Dynamic Fluid‐Assisted Continuous Multimaterial 3D Printing for Seamless Gradient Structures DOI
Wenbo Wang, Siying Liu, Luyang Liu

et al.

Advanced Materials Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Abstract Functionally gradient materials emulate nature's ability to seamlessly blend properties through variations in material composition, unlocking advanced engineering applications such as biomedical devices and high‐performance composites. Additive manufacturing, particularly stereolithography, enables sophisticated 3D geometries with diverse materials. However, current stereolithography‐based multi‐material printing is constrained by time‐intensive switching compromised interfacial properties. To overcome these challenges, we present dynamic fluid‐assisted micro continuous liquid interface production (DF‐µCLIP), a high‐speed platform that integrates varying compositions fully fashion. By utilizing the polymerization‐free “dead zone”, vliquid resins are replenished within resin bath equipped fluidic channels synchronized supply system. DF‐µCLIP achieves ultra‐fast speeds of 90 mm/hour 7.4 µ m pixel‐1 resolution while enabling on‐the‐fly transitions. This strategy enhances mechanical strength at entangled polymer networks promotes seamless transitions between distinct ilike fragile hydrogels rigid polymers, addressing failure caused mismatch swelling behavior. Additionally, replenishment real‐time composition control instead conventional step‐wise controlled gradient. Demonstrations include polymers color carbon nanotube (CNT) composites conductivity.

Language: Английский

Machine learning-based study of hardness in polypropylene/carbon nanotube and low-density polyethylene/carbon nanotube composites DOI Creative Commons
Harshit Sharma, Gaurav Arora, Raj Kumar

et al.

Discover Materials, Journal Year: 2025, Volume and Issue: 5(1)

Published: Jan. 4, 2025

In the present work, hardness prediction of polypropylene/carbon nanotubes (PP/CNT) and low-density polyethylene/carbon (LDPE/CNT) composite materials, processed by microwave technique, has been explored using machine learning models i.e. (Random Forest, Support Vector Regression, K-Nearest Neighbors, Linear Neural Network). Four input vectors have used in construction proposed network, such as CNT concentration, power, pressure applied, exposure time. Hardness is one output that evolved from work. This study presents based on for both PP/CNT LDPE/CNT results show Random Forest model consistently performs better than others context with performance metrics like Root Mean Square Error (RMSE), Absolute (MAE), Rate determination (R2) values. Investigations performed resampling strategies, showing jackknife approach enhances precision robustness case composites. For material, it noticed gives highest value R2 (0.94), whereas lowest 0.18 material. most reliable predicting characteristics material due to its ability handle complex datasets. The demonstrates superior accuracy, a maximum error just 1.61%, making option high-precision applications enhanced mechanical interactions improved dispersion.

Language: Английский

Citations

0
Tailored TiO2/WO3 Composites for Enhanced Electrocatalytic and Photocatalytic Applications DOI

Xinyang Xu,

Yingguan Xiao,

Ru‐Song Zhao

et al.

Ceramics International, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Dynamic Fluid‐Assisted Continuous Multimaterial 3D Printing for Seamless Gradient Structures DOI
Wenbo Wang, Siying Liu, Luyang Liu

et al.

Advanced Materials Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Abstract Functionally gradient materials emulate nature's ability to seamlessly blend properties through variations in material composition, unlocking advanced engineering applications such as biomedical devices and high‐performance composites. Additive manufacturing, particularly stereolithography, enables sophisticated 3D geometries with diverse materials. However, current stereolithography‐based multi‐material printing is constrained by time‐intensive switching compromised interfacial properties. To overcome these challenges, we present dynamic fluid‐assisted micro continuous liquid interface production (DF‐µCLIP), a high‐speed platform that integrates varying compositions fully fashion. By utilizing the polymerization‐free “dead zone”, vliquid resins are replenished within resin bath equipped fluidic channels synchronized supply system. DF‐µCLIP achieves ultra‐fast speeds of 90 mm/hour 7.4 µ m pixel‐1 resolution while enabling on‐the‐fly transitions. This strategy enhances mechanical strength at entangled polymer networks promotes seamless transitions between distinct ilike fragile hydrogels rigid polymers, addressing failure caused mismatch swelling behavior. Additionally, replenishment real‐time composition control instead conventional step‐wise controlled gradient. Demonstrations include polymers color carbon nanotube (CNT) composites conductivity.

Language: Английский

Citations

0