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.

翻译：近年来，3D打印（增材制造）因具备革新传统制造流程的潜力而备受关注。其关键挑战之一在于能量管理——直接影响打印成本、效率及可持续性。本文提出一种基于柔性抓取空间中流形模型形态优化（refinement of manifold model morphing）的能量管理系统，旨在降低生物3D打印成本。流形模型是对待打印三维物体的数学表征，优化过程通过调整该模型的形态参数以实现理想的打印效果。为赋予抓取空间灵活性，我们引入数据驱动方法，如机器学习与数据增强技术，提升能量管理系统的精度与鲁棒性。所提系统有效解决了层状增材制造中样本数据有限及流形模型形态复杂等难题，更适用于软体机器人与生物力学机构。通过大量实验评估，我们验证了该系统在预测与管理3D打印过程能量消耗方面的有效性。结果表明，在柔性抓取空间中优化流形模型形态对实现节能型3D打印具有重要意义，为绿色可持续制造技术的发展提供了支撑。