Towards low-budget energy efficiency design in additive manufacturing based on variational scale-aware transformer

Abstract

In opposite to subtractive manufacturing methodologies, additive manufacturing (AM) embraces plenty of sustainable advantages to decrease energy consumption and material waste. With the explosive data growth in AM, machine learning (ML) attracts widespread attention to enable AM designers to critically estimate energy consumption in the design stage, and thus adopt the optimal part geometry and process planning for cleaner production in AM. However, recent ML methods require massive annotated labels and usually neglect the dimensional variations of products, thereby leading to deficient energy efficiency design performance with a high experimental budget. To address these issues, the Variational Scale-aware Transformer (VST) network is proposed to precisely predict AM energy consumption with modest computational and experimental costs for low-budget energy efficiency design in AM. The task of AM energy consumption prediction is first modeled as a non-linear regression problem, where the sliced layered image (SLI) is generated as the network input in light of the layer-wise manufacturing principle in AM. The proposed VST leverages its novel multi-stage architecture to transfer the learnt comprehensive understanding from a small dimensional scale to a large dimensional scale, adapting to the increasing part dimension and geometry complexity. The multi-scale awareness module (MSAM) is further devised as a four-stage feature hierarchy for generating feature maps in different dimensional scales, efficiently enabling the seamless fusion of the fine-grained, coarse-grained, and local variational scale embeddings. Extensive numerical and physical experimental results demonstrate that the proposed method consistently outperforms the state-of-the-art networks and enhances the energy prediction performance for low-budget energy efficiency design in AM.