The use of 3D printing, or additive manufacturing, has gained significant attention in recent years due to its potential for revolutionizing traditional manufacturing processes. One key challenge in 3D printing is managing energy consumption, as it directly impacts the cost, efficiency, and sustainability of the process. In this paper, we propose an energy management system that leverages the refinement of manifold model morphing in a flexible grasping space, to reduce costs for biological 3D printing. The manifold model is a mathematical representation of the 3D object to be printed, and the refinement process involves optimizing the morphing parameters of the manifold model to achieve desired printing outcomes. To enable flexibility in the grasping space, we incorporate data-driven approaches, such as machine learning and data augmentation techniques, to enhance the accuracy and robustness of the energy management system. Our proposed system addresses the challenges of limited sample data and complex morphologies of manifold models in layered additive manufacturing. Our method is more applicable for soft robotics and biomechanisms. We evaluate the performance of our system through extensive experiments and demonstrate its effectiveness in predicting and managing energy consumption in 3D printing processes. The results highlight the importance of refining manifold model morphing in the flexible grasping space for achieving energy-efficient 3D printing, contributing to the advancement of green and sustainable manufacturing practices. learn more on arXiv