Robust design optimization of electrical machines and drive systems for high quality mass production

2016 ◽  
Author(s):  
Gang Lei ◽  
Jianguo Zhu ◽  
Chengcheng Liu ◽  
Bo Ma
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Mass Production ◽  
Electrical Machines ◽  
Robust Design Optimization ◽  
High Quality ◽  
Drive Systems

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

2020 ◽  
Vol 35 (4) ◽  
pp. 1815-1824 ◽  
Author(s):  
Bo Ma ◽  
Jing Zheng ◽  
Jianguo Zhu ◽  
Jinglai Wu ◽  
Gang Lei ◽  
...  
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Electrical Machines ◽  
Robust Design Optimization ◽  
Interval Uncertainties

scholarly journals Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

2020 ◽  
Vol 10 (19) ◽  
pp. 6653 ◽  
Author(s):  
Tamás Orosz ◽  
Anton Rassõlkin ◽  
Ants Kallaste ◽  
Pedro Arsénio ◽  
David Pánek ◽  
...  
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Emerging Technologies ◽  
Optimization Problems ◽  
Parameter Tuning ◽  
Optimization Techniques ◽  
Electrical Machines ◽  
Robust Design Optimization ◽  
Open Problems ◽  
Electrical Machinery

The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.

Robust Design Optimization for Crashworthiness of Vehicle Side Impact

2017 ◽  
Vol 3 (3) ◽  
Author(s):  
Souvik Chakraborty ◽  
Tanmoy Chatterjee ◽  
Rajib Chowdhury ◽  
Sondipon Adhikari
Keyword(s):  
Design Optimization ◽  
New Product Development ◽  
Robust Design ◽  
Automobile Industry ◽  
Human Error ◽  
Differential Evolution Algorithm ◽  
Side Impact ◽  
Robust Design Optimization ◽  
Parameter Uncertainties ◽  
Design Engineers

Optimization for crashworthiness is of vast importance in automobile industry. Recent advancement in computational prowess has enabled researchers and design engineers to address vehicle crashworthiness, resulting in reduction of cost and time for new product development. However, a deterministic optimum design often resides at the boundary of failure domain, leaving little or no room for modeling imperfections, parameter uncertainties, and/or human error. In this study, an operational model-based robust design optimization (RDO) scheme has been developed for designing crashworthiness of vehicle against side impact. Within this framework, differential evolution algorithm (DEA) has been coupled with polynomial correlated function expansion (PCFE). An adaptive framework for determining the optimum basis order in PCFE has also been presented. It is argued that the coupled DEA–PCFE is more efficient and accurate, as compared to conventional techniques. For RDO of vehicle against side impact, minimization of the weight and lower rib deflection of the vehicle are considered to be the primary design objectives. Case studies by providing various emphases on the two objectives have also been performed. For all the cases, DEA–PCFE is found to yield highly accurate results.

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