Reliability-Based Optimum Tolerance Design for Industrial Electromagnetic Devices

Cho, Su-Gil; Jang, Junyong; Kim, Kyu-Seob; Hong, Jung-Pyo; Jang, Woo-Kyo; Lee, Tae Hee

doi:10.1109/tmag.2013.2286398

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…The optimal design obtained without robustness assessment may have a risk of high performance-diversity and faults [1,2]. A robust design should be insensitive to the unavoidable tolerances and satisfy the reliability requirements [3,4]. As an example, Fig.…”

Section: Introductionmentioning

confidence: 99%

Robust Design Optimization of Electrical Machines Considering Hybrid Random and Interval Uncertainties

Zheng

Zhu

et al. 2020

IEEE Trans. Energy Convers.

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In robust design optimization (RDO) of electrical machines, the cases with random uncertainty and interval uncertainty are generally investigated separately. The uncertainty quantification is based on the random method and interval method, respectively. For problems with hybrid uncertainties, the uncertainty analysis methods for a single type of uncertainties may no longer be applicable as both the random and interval methods are required for the uncertainty qualification. This poses considerable challenges to the hybrid uncertainty modeling, numerical calculation, and design optimization. This paper proposes an efficient robust optimizer based on the evolutionary algorithms and the polynomial chaos Chebyshev interval (PCCI) method for RDO of electrical machines with hybrid uncertainties. With the potential candidate filtering in the population of each iteration and effective robustness assessment by the PCCI method, the optimization can be conducted efficiently. A design example of a brushless DC motor considering hybrid uncertainties is analyzed and optimized. The results confirm the feasibility of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Robust Design Optimization of Electrical Machines Considering Hybrid Random and Interval Uncertainties

Zheng

Zhu

et al. 2020

IEEE Trans. Energy Convers.

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“…[23][24][25] The cost-tolerance model and quality loss function 26 are typically combined, then tolerance design is optimized. 27 The dimensional tolerance is related to the manufacturing process and directly determines the manufacturing cost. 28 Therefore, it is important that the tolerance allocation should be reasonable, thus reducing the manufacturing cost.…”

Section: Introductionmentioning

confidence: 99%

“…23 – 25 The cost-tolerance model and quality loss function 26 are typically combined, then tolerance design is optimized. 27 …”

Section: Introductionmentioning

confidence: 99%

Tolerance analysis for automobile transmission shaft based on minimum cost and reliability target

et al. 2020

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To design a reasonable dimensional tolerance of a transmission shaft, the higher product quality while lower manufacturing cost must be considered. This paper provides a mathematical model and a flowchart which elucidates the relationship between process capability index (PCI), reliability, tolerance and manufacturing cost, considering the characteristics of the shaft diameter, the material and manufacturing process. A 10.904% cost reduction under certain PCI range of a real case shows the effectiveness of the model and flowchart, thus it can be applied to those technical area when optimizing product design.

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“…A few groups have presented studies on how cogging torque is influenced by the manufacturing tolerance, and how robust design methods can be used to address this problem [11–18]. Cho et al [19] presented reliability‐based optimum tolerance design using the Akaike information criterion (AIC) method to guarantee the high reliability of electromagnetic devices while maximising manufacturing tolerances. A few groups have studied motor drive performance in light of the manufacturing imperfections in the motor [20, 21].…”

Section: Introductionmentioning

confidence: 99%

Process capability control procedure for electrical machines by using a six‐sigma process for achieving six‐sigma quality level

Jun

Kwon

2017

IET Electric Power Applications

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An overall six‐sigma process (SSP) that includes a novel process capability control (PCC) procedure is presented for satisfying a six‐sigma level for a Z‐value as well as a mean value of target performance. An example of this process is present for the efficiency and torque ripple in a spoke‐type permanent magnet motor considering the manufacturing tolerances of five rotor dimensions. In this context, six‐sigma means that the probability of failure of the product or system is 0.00034%. A novel SSP with a PCC procedure is suggested for designing electrical machines. In this procedure, three possible PCC methods were determined based on the definition of the Z‐value. Next, each method was carried out to achieve the target Z‐value and to illustrate the advantages and possible issues associated with each method. Finally, the authors showed that the suggested PCC procedure effectively achieves the target Z‐value of the motor and can be widely used for the design of electrical machines.

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Reliability-Based Optimum Tolerance Design for Industrial Electromagnetic Devices

Cited by 11 publications

References 8 publications

Robust Design Optimization of Electrical Machines Considering Hybrid Random and Interval Uncertainties

Robust Design Optimization of Electrical Machines Considering Hybrid Random and Interval Uncertainties

Tolerance analysis for automobile transmission shaft based on minimum cost and reliability target

Process capability control procedure for electrical machines by using a six‐sigma process for achieving six‐sigma quality level

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