Towards microwave volumetric additive manufacturing: Generation of a computational multi-physics model for localized curing
Not Available
Mukherjee, Saptarshi (ORCID:0000000306841356)↗
Engineering topics
Chang, Tammy
Publications and source records attributed to Chang, Tammy.
System and method for microwave volumetric additive manufacturing
The present disclosure relates to a volumetric additive manufacturing system for forming a structure from a volume of resin using microwave energy. The system makes use of an electronic controller and at least one beam forming algorithm accessible by the electronic controller for generating information relating to an amplitude and a time delay for forming a microwave signal, where the microwave signal will be used in irradiating a build volume, and where the build volume is formed by the volume of resin. A microwave signal generating subsystem is included which is responsive to the information generated by the beam forming algorithm, and which generates a microwave signal using the amplitude and the time delay determined by the beam forming algorithm. An antenna is used to receive the microwave signal and project the microwave signal as a microwave beam, in accordance with the amplitude and time delay, into the build volume to form the structure.
Mukherjee, Saptarshi↗
System and method for sub-wavelength detection for jetting-based additive manufacturing using a split ring resonator probe
The present disclosure relates to a system for detecting and analyzing droplets of feedstock material being ejected from an additive manufacturing device. The system makes use of a split ring resonator (SRR) probe including a ring element having a gap, with the gap being positioned adjacent a path of travel of the droplets of feedstock material. An excitation signal source is used for supplying an excitation signal to the SRR probe. An analyzer analyzes signals generated by the SRR probe in response to perturbations in an electric field generated by the SRR probe as the droplets of feedstock material pass the ring element. The signals are indicative of dimensions of the droplets of feedstock material.
Mukherjee, Saptarshi↗
System and method using in-situ electromagnetic diagnostic for real-time projectile characterization
The present disclosure relates to a system for detecting characteristics of a moving element. The system may include a tubular housing having a tubular first portion having a first end and a second end, with the first end forming an input port and the second end forming an output port. A source of wireless electromagnetic energy projects a wireless electromagnetic energy signal, travelling in a first direction, into the input port and through an interior area defined by the tubular first portion. A signal processing subsystem detects at least one characteristic of the signal after the signal is reflected back to the first end after having interacted with the element as the element moves past the output port of the housing.
Chang, Tammy↗
Illumination frustums for photoconductive switches
Methods and devices for illuminating a photoconductive switch consisting of an optically actuated photoconductive material situated between two electrodes are described. Light from a light source is coupled to an optical fiber, which is attached to a frustum, the other side of which is proximate to the photoconductive switch. Light from the optical fiber enters the frustum, spreads out, and enters the photoconductive switch via the top-side electrode. Some of the light is absorbed, while the remaining light reflects off the bottom-side electrode, travels back through the photoconductive switch, and any unabsorbed light reenters the frustum. The geometry of the frustum is configured such that most of the light reflects back into the switch itself with only a negligible fraction escaping from the optical fiber, which advantageously results in near total utilization of the light.
Rushford, Michael↗
Multi-Defect Detection in Additively Manufactured Lattice Structures Using 3D Electrical Resistance Tomography
Cellular lattice structures possess high strength-to-weight ratios suitable for advanced lightweight engineering applications. However, their quality and mechanical performance can degrade because of defects introduced during manufacturing or in-service. Their complexity and small length scale features make defects difficult to detect using conventional nondestructive evaluation methods. Here we propose a current injection-based method, electrical resistance tomography (ERT), that can be used to detect damaged struts in conductive cellular lattice structures with their intrinsic electromechanical properties. The reconstructed conductivity distributions from ERT can reveal the severity and location of damaged struts without having to probe each strut. However, the low central sensitivity of ERT may result in image artifacts and inaccurate localization of damaged struts. To address this issue, this study introduces an absolute, high throughput, conductivity reconstruction algorithm for 3D ERT. The algorithm incorporates a strut-based normalized sensitivity map to compensate for lower interior sensitivity and suppresses reconstruction artifacts. Numerical simulations and experiments on fabricated representative cellular lattice structures were performed to verify the ability of ERT to quantitatively identify single and multiple damaged struts. The improved performance of this method compared with classical ERT was observed, based on greatly decreased imaging and reconstructed value errors.
36 MATERIALS SCIENCE↗
In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning
Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.
Gaikwad, Aniruddha (ORCID:0000000285642621)↗
In-situ monitoring for liquid metal jetting using a millimeter-wave impedance diagnostic
Abstract This article presents a millimeter-wave diagnostic for the in-situ monitoring of liquid metal jetting additive manufacturing systems. The diagnostic leverages a T-junction waveguide device to monitor impedance changes due to jetted metal droplets in real time. An analytical formulation for the time-domain T-junction operation is presented and supported with a quasi-static full-wave electromagnetic simulation model. The approach is evaluated experimentally with metallic spheres of known diameters ranging from 0.79 to 3.18 mm. It is then demonstrated in a custom drop-on-demand liquid metal jetting system where effective droplet diameters ranging from 0.8 to 1.6 mm are detected. Experimental results demonstrate that this approach can provide information about droplet size, timing, and motion by monitoring a single parameter, the reflection coefficient amplitude at the input port. These results show the promise of the impedance diagnostic as a reliable in-situ characterization method for metal droplets in an advanced manufacturing system.
42 ENGINEERING↗