In the 3D printing process, various error factors can affect the accuracy of the final printing quality. However, current 3D printing error compensation methods have limited effects and usually cannot work in real-time. The 3D printing error compensation process is modeled as a Markov decision process (MDP) in this paper, and Deep Reinforcement Learning (DRL) is applied for dynamic error compensation. This method learns autonomously through trial and error by interacting with the printing environment, which makes it adaptable to various types of 3D printers without specific training. Then, we simulate the digital light processing (DLP) 3D printing. Due to the huge state and action space of sliced images, applying the DRL algorithm to DLP is challenging. We propose an error compensation method based on morphological image operation and use Autoencoder to extract error features to reduce the state space. We then implement our method using a Twin Delayed Deep Deterministic policy gradient algorithm (TD3). The results demonstrate the effects of our method in compensating 3D printing errors.

生物陶瓷因其生物相容性和力学稳定性而广泛应用于生物医学领域,但目前用于制造生物陶瓷产品的传统工艺较为耗时且成本昂贵.作为一种增材制造技术,三维(3D)打印更适合制备复杂形体.同时由于所需植入物形状各异,3D打印柔性化的特点为生物陶瓷植入物的制备提供了个性化定制的可能.综述陶瓷3D打印的材料、工艺及特点,阐述陶瓷3D打印研究现状及其在医疗领域的应用,阐述限制陶瓷3D打印技术发展与医疗领域中应用的瓶颈.

In additive manufacturing (AM), accurate prediction for the deformation of printed objects contributes to compensation in advance, which is crucial to improving the accuracy of products. Many factors affect the deformation, such as the shape of the object, the properties of the material, and parameters in the printing process. Existing methods suffer from difficulties in modeling and generalizing between different shapes. In this paper, we formulate the error prediction in AM as a point-wise deviation prediction task and propose a point-based deep neural network to learn the complex deformation patterns by local and global contextual feature extraction. Furthermore, a data processing flow is proposed for automatically handling the real-scenario data. As an application case, we collect a dataset of dental crowns fabricated by the digital light processing 3D printing and validate the proposed method on the dataset. The results show that our network has a promising ability to predict nonlinear deformation. The proposed method can also be applied to other AM techniques.

Stereolithography is one of the most widely used additive manufacturing techniques for preparing high precision and complex ceramic components. Due to the high optical absorbance and refractive index of SiC powder, the rapid stereolithography of SiC ceramics components has become a key challenge. Here, we innovatively use graded silica to improve the curing thickness, rheological and settling performance of the slurry. And we presented a preparation method of SiC ceramic slurry for stereolithography with high solid content, low viscosity, low sedimentation rate and high curing thickness. The printable precision of the slurry is more than 75 μm, the dynamic viscosity is less than 2 Pa·s, and the 24 h sedimentation height is less than 5%. This strategy demonstrates a tantalizing possibility and promising prospect to rapid stereolithography of large size SiC ceramic green body.