Date on Master's Thesis/Doctoral Dissertation
12-2025
Document Type
Master's Thesis
Degree Name
M.S.
Department
Industrial Engineering
Degree Program
Industrial Engineering, MS
Committee Chair
Segura, Luis
Committee Member
Aqlan, Faisal
Committee Member
Yang, Li
Author's Keywords
electrospinning; inkjet printing; image processing; process monitoring; anomaly detection; dimensionality reduction
Abstract
Fluid-based manufacturing processes, such as inkjet printing and electrospinning, fabricate micro- and nano-scale structures with high precision, but are prone to complex fluid dynamics exhibiting axisymmetric and non-axisymmetric instabilities. Conventional monitoring often relies on single-camera inputs and symmetry assumptions, limiting the detection of three-dimensional anomalies like jet deflection. This study presents a novel multi-view streaming video-based anomaly detection framework to address this gap. The framework employs a modified Tensor Sequential Sampling (TSS) algorithm with edge-based sampling to capture geometric spatiotemporal features of each camera view using the videos obtained from an orthogonally positioned dual-camera setup. These features are fused with jet angle measurements via a Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. Experimental results demonstrate that this joint multi-view approach can detect several types of process anomalies including non-axis symmetric deviations, significantly outperforming single-view baselines in identifying non-axisymmetric deviations.
Recommended Citation
Khadka, Bidusi, "Detecting non-axisymmetric instabilities in fluid-based manufacturing via multi-view tensor analysis." (2025). Electronic Theses and Dissertations. Paper 4696.
Retrieved from https://ir.library.louisville.edu/etd/4696
