Waste Plastic Direct Extrusion Hangprinter

Petsiuk, Aliaksei; Lavu, Bharath; Dick, Rachel M; Pearce, Joshua M.

doi:10.20944/preprints202208.0075.v1

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…The ceiling unit and anchors are attached to plywood, while the room floor can be used as the printing surface. Motor gears and bracket mounts, carriage beam holders, level sliders, and the end effector case have been customized [49] to suit the existing load.…”

Section: Hybrid Printer Designmentioning

confidence: 99%

Waste Plastic Direct Extrusion Hangprinter

et al. 2022

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As the additive manufacturing industry grows, it is compounding the global plastic waste problem. Distributed recycling and additive manufacturing (DRAM) offers an economic solution to this challenge, but it has been relegated to either small-volume 3D printers (limiting waste recycling throughput) or expensive industrial machines (limiting accessibility and lateral scaling). To overcome these challenges, this paper provides proof-of-concept for a novel, open-source hybrid 3D printer that combines a low-cost hanging printer design with a compression-screw-based end-effector that allows for the direct extrusion of recycled plastic waste in large expandable printing volumes. Mechanical testing of the resultant prints from 100% waste plastic, however, showed that combining the challenges of non-uniform feedstocks and a heavy printhead for a hangprinter reduced the strength of the parts compared to fused filament fabrication. The preliminary results are technologically promising, however, and provide opportunities to improve on the open-source design to help process the volumes of waste plastic needed for DRAM to address the negative environmental impacts of global plastic use.

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Section: Hybrid Printer Designmentioning

confidence: 99%

Waste Plastic Direct Extrusion Hangprinter

et al. 2022

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Additive Manufacturing for Surgical Planning and Education: A Review

Kantaros,

Petrescu,

Abdoli

et al. 2024

Applied Sciences

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Additive manufacturing has been widely used in various industries, including the healthcare sector. Over the last few decades, AM has been playing an important role in the medical field in different areas, including surgical planning, implants, and educational activities. For surgical applications, AM can help surgeons practice and plan an operation until they are confident with the process. This can help to reduce operational risk and time. In addition, it can help to demonstrate the problem to other colleagues. AM has also been used to produce 3D models to teach students and doctors about human anatomy. This paper aims to comprehensively review the diverse applications of additive manufacturing within the domains of surgical planning and medical education. By focusing on the multifaceted roles played by AM in these critical areas, a contribution to the growing body of knowledge that underscores the transformative potential of this technology in shaping the future of healthcare practices is sought to be made.

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Synthetic-to-Real Composite Semantic Segmentation in Additive Manufacturing

Petsiuk,

Singh,

Dadhwal

et al. 2024

JMMP

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The application of computer vision and machine learning methods for semantic segmentation of the structural elements of 3D-printed products in the field of additive manufacturing (AM) can improve real-time failure analysis systems and potentially reduce the number of defects by providing additional tools for in situ corrections. This work demonstrates the possibilities of using physics-based rendering for labeled image dataset generation, as well as image-to-image style transfer capabilities to improve the accuracy of real image segmentation for AM systems. Multi-class semantic segmentation experiments were carried out based on the U-Net model and the cycle generative adversarial network. The test results demonstrated the capacity of this method to detect such structural elements of 3D-printed parts as a top (last printed) layer, infill, shell, and support. A basis for further segmentation system enhancement by utilizing image-to-image style transfer and domain adaptation technologies was also considered. The results indicate that using style transfer as a precursor to domain adaptation can improve real 3D printing image segmentation in situations where a model trained on synthetic data is the only tool available. The mean intersection over union (mIoU) scores for synthetic test datasets included 94.90% for the entire 3D-printed part, 73.33% for the top layer, 78.93% for the infill, 55.31% for the shell, and 69.45% for supports.

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Waste Plastic Direct Extrusion Hangprinter

Cited by 5 publications

References 39 publications

Waste Plastic Direct Extrusion Hangprinter

Waste Plastic Direct Extrusion Hangprinter

Additive Manufacturing for Surgical Planning and Education: A Review

Synthetic-to-Real Composite Semantic Segmentation in Additive Manufacturing

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