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Анализ, классификация и предотвращение образования дефектов в экструзионной 3D-печатистатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или Scopus- Авторы: Erokhin Kirill Sergeevich, Naumov Sergei Aleksandrovich, Ananikov Valentine Pavlovich
- Журнал: Russian Chemical Reviews
- Том: 92
- Номер: 11
- Год издания: 2023
- Издательство: Turpion - Moscow Ltd.
- Местоположение издательства: United Kingdom
- Номер статьи: RCR5103
- DOI: 10.59761/rcr5103
- Аннотация: Additive manufacturing technologies (or 3D printing) have emerged as powerful tools for creating a diverse array of objects, promising a paradigm shift in production methodologies across industries. In chemistry, it allows the manufacturing of reactors with complex topology. However, the benefits of these technologies can be diminished by the use of suboptimal parameters or inferior materials, leading to defects that significantly degrade the quality and functionality of the resulting products. The formulation of effective preventive strategies remains hampered by an incomplete understanding of defect formation. Given this, our review provides a comprehensive exploration of defects that arise during the Fused Filament Fabrication (FFF) — one of the most prevalent 3D printing methods. The defects are systematically classified according to several key characteristics, including size, type, mode of occurrence, and location. Each common defect is discussed in detail, describing its external manifestation, root causes, the impact on the properties of printed parts, and potential preventive measures. Our findings unveil the complex interplay between material properties, printing parameters, and cooling dynamics in the defect formation process. This classification has significant practical relevance, providing a solid basis for the development of strategies to minimize defects and improve the quality of 3D printed products. It provides valuable insights for a wide audience, including researchers investigating chemical processes and additive manufacturing technologies, 3D printing engineers, 3D printer operators, and quality assurance engineers involved in production quality control. In addition, our review points the way forward for future research in this area. There is a crucial need for the development of advanced machine learning and artificial intelligence models that can predict defect formation based on given printing parameters and material properties. Future investigations should also focus on the discovery of novel materials and refining of printing parameters to achieve superior quality of FFF 3D printed products. This is the first review on defect analysis, classification, and prevention methods in 3D printing. This review serves as a cornerstone for these future advances, promoting a deeper understanding of defect formation and prevention in additive manufacturing. The bibliography includes 180 references .
- Добавил в систему: Анаников Валентин Павлович