Reliability-based robust design optimization for torque ripple reduction considering manufacturing uncertainty of interior permanent magnet synchronous motor

Jang, Gyeong Uk; Kim, Chang-Wan; Bae, Dae-Sung; Cho, Younghoon; Lee, Jeong-Jong; Cho, Seunghyeon

doi:10.1007/s12206-020-0223-3

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…The computation of robustness metrics such as expectations or variances requires a large sample of the uncertain input variables and thus a large number of simulations. To limit this high computational cost, especially with the use of finite element simulations, meta-modeling techniques coupled with the design of experiments are used to replace the costly simulations with predictions using the resulting surrogate models [14].…”

Section: Introductionmentioning

confidence: 99%

Study on the impact of uncertain design parameters on the performances of a permanent magnet-assisted synchronous reluctance motor

Reyes,

Nasr,

Sinoquet

et al. 2024

STET

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In this paper, deterministic and robust design optimizations of a permanent magnet assisted synchronous reluctance motor were performed to study the impact of different uncertain input parameters on the design. These optimizations were carried out using a surrogate model based on 2-D finite element simulations. Different robust optimizations considering geometric and magnetic uncertain parameters were compared to the deterministic optimization. It was noticed that both geometrical and magnetic properties tolerances greatly impact the machines’ mean torque and torque ripple, where the magnetic properties tolerances had a more significant impact on the mean torque. In such a case, robust optimization is essential to find optimal and robust electric motors designs.

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Section: Introductionmentioning

confidence: 99%

Study on the impact of uncertain design parameters on the performances of a permanent magnet-assisted synchronous reluctance motor

Reyes,

Nasr,

Sinoquet

et al. 2024

STET

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Section: Introductionmentioning

confidence: 99%

“…Therefore, in order to improve the reliability of mechanical products and reduce the influence of external disturbance factors, the reliability-robust design of mechanical products is carried out by combining finite element analysis and reliability-robust design theories. At present, reliability-robust design has been effectively applied in many fields (Jang et al, 2020;Hu et al, 2017;Fran et al, 2020). Wang introduced reliability-robust design theory into the optimal design of axle box springs for rail vehicles, which improved the reliability and robustness of the springs while reducing the total mass of the springs (Wang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Robust optimization design method for structural reliability based on active-learning MPA-BP neural network

Dong

Sheng

Zhao

et al. 2023

IJSI

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PurposeMechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.Design/methodology/approachThe MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.FindingsThe prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.Originality/valueThe MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

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“…Jang et al performed RBRDO of IPMSM considering manufacturing and material uncertainty. They proposed a design to reduce torque ripple and the failure rate of output torque, and they explained the superiority of RBRDO by comparing the results of RBRDO, DDO, and RBDO [24]. The above studies tried to reduce torque ripple or cogging torque through EM design changes to improve vibration performance.…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based Robust Design Optimization for Maximizing the Output Torque of Brushless Direct Current (BLDC) Motors Considering Manufacturing Uncertainty

et al. 2022

Self Cite

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In recent years, the deterministic design optimization method has been widely used to improve the output performance of brushless direct current (BLDC) motors. However, it does not contribute to reducing the failure rate and performance variation of products because it cannot determine the manufacturing uncertainty. In this study, we proposed reliability-based robust design optimization to improve the output torque of a BLDC motor while reducing the failure rate and performance variation. We calculated the output torque and vibration response of the BLDC motor using the electromagnetic–structural coupled analysis. We selected the tooth thickness, slot opening width, slot radius, slot depth, tooth width, magnet thickness, and magnet length as the design variables related to the shape of the stator and rotor that affect the output torque. We considered the distribution of design variables with manufacturing tolerances. We performed a reliability analysis of the BLDC motor considering the distribution of design variables with manufacturing tolerances. Using the reliability analysis results, we performed reliability-based robust design optimization (RBRDO) to maximize the output torque; consequently, the output torque increased by 8.8% compared to the initial BLDC motor, the standard deviation in output performance decreased by 46.9% with improved robustness, and the failure rate decreased by 99.2% with enhanced reliability. The proposed reliability-based robust design optimization is considered to be useful in the actual product design field because it can evaluate both the reliability and robustness of the product and improve its performance in the design stage.

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Reliability-based robust design optimization for torque ripple reduction considering manufacturing uncertainty of interior permanent magnet synchronous motor

Cited by 10 publications

References 12 publications

Study on the impact of uncertain design parameters on the performances of a permanent magnet-assisted synchronous reluctance motor

Study on the impact of uncertain design parameters on the performances of a permanent magnet-assisted synchronous reluctance motor

Robust optimization design method for structural reliability based on active-learning MPA-BP neural network

Reliability-Based Robust Design Optimization for Maximizing the Output Torque of Brushless Direct Current (BLDC) Motors Considering Manufacturing Uncertainty

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