Due to manufacturer implemented processing parameter restrictions and the cost prohibitive nature of selective laser sintering (SLS) machines, researchers have limited opportunities to explore the processing of new materials using this additive manufacturing (3D printing) process. Accordingly, this article aimed to overcome these limitations by describing the build and operation of a customizable low-cost polymer SLS machine. The machine boasts a three piston powder bed with the center build piston heated by PID controlled ceramic heaters. Thermal energy for powder consolidation was provided via a 2.44 W solid state diode laser which was mechanically traversed using stepper motor driven belt drives. New layers of powder were deposited by a counter-rotating roller system. The SLS machine was controlled by executing G-code in Mach3 allowing full customization of processing parameters. The machine demonstrated the production of parts from polyamide-12 reaching densities of 918 ± 9 kg/m while achieving an elastic modulus of 358.36 ± 3.04 MPa and elongation at break of 11.13 ± 0.02%. With part properties similar to those achievable with a commercial machine, this low-cost SLS machine could be a vital tool in assisting researchers to explore the processing of new materials.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9041240 | PMC |
| http://dx.doi.org/10.1016/j.ohx.2020.e00119 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.
Technol Health Care
November 2024
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Korea.
Pneumonia is a dangerous disease that kills millions of children and elderly patients worldwide every year. The detection of pneumonia from a chest x-ray is perpetrated by expert radiologists. The chest x-ray is cheaper and is most often used to diagnose pneumonia.
View Article and Find Full Text PDFMarkerless Kinematic Data in the Frontal Plane Contributions to Movement Quality in the Single-Leg Squat Test: A Comparison and Decision Tree Approach.
J Sport Rehabil
November 2024
Department of Physical Education, Yonsei University, Seoul, South Korea.
Objective: The aim of this study is to compare kinematic data of the frontal trunk, pelvis, knee, and summated angles (trunk plus knee) among categorized grades using the single-leg squat (SLS) test, to classify the SLS grade, and to investigate the association between the SLS grade and the frontal angles using smartphone-based markerless motion capture.
Methods: Ninety-one participants were categorized into 3 grades (good, reduced, and poor) based on the quality of the SLS test. An automated pose estimation algorithm was employed to assess the frontal joint angles during SLS, which were captured by a single smartphone camera.
Benchmarking machine learning methods for synthetic lethality prediction in cancer.
Nat Commun
October 2024
School of Information Science and Technology, ShanghaiTech University, Shanghai, China.
Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking.
View Article and Find Full Text PDFPrediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning.
BMC Nephrol
October 2024
Divisions of Nephrology and Cardiology, University Hospital RWTH Aachen, Aachen, Germany.
Article Synopsis
- Researchers assessed the potential of machine learning, specifically using XGBoost and logistic regression, to predict the 180-day risk of gastrointestinal bleeding (GIB) hospitalizations in patients on hemodialysis.
- The study analyzed a large dataset from the US involving over 450,000 patients between 2017-2020, identifying risk factors such as age and various health indices.
- XGBoost demonstrated better predictive ability compared to logistic regression, suggesting machine learning could improve early detection of GIB risk, but further validation is required to confirm these findings.
Pitch-Tracking Metrics as a Predictor of Future Shoulder and Elbow Injuries in Major League Baseball Pitchers: A Machine-Learning and Game-Theory Based Analysis.
Orthop J Sports Med
August 2024
Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota, USA.
Background: Understanding interactions between multiple risk factors for shoulder and elbow injuries in Major League Baseball (MLB) pitchers is important to identify potential avenues by which risk can be reduced while minimizing impact on player performance.
Purpose: To apply a novel game theory-based approach to develop a machine-learning model predictive of next-season shoulder and elbow injuries in MLB pitchers and use this model to understand interdependencies and interaction effects between the most important risk factors.
Study Design: Case-control study; Level of evidence, 3.
Want 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!