skip to main content


The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 5:00 PM ET until 11:00 PM ET on Friday, June 21 due to maintenance. We apologize for the inconvenience.

Title: Sketch-Based Tensor Decomposition for Non-Parametric Monitoring of Electrospinning Processes

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):
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
ASME 2020 15th International Manufacturing Science and Engineering Conference
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    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.

    more » « less
  2. 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.

    more » « less
  3. The Tucker tensor decomposition is a natural extension of the singular value decomposition (SVD) to multiway data. We propose to accelerate Tucker tensor decomposition algorithms by using randomization and parallelization. We present two algorithms that scale to large data and many processors, significantly reduce both computation and communication cost compared to previous deterministic and randomized approaches, and obtain nearly the same approximation errors. The key idea in our algorithms is to perform randomized sketches with Kronecker-structured random matrices, which reduces computation compared to unstructured matrices and can be implemented using a fundamental tensor computational kernel. We provide probabilistic error analysis of our algorithms and implement a new parallel algorithm for the structured randomized sketch. Our experimental results demonstrate that our combination of randomization and parallelization achieves accurate Tucker decompositions much faster than alternative approaches. We observe up to a 16X speedup over the fastest deterministic parallel implementation on 3D simulation data. 
    more » « less
  4. Most commonly used classification algorithms process data in the form of vectors. At the same time, modern datasets often comprise multimodal measurements that are naturally modeled as multi-way arrays, also known as tensors. Processing multi-way data in their tensor form can enable enhanced inference and classification accuracy. Tucker decomposition is a standard method for tensor data processing, which however has demonstrated severe sensitivity to corrupted measurements due to its L2-norm formulation. In this work, we present a selection of classification methods that employ an L1-norm-based, corruption-resistant reformulation of Tucker (L1-Tucker). Our experimental studies on multiple real datasets corroborate the corruption-resistance and classification accuracy afforded by L1-Tucker. 
    more » « less
  5. Event detection is gaining increasing attention in smart cities research. Large-scale mobility data serves as an important tool to uncover the dynamics of urban transportation systems, and more often than not the dataset is incomplete. In this article, we develop a method to detect extreme events in large traffic datasets, and to impute missing data during regular conditions. Specifically, we propose a robust tensor recovery problem to recover low-rank tensors under fiber-sparse corruptions with partial observations, and use it to identify events, and impute missing data under typical conditions. Our approach is scalable to large urban areas, taking full advantage of the spatio-temporal correlations in traffic patterns. We develop an efficient algorithm to solve the tensor recovery problem based on the alternating direction method of multipliers (ADMM) framework. Compared with existing l 1 norm regularized tensor decomposition methods, our algorithm can exactly recover the values of uncorrupted fibers of a low-rank tensor and find the positions of corrupted fibers under mild conditions. Numerical experiments illustrate that our algorithm can achieve exact recovery and outlier detection even with missing data rates as high as 40% under 5% gross corruption, depending on the tensor size and the Tucker rank of the low rank tensor. Finally, we apply our method on a real traffic dataset corresponding to downtown Nashville, TN and successfully detect the events like severe car crashes, construction lane closures, and other large events that cause significant traffic disruptions. 
    more » « less