There are growing demands for multimaterial three-dimensional (3D) printing to manufacture 3D object where voxels with different properties and functions are precisely arranged. Digital light processing (DLP) is a high-resolution fast-speed 3D printing technology suitable for various materials. However, multimaterial 3D printing is challenging for DLP as the current multimaterial switching methods require direct contact onto the printed part to remove residual resin. Here we report a DLP-based centrifugal multimaterial (CM) 3D printing method to generate large-volume heterogeneous 3D objects where composition, property and function are programmable at voxel scale. Centrifugal force enables non-contact, high-efficiency multimaterial switching, so that the CM 3D printer can print heterogenous 3D structures in large area (up to 180 mm × 130 mm) made of materials ranging from hydrogels to functional polymers, and even ceramics. Our CM 3D printing method exhibits excellent capability of fabricating digital materials, soft robots, and ceramic devices.
Wire electrical discharge machining (WEDM), widely used to fabricate micro and precision parts in manufacturing industry, is a nontraditional machining method using discharge energy which is transformed into thermal energy to efficiently remove materials. A great amount of research has been conducted based on pulse characteristics. However, the spark image-based approach has little research reported. This paper proposes a discharge spark image-based approach. A model is introduced to predict the discharge status using spark image features through a synchronous high-speed image and waveform acquisition system. First, the relationship between the spark image features (e.g., area, energy, energy density, distribution, etc.) and discharge status is explored by a set of experiments). Traditional methods have claimed that pulse waveform of “short” status is related to the status of non-machining while through our research, it is concluded that this is not always true by conducting experiments based on the spark images. Second, a deep learning model based on Convolution neural network (CNN) and Gated recurrent unit (GRU) is proposed to predict the discharge status. A time series of spark image features extracted by CNN form a 3D feature space is used to predict the discharge status through GRU. Moreover, a quantitative labeling method of machining state is proposed to improve the stability of the model. Due the effective features and the quantitative labeling method, the proposed approach achieves better predict result comparing with the single GRU model.
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