High-throughput additive manufacturing of thiol-based, oxygen-insensitive photoresins with digital micromirror devices (Conference Presentation)

2019 ◽  
Author(s):  
Walter Voit ◽  
Benjamin Lund ◽  
Stephen Kay ◽  
Caleb Lund
Keyword(s):  
Additive Manufacturing ◽  
High Throughput ◽  
Digital Micromirror Devices

scholarly journals A Tunable 3D Printed Microfluidic Resistive Pulse Sensor for the Characterisation of Algae and Microplastics

2020 ◽  
Author(s):  
Mark Platt ◽  
Eugenie Hunsicker ◽  
Marcus Pollard
Keyword(s):  
Additive Manufacturing ◽  
High Throughput ◽  
Low Cost ◽  
Physical Property ◽  
Health Food ◽  
Suspended Particulates ◽  
Resistive Pulse ◽  
Pulse Sensor ◽  
3D Printed ◽  
Rod Structures

Technologies that can detect and characterise particulates in liquids have applications in health, food and environmental monitoring. Here we present a low-cost and high-throughput multiuse counter that classifies a particle’s size, concentration, porosity and shape. Using an additive manufacturing process, we have assembled a reusable flow resistive pulse sensor. The device remains stable for several days with repeat measurements. We demonstrate its use for characterising algae with spherical and rod structures as well as microplastics shed from teabags. We present a methodology that results in a specific signal for microplastics, namely a conductive pulse, in contrast to particles with smooth surfaces such as calibration particles or algae, allowing the presence of microplastics to be easily confirmed and quantified. In addition, the shape of the signal and particle are correlated, giving an extra physical property to characterise suspended particulates. The technology can rapidly screen volumes of liquid, 1 mL/ min, for the presence of microplastics and algae.<br>

scholarly journals High-throughput synthesis of Mo-Nb-Ta-W high-entropy alloys via additive manufacturing

2020 ◽  
Vol 187 ◽  
pp. 108358 ◽  
Author(s):  
Michael Moorehead ◽  
Kaila Bertsch ◽  
Michael Niezgoda ◽  
Calvin Parkin ◽  
Mohamed Elbakhshwan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
High Throughput ◽  
High Entropy Alloys ◽  
High Entropy

scholarly journals Local Viscosity Control Printing for High-Throughput Additive Manufacturing of Polymers

2016 ◽  
Vol 3 (4) ◽  
pp. 252-261 ◽  
Author(s):  
Joshua E. Siegel ◽  
Dylan C. Erb ◽  
Isaac M. Ehrenberg ◽  
Pranay Jain ◽  
Sanjay E. Sarma
Keyword(s):  
Additive Manufacturing ◽  
High Throughput

scholarly journals Uncertainty quantification and reduction in metal additive manufacturing

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Zhuo Wang ◽  
Chen Jiang ◽  
Pengwei Liu ◽  
Wenhua Yang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
Quality Control ◽  
Additive Manufacturing ◽  
Computational Model ◽  
Uncertainty Quantification ◽  
High Throughput ◽  
Surrogate Model ◽  
Metal Additive Manufacturing ◽  
Uncertainty Sources ◽  
Melt Pool ◽  
Metal Additive

AbstractUncertainty quantification (UQ) in metal additive manufacturing (AM) has attracted tremendous interest in order to dramatically improve product reliability. Model-based UQ, which relies on the validity of a computational model, has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments. However, its adoption in the practical AM process requires overcoming two main challenges: (1) the inaccurate knowledge of uncertainty sources and (2) the intrinsic uncertainty associated with the computational model. Here, we propose a data-driven framework to tackle these two challenges by combining high throughput physical/surrogate model simulations and the AM-Bench experimental data from the National Institute of Standards and Technology (NIST). We first construct a surrogate model, based on high throughput physical simulations, for predicting the three-dimensional (3D) melt pool geometry and its uncertainty with respect to AM parameters and uncertainty sources. We then employ a sequential Bayesian calibration method to perform experimental parameter calibration and model correction to significantly improve the validity of the 3D melt pool surrogate model. The application of the calibrated melt pool model to UQ of the porosity level, an important quality factor, of AM parts, demonstrates its potential use in AM quality control. The proposed UQ framework can be generally applicable to different AM processes, representing a significant advance toward physics-based quality control of AM products.

scholarly journals A Tunable 3D Printed Microfluidic Resistive Pulse Sensor for the Characterisation of Algae and Microplastics

2020 ◽  
Author(s):  
Mark Platt ◽  
Eugenie Hunsicker ◽  
Marcus Pollard
Keyword(s):  
Additive Manufacturing ◽  
High Throughput ◽  
Low Cost ◽  
Physical Property ◽  
Health Food ◽  
Suspended Particulates ◽  
Resistive Pulse ◽  
Pulse Sensor ◽  
3D Printed ◽  
Rod Structures

Technologies that can detect and characterise particulates in liquids have applications in health, food and environmental monitoring. Here we present a low-cost and high-throughput multiuse counter that classifies a particle’s size, concentration, porosity and shape. Using an additive manufacturing process, we have assembled a reusable flow resistive pulse sensor. The device remains stable for several days with repeat measurements. We demonstrate its use for characterising algae with spherical and rod structures as well as microplastics shed from teabags. We present a methodology that results in a specific signal for microplastics, namely a conductive pulse, in contrast to particles with smooth surfaces such as calibration particles or algae, allowing the presence of microplastics to be easily confirmed and quantified. In addition, the shape of the signal and particle are correlated, giving an extra physical property to characterise suspended particulates. The technology can rapidly screen volumes of liquid, 1 mL/ min, for the presence of microplastics and algae.<br>

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