Optimum Design of Tubular Permanent-Magnet Motors for Thrust Characteristics Improvement by Combined Taguchi-Neural Network Approach

Although tubular permanent-magnet motors have advantages such as remarkable force capability and high efficiency due to lack of end winding, they suffer from high thrust force ripple. This paper presents the use of Taguchi method and artificial neural network (ANN) for shape optimization of axially magnetized tubular linear permanent-magnet (TLPM) motors. A multiobjective design optimization is presented to improve force ripple, developed thrust, and permanent-magnet volume simultaneously. The iron pole-piece slotting technique is used and its design parameters are optimized to minimize the motor's force pulsation. To obtain optimal configuration using this technique, four design variables are selected and their approximate optimum values are determined by the Taguchi method using analysis of means (ANOM). In the next step, two more influential parameters are selected by analysis of variance (ANOVA) and their accurate optimum values are obtained by a trained ANN. Finite-element analysis (FEA) is used to appraise the performance of the motor in different experiments of the Taguchi method and for training the ANN. The results show that force pulsation of the optimized motor is greatly reduced while there is small drop in the motor thrust.