Condensation figure (CF) is a simple and cost-effective method to inspect patterns and defects on product surfaces. This inspection method is based on energy differentials on surfaces. Due to wettability contrast, water droplets are preferentially nucleated and grown on hydrophilic regions. The formed CF can further be segmented for the recognition and measurement of the patterns on the surfaces. The state-of-the-art CF methods are closeenvironmental, while controlled open-environmental CF has broader applications in manufacturing and quality inspection. The lack of open-environmental CF for such applications is mostly because of the unavailable droplet size control methods. In this paper, we designed a high-resolution optical surface inspection system based on open environment droplet-size-controlled CFs. This is done by real-time imaging and recognizing the condensed droplet sizes and densities on surfaces, and accordingly tuning the vaporization and evaporation of droplets on the surface by the vapor flow rate. Our experimental results show that the average diameter of droplets can be controlled below 3.5 µm in a laboratory setup for different metal substrates. We also test the system for inspecting self-assembled monolayer patterns with linewidth of 5 µm on a gold surface; this can be promisingly used for online quality monitoring and in-process controlmore »
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 more »
- Publication Date:
- NSF-PAR ID:
- 10370559
- Journal Name:
- Journal of Intelligent Manufacturing
- Volume:
- 33
- Issue:
- 7
- Page Range or eLocation-ID:
- p. 2093-2117
- ISSN:
- 0956-5515
- Publisher:
- Springer Science + Business Media
- Sponsoring Org:
- National Science Foundation
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