Energy management system for biological 3D printing based on manifold model morphing and semi-supervised machine learning

Abstract

This paper focuses on addressing a critical challenge in 3D printing: managing energy consumption. 3D printing has emerged as a transformative technology in manufacturing, but its energy demands are a key concern for cost-effectiveness and sustainability. To tackle this challenge, an energy management system is proposed to leverage manifold model morphing and semi-supervised machine learning, to reduce production costs for 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, the semi-supervised machine learning is then incorporated to leverage unlabeled data, enhancing the accuracy and robustness of the energy management system. The proposed system addresses the challenges of limited labeled data and complex morphologies of biological products in layered additive manufacturing. The energy management system is more applicable to soft robotics and biomedical products. The performance of the proposed system is evaluated through extensive experiments that demonstrate its effectiveness and efficacy in predicting and managing energy consumption in 3D printing. Our system offers a practical solution for estimating energy consumption in the design stage, and thus guides the designers to adopt the optimal part geometry and process planning for cleaner production. The results highlight that the proposed system is capable of optimizing energy usage in 3D printing, contributing to the advancement of green and sustainable industrial practices.