Abstract Nature provides us with a large number of functional material systems consisting of hierarchical structures, where significant variations in dimensions are present. Such hierarchical structures are difficult to build by traditional manufacturing processes due to manufacturing limitations. Nowadays, three-dimensional (3D) objects with complex structures can be built by gradually accumulating in a layer-based additive manufacturing (AM); however, the hierarchical structure measured from macroscale to nanoscale sizes still raises significant challenges to the AM processes, whose manufacturing capability is intrinsically specified within a certain scope. It is desired to develop a multiscale AM process to narrow this gap between scales of feature in hierarchical structures. This research aims to investigate an integration approach to fabricating hierarchical objects that have macro-, micro-, and nano-scales features in an object. Firstly, the process setup and the integrated process of two-photon polymerization (TPP), immersed surface accumulation (ISA), and mask image projection-based stereolithography (MIP-SL) were introduced to address the multiscale fabrication challenge. Then, special hierarchical design and process planning toward integrating multiple printing processes are demonstrated. Lastly, we present two test cases built by our hierarchical printing method to validate the feasibility and efficiency of the proposed multiscale hierarchical printing approach. The results demonstrated the capability of the developed multiscale 3D printing process and showed its future potential in various novel applications, such as optics, microfluidics, cell culture, as well as interface technology.
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Spatiotemporal Projection‐Based Additive Manufacturing: A Data‐Driven Image Planning Method for Subpixel Shifting in a Split Second
Additive manufacturing (AM) is a digital manufacturing process that can directly convert a computer‐aided design model into a physical object in a layer‐by‐layer manner. Due to the additive and discrete nature of the digital manufacturing process, AM needs to find a trade‐off between process resolution and production efficiency. Traditional AM processes balance the resolution and efficiency by tuning the processes either in the temporal domain (e.g., higher speed in serial processes) or in the spatial domain (e.g., more tools in parallel processes). To improve the resolution without sacrificing efficiency, a data‐driven mask image planning method based on subpixel shifting in a split second by tuning the process in both temporal and spatial domains is presented. The method is based on the optimized pixel blending principle and a fast error diffusion‐based optimization model. Various simulation and experimental tests are carried out to verify the developed subpixel shifting method. The experimental results demonstrate the data‐driven‐based mask image calibration and planning techniques significantly improve the fabricated part quality without compromising the process efficiency. The presented spatiotemporal strategy may shed light for future research on the projection‐based AM processes.
- NSF-PAR ID:
- 10275586
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- Volume:
- 3
- Issue:
- 12
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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