines the optimal design combination. Results indicate that stator core material contributes most significantly, accounting for 77.89% of torque ripple variation and 69.24% of efficiency variation, followed by slot width parameters and air-gap length. The optimized design reduces torque ripple from 17.27% to 17.13% and increases efficiency from 86.58% to 88.52%, while reducing total power losses by 2.25 kW. In addition, the optimized design provides smoother torque response and more stable operation during startup and steady-state conditions. The proposed approach demonstrates a computationally efficient and statistically robust method for industrial induction motor design, offering practical applicability for improving energy efficiency, reliability, and operational stability.]]>
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