论文标题
使用机器学习对电机的多目标优化优化
Multi-Objective Yield Optimization for Electrical Machines using Machine Learning
论文作者
论文摘要
这项工作涉及在考虑制造不确定性的情况下对电机的设计优化。为了有效地量化不确定性，采用了黑框机器学习方法。提出了一个多目标优化问题，同时最大程度地提高了可靠性，即产量和进一步的绩效目标，例如成本。在商业有限元仿真软件中对永久磁铁同步机进行建模和模拟。描述并比较了解决多目标优化问题的四种方法，即：Epsilon-constraint标量，加权总和标量表，多启动加权总和方法和遗传算法。
This work deals with the design optimization of electrical machines under the consideration of manufacturing uncertainties. In order to efficiently quantify the uncertainty, blackbox machine learning methods are employed. A multi-objective optimization problem is formulated, maximizing simultaneously the reliability, i.e., the yield, and further performance objectives, e.g., the costs. A permanent magnet synchronous machine is modeled and simulated in commercial finite element simulation software. Four approaches for solving the multi-objective optimization problem are described and numerically compared, namely: epsilon-constraint scalarization, weighted sum scalarization, a multi-start weighted sum approach and a genetic algorithm.
