Additive manufacturing of thermoset elastomers: A review of emerging technologies
Thermoset elastomers (TSEs) are widely used in industries such as automotive, household appliances, healthcare, and fashion due to their flexibility and stability. However, these same properties make TSEs challenging to process using additive manufacturing (AM) techniques. This review categorizes AM...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098625001983 |
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| Summary: | Thermoset elastomers (TSEs) are widely used in industries such as automotive, household appliances, healthcare, and fashion due to their flexibility and stability. However, these same properties make TSEs challenging to process using additive manufacturing (AM) techniques. This review categorizes AM technologies for producing TSEs into three main groups: photopolymerization-based, two-phase, and material extrusion–based techniques. Photopolymerization offers high resolution and material versatility but is constrained by build size and environmental impacts. Two-phase systems enable tunable properties but suffer from rheological and bonding issues, while material extrusion is more cost-effective yet less precise. Overall, current research primarily concentrates on technology and formulation development. Mechanical characterization of 3D-printed TSEs is typically limited to tensile properties. Comprehensive mechanical testing, including application-specific properties, is still rare and remains an essential area for future qualification of technological advances. The sustainability aspects of 3D printing TSEs are also addressed, with a focus on environmentally friendly raw material selection and the general environmental considerations of 3D printing methods. Emerging trends in this field include the development of smart materials, sustainable solutions, and integrated hybrid methods that incorporate artificial intelligence and machine learning. |
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| ISSN: | 2215-0986 |