Liquid droplet impact is a subject that has been investigated in both engineering and non-engineering applications to understand and to control this phenomenon. Spray cooling, ink-jet printing, spray coating and painting, soil erosion prevention, pesticide application, and impact erosion are merely a few examples in which droplet impact is involved. Erosion caused by droplet impact on a solid surface is important in numerous elements of industrial equipment, such as pipelines, steam turbines, and wind turbine blades. Though experimental and modeling studies have been performed on this topic, most failed to perform quantitative investigation especially when it came to the erosion of wind turbine blades. Moreover, most approaches assume that the impacting droplets are completely spherical and unaffected by any local turbulence or vortex shedding. As the droplet erosion process could be affected by several parameters, such as the impact velocity, shape and size of the droplets, this study focuses on investigating droplet properties and movement in a controlled lab environment. High speed imaging and Particle Image Velocimetry (PIV) methods are used for this purpose. PIV is used to measure the velocity, circularity, and size of the falling droplets in both disturbed and un-disturbed flow conditions. High-speed camera imaging provides additional insight to the path of the droplets’ movement in the presence of any turbulence. Experiments are performed at a variety of flow rates utilizing a range of blunt needle gauge sizes to create different droplet sizes. It is observed that the blunt needles produce a train of droplets that are different in size following each leading droplet. This is a crucial observation as it will have a direct impact on the magnitude of erosion and should be considered in the future modeling efforts.
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In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning
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.
more » « less- NSF-PAR ID:
- 10370559
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of Intelligent Manufacturing
- Volume:
- 33
- Issue:
- 7
- ISSN:
- 0956-5515
- Page Range / eLocation ID:
- p. 2093-2117
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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