A Capability‐Driven Sequential Optimization Framework for Electrical Machines Under Manufacturing Uncertainty.
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| Title: | A Capability‐Driven Sequential Optimization Framework for Electrical Machines Under Manufacturing Uncertainty. |
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| Authors: | Abunike, Chiweta E.1,2 (AUTHOR), Okoro, Ogbonnaya I.2 (AUTHOR), Aphale, Sumeet S.1,3 (AUTHOR) s.aphale@abdn.ac.uk, Mishra, Pramita (AUTHOR) pmishra@wiley.com |
| Source: | International Transactions on Electrical Energy Systems. 9/1/2026, Vol. 2026, p1-19. 19p. |
| Subject Terms: | *Surrogate-based optimization, *Process capability, *Multi-objective optimization, *Fault tolerance (Engineering), *Electric machinery, *Prediction models |
| Abstract: | This paper presents a capability‐driven sequential optimization framework for the robust design of electrical machines under manufacturing uncertainty. The proposed methodology integrates surrogate modeling, NSGA‐II‐based Pareto front exploration, and Design for Six Sigma (DFSS)‐driven refinement to ensure production‐level reliability. Surrogate models constructed via Response Surface Methodology (RSM) enable efficient trade‐off analysis across key performance metrics, including torque ripple, average torque, efficiency, and power factor. The first optimization stage applies NSGA‐II to identify Pareto‐optimal trade‐offs, while statistical analysis reveals residual variability risks in critical objectives. To address this, the second stage introduces DFSS‐based capability constraints within a sequential refinement process, driving capability indices (Cpk) above 2.0 and reducing probability of failure (PoF) to below 0.01% across all objectives. Compared to baseline designs, the optimized solutions demonstrate significant reductions in variability: 36% in torque ripple, 40% in power factor, and 27% in torque spread. The proposed framework is validated as an effective and scalable approach for robust optimization of electrical machines subjected to real‐world manufacturing tolerances, enabling production‐ready solutions with high statistical confidence. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | This paper presents a capability‐driven sequential optimization framework for the robust design of electrical machines under manufacturing uncertainty. The proposed methodology integrates surrogate modeling, NSGA‐II‐based Pareto front exploration, and Design for Six Sigma (DFSS)‐driven refinement to ensure production‐level reliability. Surrogate models constructed via Response Surface Methodology (RSM) enable efficient trade‐off analysis across key performance metrics, including torque ripple, average torque, efficiency, and power factor. The first optimization stage applies NSGA‐II to identify Pareto‐optimal trade‐offs, while statistical analysis reveals residual variability risks in critical objectives. To address this, the second stage introduces DFSS‐based capability constraints within a sequential refinement process, driving capability indices (Cpk) above 2.0 and reducing probability of failure (PoF) to below 0.01% across all objectives. Compared to baseline designs, the optimized solutions demonstrate significant reductions in variability: 36% in torque ripple, 40% in power factor, and 27% in torque spread. The proposed framework is validated as an effective and scalable approach for robust optimization of electrical machines subjected to real‐world manufacturing tolerances, enabling production‐ready solutions with high statistical confidence. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20507038 |
| DOI: | 10.1155/etep/2489065 |
