This paper reports a high‐resolution, template‐free, and direct‐printing method of functional nanofiber on 3D surfaces using a self‐aligning nanojet (SA‐N) in near‐field electrospinning (NFES). In the lowest regime of NFES, the cone‐jet transition is induced by the surface current, which leads to a unique jetting configuration where the microscale Taylor cone (microcone) is formed on the surface of the spherical‐shape droplet. The microcone rapidly develops to the nanoscale jet where the tangential electric force dominates the kinematics of the charged jet. The spherical‐shape ejection boundary allows the jetting angle from 0° to ±90° in both convex and concave surfaces, enabling precise deposition of nanofiber regardless of the curvature of the 3D surfaces. Using SA‐N, precise printing of functional nanofiber is successfully demonstrated on various 3D geometries, including convex, concave, and inner surface of the 3D structure. The direct‐printing ability of nanofiber on 3D surfaces using SA‐N will be a promising strategy to utilize various functional polymers in flexible electronics, printed electronics, optics, and biomedical engineering.
Electrospinning is a promising process to fabricate functional parts from macrofibers and nanofibers of bio-compatible materials including collagen, polylactide (PLA), and polyacrylonitrile (PAN). However, the functionality of the produced parts highly rely on quality, repeatability, and uniformity of the electrospun fibers. Due to the variations in material composition, process settings, and ambient conditions, the process suffers from large variations. In particular, the fiber formation in the stable regime (i.e., Taylor cone and jet) and its propagation to the substrate plays the most significant role in the process stability. This work aims to designing a fast process monitoring tool from scratch for monitoring the dynamic electrospinning process based on the Taylor cone and jet videos. Nevertheless, this is challenging since the videos are of high frequency and high dimension, and the monitoring statistics may not have a parametric distribution. To achieve this goal, a framework integrating image analysis, sketch-based tensor decomposition, and non-parametric monitoring, is proposed. In particular, we use Tucker tensor-sketch (Tucker-TS) based tensor decomposition to extract the sparse structure representations of the videos. Additionally, the extracted monitoring variables are non-normally distributed, hence non-parametric bootstrap Hotelling T2 control chart is deployed to handle this issue during the monitoring. The framework is demonstrated by electrospinning a PAN-based polymeric solution. Finally, it is demonstrated that the proposed framework, which uses Tucker-TS, largely outperformed the computational speed of the alternating least squares (ALS) approach for the Tucker tensor decomposition, i.e., Tucker-ALS, in various anomaly detection tasks while keeping the comparable anomaly detection accuracy.
more » « less- Award ID(s):
- 1846863
- NSF-PAR ID:
- 10212290
- Date Published:
- Journal Name:
- ASME 2020 15th International Manufacturing Science and Engineering Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Here, a novel melt electrospinning method to produce few‐micron and nanometer thick fibers is presented, in which a polymer‐coated wire with a sharp tip is used as the polymer source. The polymer coating is melted via Joule heating of the source wire and extracted toward the target via electrostatic forces. The high viscosity and low charge density of polymer melts lower their stretchability in melt. The method relies on confining the Taylor cone and reducing initial jet diameter via concentrated electrostatic fields as a means to reduce the diameter of fibers. As a result, the initial jet diameter and the final fiber diameter are reduced by an order of magnitude of three to ten times, respectively, using wire melt electrospinning compared to syringe‐ and edge‐based electrospinning. The fiber diameter melt electrospun via this novel method is 1.0 ± 0.9 µm, considerably thinner than conventional melt electrospinning techniques. The generation of thin fibers are explained in terms of the electrostatic field around the wire tip, as obtained from finite element analysis (FEA), which controls the size and shape of the melt electrospun jet.
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Abstract Wire arc additive manufacturing (WAAM) has gained attention as a feasible process in large-scale metal additive manufacturing due to its high deposition rate, cost efficiency, and material diversity. However, WAAM induces a degree of uncertainty in the process stability and the part quality owing to its non-equilibrium thermal cycles and layer-by-layer stacking mechanism. Anomaly detection is therefore necessary for the quality monitoring of the parts. Most relevant studies have applied machine learning to derive data-driven models that detect defects through feature and pattern learning. However, acquiring sufficient data is time- and/or resource-intensive, which introduces a challenge to applying machine learning-based anomaly detection. This study proposes a multisource transfer learning method that generates anomaly detection models for balling defect detection, thus ensuring quality monitoring in WAAM. The proposed method uses convolutional neural network models to extract sufficient image features from multisource materials, then transfers and fine-tunes the models for anomaly detection in the target material. Stepwise learning is applied to extract image features sequentially from individual source materials, and composite learning is employed to assign the optimal frozen ratio for converging transferred and present features. Experiments were performed using a gas tungsten arc welding-based WAAM process to validate the classification accuracy of the models using low-carbon steel, stainless steel, and Inconel.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/MSEC2020-8367
