Next Article in Journal
Characterisation of Cannabis-Based Products Marketed for Medical and Non-Medical Use Purchased in Portugal
Next Article in Special Issue
Injecting Sustainability into Epoxy-Based Composite Materials by Using Bio-Binder from Hydrothermal Liquefaction Processing of Microalgae
Previous Article in Journal
Encapsulated Mn-Saturated Lactoferrin as a Safe Source of Manganese Ions for Restoring Probiotic Lactobacillus plantarum
Previous Article in Special Issue
Control of Pore Sizes in Epoxy Monoliths and Applications as Sheet-Type Adhesives in Combination with Conventional Epoxy and Acrylic Adhesives
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Additively Manufactured Carbon-Reinforced ABS Honeycomb Composite Structures and Property Prediction by Machine Learning

by
Meelad Ranaiefar
1,*,
Mrityunjay Singh
2,* and
Michael C. Halbig
1
1
NASA Glenn Research Center, Cleveland, OH 44135, USA
2
Ohio Aerospace Institute, Cleveland, OH 44142, USA
*
Authors to whom correspondence should be addressed.
Molecules 2024, 29(12), 2736; https://doi.org/10.3390/molecules29122736
Submission received: 30 March 2024 / Revised: 8 May 2024 / Accepted: 3 June 2024 / Published: 8 June 2024

Abstract

The expansive utility of polymeric 3D-printing technologies and demand for high- performance lightweight structures has prompted the emergence of various carbon-reinforced polymer composite filaments. However, detailed characterization of the processing–microstructure–property relationships of these materials is still required to realize their full potential. In this study, acrylonitrile butadiene styrene (ABS) and two carbon-reinforced ABS variants, with either carbon nanotubes (CNT) or 5 wt.% chopped carbon fiber (CF), were designed in a bio-inspired honeycomb geometry. These structures were manufactured by fused filament fabrication (FFF) and investigated across a range of layer thicknesses and hexagonal (hex) sizes. Microscopy of material cross-sections was conducted to evaluate the relationship between print parameters and porosity. Analyses determined a trend of reduced porosity with lower print-layer heights and hex sizes compared to larger print-layer heights and hex sizes. Mechanical properties were evaluated through compression testing, with ABS specimens achieving higher compressive yield strength, while CNT-ABS achieved higher ultimate compressive strength due to the reduction in porosity and subsequent strengthening. A trend of decreasing strength with increasing hex size across all materials was supported by the negative correlation between porosity and increasing print-layer height and hex size. We elucidated the potential of honeycomb ABS, CNT-ABS, and ABS-5wt.% CF polymer composites for novel 3D-printed structures. These studies were supported by the development of a predictive classification and regression supervised machine learning model with 0.92 accuracy and a 0.96 coefficient of determination to help inform and guide design for targeted performance.
Keywords: acrylonitrile butadiene styrene (ABS); additive manufacturing; carbon-reinforced ABS; classification; fused filament fabrication; honeycomb; machine learning; mechanical properties; polymer composites; regression acrylonitrile butadiene styrene (ABS); additive manufacturing; carbon-reinforced ABS; classification; fused filament fabrication; honeycomb; machine learning; mechanical properties; polymer composites; regression

Share and Cite

MDPI and ACS Style

Ranaiefar, M.; Singh, M.; Halbig, M.C. Additively Manufactured Carbon-Reinforced ABS Honeycomb Composite Structures and Property Prediction by Machine Learning. Molecules 2024, 29, 2736. https://doi.org/10.3390/molecules29122736

AMA Style

Ranaiefar M, Singh M, Halbig MC. Additively Manufactured Carbon-Reinforced ABS Honeycomb Composite Structures and Property Prediction by Machine Learning. Molecules. 2024; 29(12):2736. https://doi.org/10.3390/molecules29122736

Chicago/Turabian Style

Ranaiefar, Meelad, Mrityunjay Singh, and Michael C. Halbig. 2024. "Additively Manufactured Carbon-Reinforced ABS Honeycomb Composite Structures and Property Prediction by Machine Learning" Molecules 29, no. 12: 2736. https://doi.org/10.3390/molecules29122736

APA Style

Ranaiefar, M., Singh, M., & Halbig, M. C. (2024). Additively Manufactured Carbon-Reinforced ABS Honeycomb Composite Structures and Property Prediction by Machine Learning. Molecules, 29(12), 2736. https://doi.org/10.3390/molecules29122736

Article Metrics

Back to TopTop