ABSTRACT
We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle-substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle-substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.
ABSTRACT
For most electrodes fabricated with carbon, transition metal compounds, or conductive polymers, the capacitance may deteriorate with cyclic charging and discharging. Thus, an electrochemically stable supercapacitor has long been pursued by researchers. In this work, the hierarchical structure of balsa wood is preserved in the converted carbon which is used as a supporting framework to fabricate electrodes for supercapacitors. Well-grown carbon nanotubes (CNTs) on interior and exterior surfaces of balsa carbon channels provide two advantages including 1) offering more specific surface area to boost capacitance via electric double layer capacitance and 2) offering more active Fe and Ni sites to participate in the redox reaction to enhance capacitance of the balsa carbon/CNTs electrode. The balsa carbon/CNTs demonstrate an excellent area capacitance of 1940 mF cm-2 . As active sites on Ni and Fe catalysts and inner walls of CNTs are gradually released, the capacitance increases 66% after 4000 charge-discharge cycles. This work brings forward a strategy for the rational design of high-performance biomass carbon coupled with advanced nanostructures for energy storage.
