Machine learning accelerated MMC-based topology optimization for sound quality enhancement of serialized acoustic structures

A key requirement for product design is the ability to capture the physical features of structures quickly and efficiently. One way to achieve this purpose is to use machine learning to support topology optimization. However, due to the diversity of application requirements in product updates, the machine learning-based optimization model needs to be frequently rebuilt by collecting large amounts of data. In this work, Moving Morphable Component approach is combined with Deep Neural Network to achieve the quick topology optimization design of acoustic devices. A deep transfer learning approach to the predictive model is also developed for the model to be adapted to different design conditions. Thus, using the proposed approach, not only the originally designed acoustic structure can be quickly optimized, but also the serialized structures evolved from the source model can be easily obtained. Numerical examples illustrate the effectiveness and advantages of the proposed method.