Nowadays, metallic bone replacement is in high demand due to different issues, like sicknesses and accidents. Thus, bone implants are fabricated with tailored properties and microstructure for long-term use in the human body. To improve such implants, 3D printing is the most promising technique. Therefore, this work aims to evaluate the fabrication of porous materials by extrusion 3D printing of Ti6Al4V. Cylindrical samples were fabricated from pellets for metal injection molding of Ti6Al4V powders, creating hexagonal channels with three different sizes. The densification kinetics was evaluated by dilatometry tests, which enabled following the densification of the samples during the sintering cycle. Subsequently, the samples were characterized by scanning electron microscopy and X-ray computed tomography to analyze their microstructure. Compression tests evaluated the mechanical strength of sintered samples. It was found that the hexagonal shape during printing is better defined as the channel size increases. The results show similar behavior for each of the channel sizes during sintering; however, greater densification is obtained as the channel size decreases. Additionally, microporosity is obtained at the particle level, which is completely interconnected, ensuring the passage of fluids through the entire sample. On the other hand, as the channel size increases, Young's modulus and yield strength are considerably reduced. The main conclusion is that parts with two scales of porosity can be designed by the 3D printing extrusion process.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3390/ma18020389 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Age group classification based on optical measurement of brain pulsation using machine learning.
Sci Rep
January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
View Article and Find Full Text PDFA vision model for automated frozen tuna processing.
Sci Rep
January 2025
School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan, 316022, People's Republic of China.
Accurate and rapid segmentation of key parts of frozen tuna, along with precise pose estimation, is crucial for automated processing. However, challenges such as size differences and indistinct features of tuna parts, as well as the complexity of determining fish poses in multi-fish scenarios, hinder this process. To address these issues, this paper introduces TunaVision, a vision model based on YOLOv8 designed for automated tuna processing.
View Article and Find Full Text PDFEffect of artificial fish nest placement on spawning site preference and reproductive efficiency in reservoir fish with adhesive and demersal eggs.
Sci Rep
January 2025
Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, China.
Artificial fish nests are common tools in fisheries management, providing spawning grounds to enhance the size and diversity of fish populations. This study aimed to explore the effects of deployment locations on the reproductive efficiency and preferences of fish with adhesive and demersal eggs using artificial nests. Floating artificial nests were deployed in three regions (upstream, midstream, and downstream) of a reservoir in Zhejiang, China, at locations with three topographical types: steep slope (reservoir shore, slopes > 60°), gentle slope (reservoir shore, slopes < 30°), and confluence (middle thread of channel).
View Article and Find Full Text PDFEnhancing waste classification accuracy with Channel and Spatial Attention-Based Multiblock Convolutional Network.
Environ Monit Assess
January 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Shollinganallur, Chennai, India.
Municipal waste classification is significant for effective recycling and waste management processes that involve the classification of diverse municipal waste materials such as paper, glass, plastic, and organic matter using diverse techniques. Yet, this municipal waste classification process faces several challenges, such as high computational complexity, more time consumption, and high variability in the appearance of waste caused by variations in color, type, and degradation level, which makes an inaccurate waste classification process. To overcome these challenges, this research proposes a novel Channel and Spatial Attention-Based Multiblock Convolutional Network for accurately classifying municipal waste that utilizes a unique attention mechanism for enhancing feature learning and waste classification accuracy.
View Article and Find Full Text PDFQuantitative Insights into Processivity of an Hsp100 Protein Disaggregase on Folded Proteins.
Biophys J
January 2025
Department of Chemistry, University of Alabama at Birmingham, Birmingham, Alabama. Electronic address:
The Hsp100 family of protein disaggregases play important roles in maintaining protein homeostasis in cells. E. coli ClpB is an Hsp100 protein that solubilizes protein aggregates.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!